From 70a7d217d10b031a99f8dab0382d9c7f7e50a7e9 Mon Sep 17 00:00:00 2001 From: chetan-hirapara Date: Thu, 1 Aug 2024 14:36:59 +0000 Subject: [PATCH 1/7] added consumer complaints with google --- .../Complaint_Analysis_Customer360.ipynb | 0 .../Complaint_Analysis_Dashboard.ipynb | 2 +- .../Complaint_Summarization.ipynb | 0 .../Complaints_Classification.ipynb | 0 .../Complaints_Clustering.ipynb | 0 .../Sentiment_Analysis.ipynb} | 0 .../Speech_Recognition.ipynb} | 0 .../Topic_Modelling.ipynb | 0 .../audio_files/Conversation_1.mp3 | Bin .../audio_files/Conversation_2.mp3 | Bin .../audio_files/Conversation_3.mp3 | Bin .../audio_files/Conversation_4.mp3 | Bin .../audio_files/Conversation_5.mp3 | Bin .../audio_files/audio1.mp3 | Bin .../images/architecture_bedrock_audio.png | Bin .../images/classification.png | Bin .../images/clustering.png | Bin .../images/customer360.png | Bin .../images/sentiment_analysis.png | Bin .../images/summarization.png | Bin .../images/topics.png | Bin .../images/voice-note.jpg | Bin .../requirements.txt | 0 .../Complaint_Analysis_Customer360.ipynb | 508 +++++++++++ .../Complaint_Analysis_Dashboard.ipynb | 185 ++++ .../Complaint_Summarization.ipynb | 467 ++++++++++ .../Complaints_Classification.ipynb | 538 +++++++++++ .../Complaints_Clustering.ipynb | 845 ++++++++++++++++++ .../Sentiment_Analysis.ipynb | 733 +++++++++++++++ .../Speech_Recognition.ipynb | 30 +- .../Topic_Modelling.ipynb | 513 +++++++++++ .../audio_files/audio1.mp3 | Bin 0 -> 129888 bytes .../images/classification.png | Bin 0 -> 120725 bytes .../images/clustering.png | Bin 0 -> 213628 bytes .../images/cusotmer360_gemini.jpg | Bin .../images/sentiment_analysis.png | Bin 0 -> 91531 bytes .../images/summarization.png | Bin 0 -> 60139 bytes .../images/topics.png | Bin 0 -> 19606 bytes .../images/voice-note.jpg | Bin 0 -> 89876 bytes .../requirements.txt | 2 + 40 files changed, 3812 insertions(+), 11 deletions(-) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/Complaint_Analysis_Customer360.ipynb (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/Complaint_Analysis_Dashboard.ipynb (99%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/Complaint_Summarization.ipynb (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/Complaints_Classification.ipynb (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/Complaints_Clustering.ipynb (100%) rename UseCases/{Complaints_Analysis_GenAI/Sentiment_Analysis_Bedrock.ipynb => Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb} (100%) rename UseCases/{Complaints_Analysis_GenAI/Speech_Recognition_AWS_Bedrock.ipynb => Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb} (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/Topic_Modelling.ipynb (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/audio_files/Conversation_1.mp3 (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/audio_files/Conversation_2.mp3 (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/audio_files/Conversation_3.mp3 (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/audio_files/Conversation_4.mp3 (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/audio_files/Conversation_5.mp3 (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/audio_files/audio1.mp3 (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/images/architecture_bedrock_audio.png (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/images/classification.png (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/images/clustering.png (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/images/customer360.png (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/images/sentiment_analysis.png (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/images/summarization.png (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/images/topics.png (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/images/voice-note.jpg (100%) rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Bedrock}/requirements.txt (100%) create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Customer360.ipynb create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Dashboard.ipynb create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Summarization.ipynb create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Gemini}/Speech_Recognition.ipynb (94%) create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/audio_files/audio1.mp3 create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/images/classification.png create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/images/clustering.png rename UseCases/{Complaints_Analysis_GenAI => Complaints_Analysis_GenAI_Gemini}/images/cusotmer360_gemini.jpg (100%) create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/images/sentiment_analysis.png create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/images/summarization.png create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/images/topics.png create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/images/voice-note.jpg create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/requirements.txt diff --git a/UseCases/Complaints_Analysis_GenAI/Complaint_Analysis_Customer360.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/Complaint_Analysis_Customer360.ipynb rename to UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb diff --git a/UseCases/Complaints_Analysis_GenAI/Complaint_Analysis_Dashboard.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Dashboard.ipynb similarity index 99% rename from UseCases/Complaints_Analysis_GenAI/Complaint_Analysis_Dashboard.ipynb rename to UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Dashboard.ipynb index 5e858d05..14975698 100644 --- a/UseCases/Complaints_Analysis_GenAI/Complaint_Analysis_Dashboard.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Dashboard.ipynb @@ -7,7 +7,7 @@ "source": [ "
\n", "

\n", - " Customer Complaint Analyzer with Gen-AI\n", + " Customer Complaint Analyzer with Gen-AI (Amazon Bedrock)\n", "
\n", " \"Teradata\"\n", "

\n", diff --git a/UseCases/Complaints_Analysis_GenAI/Complaint_Summarization.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/Complaint_Summarization.ipynb rename to UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb diff --git a/UseCases/Complaints_Analysis_GenAI/Complaints_Classification.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/Complaints_Classification.ipynb rename to UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb diff --git a/UseCases/Complaints_Analysis_GenAI/Complaints_Clustering.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/Complaints_Clustering.ipynb rename to UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb diff --git a/UseCases/Complaints_Analysis_GenAI/Sentiment_Analysis_Bedrock.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/Sentiment_Analysis_Bedrock.ipynb rename to UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb diff --git a/UseCases/Complaints_Analysis_GenAI/Speech_Recognition_AWS_Bedrock.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/Speech_Recognition_AWS_Bedrock.ipynb rename to UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb diff --git a/UseCases/Complaints_Analysis_GenAI/Topic_Modelling.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/Topic_Modelling.ipynb rename to UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb diff --git a/UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_1.mp3 b/UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_1.mp3 similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_1.mp3 rename to UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_1.mp3 diff --git a/UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_2.mp3 b/UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_2.mp3 similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_2.mp3 rename to UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_2.mp3 diff --git a/UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_3.mp3 b/UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_3.mp3 similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_3.mp3 rename to UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_3.mp3 diff --git a/UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_4.mp3 b/UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_4.mp3 similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_4.mp3 rename to UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_4.mp3 diff --git a/UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_5.mp3 b/UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_5.mp3 similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/audio_files/Conversation_5.mp3 rename to UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_5.mp3 diff --git a/UseCases/Complaints_Analysis_GenAI/audio_files/audio1.mp3 b/UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/audio1.mp3 similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/audio_files/audio1.mp3 rename to UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/audio1.mp3 diff --git a/UseCases/Complaints_Analysis_GenAI/images/architecture_bedrock_audio.png b/UseCases/Complaints_Analysis_GenAI_Bedrock/images/architecture_bedrock_audio.png similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/images/architecture_bedrock_audio.png rename to UseCases/Complaints_Analysis_GenAI_Bedrock/images/architecture_bedrock_audio.png diff --git a/UseCases/Complaints_Analysis_GenAI/images/classification.png b/UseCases/Complaints_Analysis_GenAI_Bedrock/images/classification.png similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/images/classification.png rename to UseCases/Complaints_Analysis_GenAI_Bedrock/images/classification.png diff --git a/UseCases/Complaints_Analysis_GenAI/images/clustering.png b/UseCases/Complaints_Analysis_GenAI_Bedrock/images/clustering.png similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/images/clustering.png rename to UseCases/Complaints_Analysis_GenAI_Bedrock/images/clustering.png diff --git a/UseCases/Complaints_Analysis_GenAI/images/customer360.png b/UseCases/Complaints_Analysis_GenAI_Bedrock/images/customer360.png similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/images/customer360.png rename to UseCases/Complaints_Analysis_GenAI_Bedrock/images/customer360.png diff --git a/UseCases/Complaints_Analysis_GenAI/images/sentiment_analysis.png b/UseCases/Complaints_Analysis_GenAI_Bedrock/images/sentiment_analysis.png similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/images/sentiment_analysis.png rename to UseCases/Complaints_Analysis_GenAI_Bedrock/images/sentiment_analysis.png diff --git a/UseCases/Complaints_Analysis_GenAI/images/summarization.png b/UseCases/Complaints_Analysis_GenAI_Bedrock/images/summarization.png similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/images/summarization.png rename to UseCases/Complaints_Analysis_GenAI_Bedrock/images/summarization.png diff --git a/UseCases/Complaints_Analysis_GenAI/images/topics.png b/UseCases/Complaints_Analysis_GenAI_Bedrock/images/topics.png similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/images/topics.png rename to UseCases/Complaints_Analysis_GenAI_Bedrock/images/topics.png diff --git a/UseCases/Complaints_Analysis_GenAI/images/voice-note.jpg b/UseCases/Complaints_Analysis_GenAI_Bedrock/images/voice-note.jpg similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/images/voice-note.jpg rename to UseCases/Complaints_Analysis_GenAI_Bedrock/images/voice-note.jpg diff --git a/UseCases/Complaints_Analysis_GenAI/requirements.txt b/UseCases/Complaints_Analysis_GenAI_Bedrock/requirements.txt similarity index 100% rename from UseCases/Complaints_Analysis_GenAI/requirements.txt rename to UseCases/Complaints_Analysis_GenAI_Bedrock/requirements.txt diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Customer360.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Customer360.ipynb new file mode 100644 index 00000000..6c7452f3 --- /dev/null +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Customer360.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1f44f3bc-51cc-47e3-a033-f2883ed97408", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Complaints Analysis Integration with Customer360\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "19c23c87-b5a5-4905-913e-d4f7980497fa", + "metadata": {}, + "source": [ + "

Introduction:

\n", + "

Complaints Analysis Integration with Customer360 is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view using Teradata Vantage and Google Gemini. This integration aims to provide a seamless and personalized customer experience by leveraging data from various sources, including CRM systems, marketing platforms, and social media.

The key components of this integration include:

\n", + "\n", + "
  1. Customer 360 Data Manager: Responsible for managing and maintaining a comprehensive view of customer data, including collection, integration, and analysis from multiple sources.
  2. Complaints Management Dashboard: Analyzes customer complaints, providing insights into complaint volume, trends, and resolution progress.
  3. Customer Insights: Tools for gaining insights into customer behavior and preferences, enabling targeted marketing campaigns and informed business decisions.

The benefits of this integration include:

  1. Improved Customer Experience: By integrating complaints analysis with Customer 360, businesses can address customer complaints more effectively, leading to increased customer satisfaction and loyalty.
  2. Data-Driven Decision Making: The integration provides a centralized platform for analyzing customer data, enabling businesses to make informed decisions about product development, marketing strategies, and customer engagement.
  3. Enhanced Customer Insights: The comprehensive view of customer data allows businesses to better understand customer needs and preferences, leading to more targeted and effective marketing efforts.
\n", + "\n", + "\n", + "

By integrating complaints analysis with Customer 360, businesses can create a more comprehensive and personalized customer experience, driving business growth and customer satisfaction.

\n", + "\n", + "

Steps in the analysis:

\n", + "
    \n", + "
  1. Configuring the environment
    2. \n", + "
  2. Connect to Vantage
    3. \n", + "
  3. Setup API key for Google Gemini
    4. \n", + "
  4. Using LLM for Sentiment Analysis, Topic Modelling and Complaint Summerization
    5. \n", + "
  5. Integrated data with customer 360
    6. \n", + "
  6. Cleanup
    7. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "439c593b-d12a-4edf-8b5f-6070f1122914", + "metadata": {}, + "source": [ + "
\n", + "1. Configuring the environment\n", + "\n", + "

1.1 Install the required libraries

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebc0a97b-78f8-4274-9844-d3b1d31848e6", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "7a8a5583-362f-4d11-8b3a-17b20637f517", + "metadata": {}, + "source": [ + "
\n", + "

Note: Please restart the kernel after executing these two lines. The simplest way to restart the Kernel is by typing zero zero: 0 0

" + ] + }, + { + "cell_type": "markdown", + "id": "7e7e5046-3c5f-4f6d-aeaf-60028655ff13", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

1.2 Import the required libraries

\n", + "\n", + "

Here, we import the required libraries, set environment variables and environment paths (if required).

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83dc0922-f932-4378-a505-3bb2d1f1243b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import timeit\n", + "from tqdm import tqdm\n", + "from teradataml import *\n", + "import plotly.express as px\n", + "from langchain.llms.bedrock import Bedrock\n", + "\n", + "# GenAI libs\n", + "import google.generativeai as genai\n", + "from google.generativeai import protos\n", + "\n", + "display.max_rows = 5\n", + "pd.set_option('display.max_colwidth', 50)\n", + "pd.set_option('display.max_rows', 5)" + ] + }, + { + "cell_type": "markdown", + "id": "768bf2ed-ae11-4969-b20a-88496e4a2b67", + "metadata": {}, + "source": [ + "
\n", + "2. Connect to Vantage\n", + "

2.1 Connect to Vantage

\n", + "

We will be prompted to provide the password. We will enter the password, press the Enter key, and then use the down arrow to go to the next cell.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "454a2e81-c377-4058-9e68-78abd801ad9c", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../startup.ipynb\n", + "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", + "print(eng)\n", + "execute_sql('''SET query_band='DEMO=Complaint_Summarization.ipynb;' UPDATE FOR SESSION;''')" + ] + }, + { + "cell_type": "markdown", + "id": "aed444a1-f0de-4bff-b0b9-d2c4e92573f5", + "metadata": {}, + "source": [ + "

Begin running steps with Shift + Enter keys.

" + ] + }, + { + "cell_type": "markdown", + "id": "782ff009-1182-45c0-aa75-2957518ae7c2", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

2.2 Getting Data for This Demo

\n", + "

We have provided data for this demo on cloud storage. We have the option of either running the demo using foreign tables to access the data without using any storage on our environment or downloading the data to local storage, which may yield somewhat faster execution. However, we need to consider available storage. There are two statements in the following cell, and one is commented out. We may switch which mode we choose by changing the comment string.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65bf814d-4783-4213-936f-886667adf4a7", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_cloud');\" # Takes 1 minute\n", + "# %run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_local');\" # Takes 2 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "53e3b7cf-b45e-483f-8db5-b78527ed873c", + "metadata": {}, + "source": [ + "
\n", + "3. Setup API key for Google Gemini\n", + "

Please enter the Google API Key, if you don't have one, please get it from here

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49e92709-9a74-4372-a57a-7b078fe2ae2c", + "metadata": {}, + "outputs": [], + "source": [ + "GOOGLE_API_KEY = getpass.getpass(prompt = 'Please enter GOOGLE_API_KEY: ')\n", + "genai.configure(api_key = GOOGLE_API_KEY)" + ] + }, + { + "cell_type": "markdown", + "id": "75d9ca05-b97e-4336-a439-65f52180e8e8", + "metadata": {}, + "source": [ + "
\n", + "3.1. Define the Gemini model and Prompt\n", + "

The following section defines the type of Gemini model used. Here we use gemini-1.5-pro-latest

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecada13c-1aac-45c1-8bf0-6db4be2eec82", + "metadata": {}, + "outputs": [], + "source": [ + "from google.generativeai.types import HarmCategory, HarmBlockThreshold\n", + "\n", + "model = genai.GenerativeModel(\n", + " model_name = \"models/gemini-1.5-pro-latest\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "596cfbe5-e6d7-4b14-9d46-08929397e1a3", + "metadata": {}, + "source": [ + "
\n", + "4. Using LLM for Sentiment Analysis, Topic Modelling and Complaint Summarization\n", + "\n", + "

Sentiment Analysis, Topic Modelling and Complaint Summarization using Large Language Models (LLMs) revolutionizes the way we understand and categorize vast collections of text data. LLMs excel in understanding the semantics and context of words, enabling sophisticated topic modeling techniques.

\n", + "\n", + "

Sentiment Analysis Using Large Language Models (LLMs) is a cutting-edge approach to understanding customer opinions and emotions expressed through text-based data. This advanced technique leverages the capabilities of LLMs to accurately identify and categorize sentiment as positive, negative, or neutral, providing businesses with valuable insights into customer perceptions and preferences.

\n", + "\n", + "

LLMs can generate coherent topics without needing predefined categories, making them ideal for exploratory analysis of diverse datasets. Moreover, their ability to capture subtle nuances in language allows for more precise topic identification, even in noisy or ambiguous texts.

\n", + "\n", + "

Reasoning with a Chain of Thought: Imagine you're trying to solve a problem. With a large language model, you start with an initial idea or question. Then, you use the model's capabilities to explore related concepts, gradually connecting them together. Each step builds upon the previous one, leading you closer to understanding or solving the problem. It's like putting together puzzle pieces, one by one, until you see the whole picture.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3506e344-bf92-44f4-9cee-7d85eac3290b", + "metadata": {}, + "outputs": [], + "source": [ + "customer_data = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Customer_360_Details'))\n", + "customer_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb0b5121-6071-42cd-a928-af0116094289", + "metadata": {}, + "outputs": [], + "source": [ + "complaints_data = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Customer_360_Complaints'))\n", + "complaints_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0508336-8aa2-48e9-99a6-e9c4919abd15", + "metadata": {}, + "outputs": [], + "source": [ + "pd_df = complaints_data.to_pandas()\n", + "pd_df['Sentiment'] = \"\"\n", + "pd_df['Topic'] = \"\"\n", + "pd_df['Summary'] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "507848d6-6871-4874-a8db-e0c0de0ba106", + "metadata": {}, + "outputs": [], + "source": [ + "# Sentiment\n", + "for i in tqdm(range(len(pd_df))):\n", + " prompt = f'''\n", + " User prompt: \n", + " The following is text from a review:\n", + "\n", + " “{pd_df['consumer_complaint_narrative'][i]}”\n", + "\n", + " Categorize the review as one of the following:\n", + "\n", + " Positive\n", + " Negative\n", + " Neutral\n", + " \n", + " - Important: Do not add any formating into the output.\n", + " - Return just one of the above options, Do not return explaination\n", + " '''\n", + " output = model.generate_content([prompt])\n", + " sentiment = output.candidates[0].content.parts[0].text\n", + " \n", + " pd_df['Sentiment'][i] = sentiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff4efda4-0fa1-4fde-8b24-56ab91912849", + "metadata": {}, + "outputs": [], + "source": [ + "# Topic\n", + "for i in tqdm(range(len(pd_df))):\n", + " prompt = f'''\n", + " User prompt:\n", + " The following is text from a complaint:\n", + "\n", + " “{pd_df['consumer_complaint_narrative'][i]}”\n", + "\n", + " Identify the topic of the complaint and categorize into one of the following topics. Only output one of the following options:\n", + "\n", + " - Mortgage Application\n", + " - Payment Trouble\n", + " - Mortgage Closing\n", + " - Report Inaccuracy\n", + " - Payment Struggle\n", + " \n", + " - Important: Do not add any formating into the output. For example **Mortgage Application** or **Report Inaccuracy** refrain from such formating in the response.\n", + " - Return just one of the above options\n", + "'''\n", + " \n", + " output = model.generate_content([prompt])\n", + " topic = output.candidates[0].content.parts[0].text\n", + " \n", + " pd_df['Topic'][i] = topic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0174db51-edcc-42d2-b251-c3171648a0e2", + "metadata": {}, + "outputs": [], + "source": [ + "# Summary\n", + "for i in tqdm(range(len(pd_df))):\n", + " prompt = f'''\n", + " The following is text from a Bank Review:\n", + " “{pd_df['consumer_complaint_narrative'][i]}”\n", + " Summarize the Bank Review in one sentence\n", + " '''\n", + "\n", + " output = model.generate_content([prompt])\n", + " summary = output.candidates[0].content.parts[0].text\n", + " \n", + " pd_df['Summary'][i] = summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5977498-de10-4392-888d-5541dae6801f", + "metadata": {}, + "outputs": [], + "source": [ + "pd_df['Sentiment'] = pd_df['Sentiment'].apply(lambda x: x.strip())\n", + "pd_df['Topic'] = pd_df['Topic'].apply(lambda x: x.strip())\n", + "pd_df['Summary'] = pd_df['Summary'].apply(lambda x: x.strip())\n", + "\n", + "combined_df = customer_data.to_pandas().join(pd_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb6e794b-fa9b-4a52-89c6-5cf02ab07bdc", + "metadata": {}, + "outputs": [], + "source": [ + "combined_df[\"Bank Strategy\"] = ['Wealth Manager to contact customer immediately',\n", + " 'Send Policy Letter from Mortgage Servicing',\n", + " 'Send Policy Letter from Executive Office',\n", + " 'Mortgage Banker to follow-up with Title Company for documentation and contact customer',\n", + " 'Branch Manager to contact customer immediately'\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "576a3dd9-b74c-4a11-851c-6565d1a347e0", + "metadata": {}, + "source": [ + "
\n", + "5. Integrated data with customer 360\n", + "

The following is an example of the output from LLM integrated with existing customer360 data. Please scroll to the right to see all the columns.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c27e62ea-4998-46fe-8708-c9881a691075", + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_colwidth', None)\n", + "combined_df[[\"complaint_id\",\"Customer_ID\",\"Sentiment\",\"Topic\",\"Summary\"]]" + ] + }, + { + "cell_type": "markdown", + "id": "06cbb0f4-d026-4a0b-8ab8-6982b7f7777a", + "metadata": {}, + "source": [ + "

Save the results back to Vantage.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48861c1a-5e27-4eb7-938f-824eafdd5569", + "metadata": {}, + "outputs": [], + "source": [ + "combined_df.drop(columns=['state'], inplace=True, index=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a940a4d-b556-45e1-b36a-4efc11bce30c", + "metadata": {}, + "outputs": [], + "source": [ + "copy_to_sql(df = combined_df, table_name = 'Customer_360_Complaints_analysis', if_exists = 'replace', primary_index=\"complaint_id\")" + ] + }, + { + "cell_type": "markdown", + "id": "561ff317-6468-4941-bf9b-840849bfb09d", + "metadata": {}, + "source": [ + "
\n", + "6. Cleanup" + ] + }, + { + "cell_type": "markdown", + "id": "476fb82b-d7a0-4e29-a6d4-8372a247cea8", + "metadata": {}, + "source": [ + "

Databases and Tables

\n", + "

The following code will clean up tables and databases created above.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9472c2ca-65d0-4116-9c45-6411fcea52ef", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../run_procedure.py \"call remove_data('DEMO_ComplaintAnalysis');\" # Takes 10 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "358f56a0-ad97-4317-ab4c-88be00b8d179", + "metadata": {}, + "outputs": [], + "source": [ + "remove_context()" + ] + }, + { + "cell_type": "markdown", + "id": "6cf670cd-4594-458d-af98-8efff5a72f73", + "metadata": {}, + "source": [ + "
\n", + "Dataset:\n", + "
\n", + "
\n", + "

The dataset is sourced from Consumer Financial Protection Bureau

" + ] + }, + { + "cell_type": "markdown", + "id": "eeebf3ab-357c-488e-ba9d-78bf82f4d0dd", + "metadata": {}, + "source": [ + "
\n", + "
ClearScape Analytics™
\n", + "
\n", + "
\n", + " Copyright © Teradata Corporation - 2024. All Rights Reserved\n", + "
\n", + "
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Dashboard.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Dashboard.ipynb new file mode 100644 index 00000000..8e499e77 --- /dev/null +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Dashboard.ipynb @@ -0,0 +1,185 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "551d92a1-a80f-4162-89a2-522369bf2e33", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Customer Complaint Analyzer with Gen-AI (Google Gemini)\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "92a7b9ae-674d-4414-9597-b10e860063c7", + "metadata": {}, + "source": [ + "\n", + "
\n", + "

Customer 360

\n", + "

This demo shows Complaints Analysis Integration with Customer360 which is a comprehensive approach to managing customer complaints and feedback within the framework of a Customer 360-degree view using Teradata Vantage and Google Gemini. This integration aims to provide a seamless and personalized customer experience by leveraging data from various sources, including CRM systems, marketing platforms, and social media.

\n", + " \"Customer\n", + "

Integrating Complaints Analysis with Customer360 data

\n", + "
\n", + " \n", + " Open Demo\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b836f94d-9d43-4ce8-a681-88b8298d3a02", + "metadata": {}, + "source": [ + "\n", + "
\n", + "

Speech Analysis with Gemini

\n", + "

This demo shows Speech Recognition using Teradata Vantage on Google and Google Gemini LLM model is a cutting-edge technology that leverages the power of cloud computing and advanced algorithms to accurately recognize and transcribe spoken words. This innovative solution integrates the capabilities of Teradata Vantage, Google Cloud, and Gemini to provide a robust and efficient speech recognition system.

\n", + " \"Speech\n", + "

Speech Analysis with Google Gemini

\n", + "
\n", + " \n", + " Open Demo\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "2dcf689a-baa1-42ff-b223-715b1bf68d1d", + "metadata": {}, + "source": [ + "\n", + "
\n", + "

Sentiment Analysis

\n", + "

This demo shows sentiment analysis using Teradata Vantage and the advanced Google Gemini model involves leveraging cutting-edge technologies to extract insights from unstructured data. This process empowers businesses to swiftly identify and address customer concerns, enhancing overall customer satisfaction and loyalty.

\n", + " \"Sentiment\n", + "

Sentiment Analysis Using Vantage and Google Gemini

\n", + "
\n", + " \n", + " Open Demo\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "90e0f124-1476-4e95-af54-0bc62c467b4e", + "metadata": {}, + "source": [ + "\n", + "
\n", + "

Complaints Clustering

\n", + "

This demo uses advanced clustering techniques powered by Teradata Vantage and Google Gemini's Generative AI Embeddings model to group similar customer complaints together. By identifying common themes and patterns, this functionality provides valuable insights into the key issues and pain points experienced by customers.

\n", + " \"Complaints\n", + "

Complaints Clustering using Vantage and Google Gemini

\n", + "
\n", + " \n", + " Open Demo\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "a8554f25-c10b-461f-93fb-1920c7cc90d0", + "metadata": {}, + "source": [ + "\n", + "
\n", + "

Complaints Classification

\n", + "

This demo shows a revolutionary way for customer complaint resolution with our pioneering solution, which seamlessly integrates the capabilities of Teradata Vantage and Google Gemini model as LLM. This powerful synergy enables businesses to classify customer complaints with unmatched precision and speed, allowing them to swiftly identify and address concerns, thereby elevating overall customer satisfaction and loyalty.

\n", + " \"Complaints\n", + "

Complaints Classification using Vantage and Google Gemini

\n", + "
\n", + " \n", + " Open Demo\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "45f63b0b-8665-433d-8776-0f285844cd29", + "metadata": {}, + "source": [ + "\n", + "
\n", + "

Complaints Summarization

\n", + "

This demo shows Complaints Summarization using Teradata Vantage and Google Gemini model. This cutting-edge solution empowers organizations to efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.

\n", + " \"Complaints\n", + "

Complaints Summarization Using Vantage and Google Gemini

\n", + "
\n", + " \n", + " Open Demo\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "7c1cb232-1fa2-487b-860e-2999f30f12e0", + "metadata": {}, + "source": [ + "\n", + "
\n", + "

Topic Modelling

\n", + "

This demo shows world of topic modeling using Teradata Vantage and Google Gemini LLM model. This cutting-edge technology empowers businesses to uncover hidden insights from vast amounts of consumer complaints data, enabling them to identify trends, improve customer satisfaction, and enhance their overall brand reputation.

\n", + " \"Topic\n", + "

Topic Modelling using Vantage and Google Gemini

\n", + "
\n", + " \n", + " Open Demo\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "54a71f39-741a-4a22-9d02-83468fc96993", + "metadata": {}, + "source": [ + "
\n", + "
ClearScape Analytics™
\n", + "
\n", + "
Copyright © Teradata Corporation - 2024. All Rights Reserved.
\n", + "
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Summarization.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Summarization.ipynb new file mode 100644 index 00000000..1e36852d --- /dev/null +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Summarization.ipynb @@ -0,0 +1,467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1f44f3bc-51cc-47e3-a033-f2883ed97408", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Complaints Summarization Using Vantage and Google Gemini\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "62ff194b-33c1-4c2f-9c5f-042cb29e3dc6", + "metadata": {}, + "source": [ + "

Introduction:

\n", + "\n", + "

In this demo we'll deep dive on Complaints Summarization using Teradata Vantage and Google Gemini model. This cutting-edge solution empowers organizations to efficiently manage and analyze customer complaints, providing actionable insights to enhance customer satisfaction and improve business operations.

\n", + "\n", + "

Key Features:

\n", + "\n", + "
    \n", + "
  1. AI-Powered Summarization: Utilizing advanced natural language processing (NLP) and machine learning algorithms, the system automatically summarizes complaints, identifying key issues, sentiment, and root causes.
    2. \n", + "
  2. Real-Time Analytics: The platform provides real-time analytics and visualization tools, enabling users to track complaint trends, sentiment analysis, and issue resolution rates.
    3. \n", + "
  3. Customizable Dashboards: Users can create personalized dashboards to monitor specific complaint categories, product lines, or geographic regions, ensuring targeted insights and swift action.
    4. \n", + "
  4. Integration with Google Gemini: Seamless integration with Teradata Vantage and Google Gemini models enables users to leverage the power of cloud-based infrastructure and advanced analytics capabilities.
    5. \n", + "
\n", + "\n", + "

Benefits:

\n", + "
    \n", + "
  1. Enhanced Customer Experience: By quickly identifying and addressing customer concerns, organizations can improve customer satisfaction and loyalty.
    2. \n", + "
  2. Operational Efficiency: Automated complaint summarization and analytics reduce manual processing time, allowing teams to focus on issue resolution and strategic decision-making.
    3. \n", + "
  3. Data-Driven Decision-Making: The platform provides actionable insights, enabling organizations to make informed decisions and drive business growth.
    4. \n", + "
\n", + "\n", + "

Steps in the analysis:

\n", + "
    \n", + "
  1. Configuring the environment
    2. \n", + "
  2. Connect to Vantage
    3. \n", + "
  3. Setup API key for Google Gemini
    4. \n", + "
  4. Complaints Summarization
    5. \n", + "
  5. Cleanup
    6. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "439c593b-d12a-4edf-8b5f-6070f1122914", + "metadata": {}, + "source": [ + "
\n", + "1. Configuring the environment\n", + "\n", + "

1.1 Install the required libraries

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebc0a97b-78f8-4274-9844-d3b1d31848e6", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -r requirements.txt --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61ae8bca-87dd-4771-a7c4-4f7c2c0cc641", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!pip install pyopenssl cryptography --force-reinstall --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "54b6bcb4-ea98-42f9-9088-b15b4ddd03bc", + "metadata": {}, + "source": [ + "
\n", + "

Note: Please restart the kernel after executing these two lines. The simplest way to restart the Kernel is by typing zero zero: 0 0

" + ] + }, + { + "cell_type": "markdown", + "id": "7e7e5046-3c5f-4f6d-aeaf-60028655ff13", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

1.2 Import the required libraries

\n", + "\n", + "

Here, we import the required libraries, set environment variables and environment paths (if required).

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83dc0922-f932-4378-a505-3bb2d1f1243b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import timeit\n", + "import boto3\n", + "from tqdm import tqdm\n", + "from teradataml import *\n", + "import plotly.express as px\n", + "from langchain.llms.bedrock import Bedrock\n", + "\n", + "# GenAI libs\n", + "import google.generativeai as genai\n", + "from google.generativeai import protos\n", + "\n", + "display.max_rows = 5\n", + "pd.set_option('display.max_colwidth', 50)\n", + "pd.set_option('display.max_rows', 5)" + ] + }, + { + "cell_type": "markdown", + "id": "768bf2ed-ae11-4969-b20a-88496e4a2b67", + "metadata": {}, + "source": [ + "
\n", + "2. Connect to Vantage\n", + "

2.1 Connect to Vantage

\n", + "

We will be prompted to provide the password. We will enter the password, press the Enter key, and then use the down arrow to go to the next cell.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "454a2e81-c377-4058-9e68-78abd801ad9c", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../startup.ipynb\n", + "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", + "print(eng)\n", + "execute_sql('''SET query_band='DEMO=Complaint_Summarization.ipynb;' UPDATE FOR SESSION;''')" + ] + }, + { + "cell_type": "markdown", + "id": "aed444a1-f0de-4bff-b0b9-d2c4e92573f5", + "metadata": {}, + "source": [ + "

Begin running steps with Shift + Enter keys.

" + ] + }, + { + "cell_type": "markdown", + "id": "782ff009-1182-45c0-aa75-2957518ae7c2", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

2.2 Getting Data for This Demo

\n", + "

We have provided data for this demo on cloud storage. We have the option of either running the demo using foreign tables to access the data without using any storage on our environment or downloading the data to local storage, which may yield somewhat faster execution. However, we need to consider available storage. There are two statements in the following cell, and one is commented out. We may switch which mode we choose by changing the comment string.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65bf814d-4783-4213-936f-886667adf4a7", + "metadata": {}, + "outputs": [], + "source": [ + "# %run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_cloud');\" # Takes 1 minute\n", + "%run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_local');\" # Takes 2 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "e1b19ab4-254a-4322-8b40-008fb7162e8c", + "metadata": {}, + "source": [ + "
\n", + "3. Setup API key for Google Gemini\n", + "

Please enter the Google API Key, if you don't have one, please get it from here

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49e92709-9a74-4372-a57a-7b078fe2ae2c", + "metadata": {}, + "outputs": [], + "source": [ + "GOOGLE_API_KEY = getpass.getpass(prompt = 'Please enter GOOGLE_API_KEY: ')\n", + "genai.configure(api_key = GOOGLE_API_KEY)" + ] + }, + { + "cell_type": "markdown", + "id": "24ba7963-2d4c-4b31-8934-00dffbd6849d", + "metadata": {}, + "source": [ + "
\n", + "3.1. Define the Gemini model and Prompt\n", + "

The following section defines the type of Gemini model used. Here we use gemini-1.5-pro-latest

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecada13c-1aac-45c1-8bf0-6db4be2eec82", + "metadata": {}, + "outputs": [], + "source": [ + "from google.generativeai.types import HarmCategory, HarmBlockThreshold\n", + "\n", + "model = genai.GenerativeModel(\n", + " model_name = \"models/gemini-1.5-pro-latest\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "596cfbe5-e6d7-4b14-9d46-08929397e1a3", + "metadata": {}, + "source": [ + "
\n", + "4. Complaints summarization\n", + "

Complaints summarization with Language Model (LLM) models involves condensing lengthy complaints into concise, informative summaries. By leveraging advanced natural language processing techniques, LLMs efficiently extract key issues, sentiments, and resolutions, aiding in quicker understanding and response to customer grievances.

\n", + "\n", + "

Streamlining the complaint summarization process, Language Model (LLM) models efficiently distill verbose grievances into concise, yet informative summaries. These summaries meticulously capture crucial elements including primary issues, prevalent sentiments, and possible resolutions. Harnessing advanced natural language processing capabilities, LLMs accelerate both comprehension and response to customer concerns, thereby elevating operational efficiency and bolstering overall customer satisfaction.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30ac5e3a-9264-46f5-94eb-76e813503ac6", + "metadata": {}, + "outputs": [], + "source": [ + "df = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Consumer_Complaints')).to_pandas(num_rows = 20)\n", + "df['Summary'] = \"\"\n", + "df['Reasoning with Chain of Thought'] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0174db51-edcc-42d2-b251-c3171648a0e2", + "metadata": {}, + "outputs": [], + "source": [ + "for i in tqdm(range(len(df))):\n", + " prompt = f'''\n", + " The following is text from a Bank Review:\n", + " \n", + " “{df['consumer_complaint_narrative'][i]}”\n", + " \n", + " Give me reasoning as well as summary for this review.\n", + " \n", + " Instructions for Reasoning:\n", + " - Give me Reasoning in short\n", + " - Only one sentence reasoning\n", + " Instructions for Summary:\n", + " - A short one sentence Summary of everything the review states.\n", + " \n", + " My output comes in the format:\n", + " Summary: ,\n", + " Reasoning: ,\n", + " '''\n", + "\n", + " output = model.generate_content([prompt])\n", + " output = output.candidates[0].content.parts[0].text\n", + " \n", + " summary = re.search('Summary:(.*)', output).group(1)\n", + " if summary == \"\":\n", + " summary = re.search('Summary:\\n(.*)', output).group(1)\n", + " \n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " if reasoning == \"\":\n", + " reasoning = re.search('Reasoning:\\n(.*)', output).group(1)\n", + " \n", + " df['Summary'][i] = summary\n", + " df['Reasoning with Chain of Thought'][i] = reasoning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5977498-de10-4392-888d-5541dae6801f", + "metadata": {}, + "outputs": [], + "source": [ + "df['Summary'] = df['Summary'].apply(lambda x: x.strip())\n", + "df['Reasoning with Chain of Thought'] = df['Reasoning with Chain of Thought'].apply(lambda x: x.strip())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23626789-0317-4edb-856c-cb5ab05cb1fb", + "metadata": {}, + "outputs": [], + "source": [ + "df[['complaint_id', 'consumer_complaint_narrative', 'Summary', 'Reasoning with Chain of Thought']]" + ] + }, + { + "cell_type": "markdown", + "id": "0d8ace4c-b948-413a-bdf7-53fa20088326", + "metadata": {}, + "source": [ + "
\n", + "

4.1 Graph for Complaint and Summary Lengths

A graph illustrating the Narrative length vs summary length. On the x-axis, you'd have \"Narrative length\" ranging from short to long complaints or narratives. On the y-axis, you'd have \"Summary length\" ranging from brief to detailed summaries. As narrative length increases, summary length would generally decrease, indicating the summarization process effectively condenses longer narratives into shorter summaries. This relationship would likely follow a downward trend, showcasing the summarization efficiency of the LLM models.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f8191c6-4ab5-4b6c-9a51-f8cbca2c9aba", + "metadata": {}, + "outputs": [], + "source": [ + "# Truncate text for hover data\n", + "max_chars = 50 # Maximum characters to display\n", + "df['truncated_narrative'] = df['consumer_complaint_narrative'].apply(lambda x: x[:max_chars] + '...' if len(x) > max_chars else x)\n", + "df['truncated_summary'] = df['Summary'].apply(lambda x: x[:max_chars] + '...' if len(x) > max_chars else x)\n", + "\n", + "# Calculate the length of consumer_complaint_narrative and Summary\n", + "df['narrative_length'] = df['consumer_complaint_narrative'].apply(len)\n", + "df['summary_length'] = df['Summary'].apply(len)\n", + "\n", + "# Create a scatter plot\n", + "fig = px.scatter(df.sort_values(['narrative_length']), x='narrative_length', y='summary_length',\n", + " hover_data=['complaint_id', 'truncated_narrative', 'truncated_summary'],\n", + " labels={'narrative_length': 'Narrative Length', 'summary_length': 'Summary Length'},\n", + " title='Complaint and Summary Lengths')\n", + "\n", + "# Update the x-axis to show values as they are (not in scientific notation)\n", + "fig.update_xaxes(type='category')\n", + "\n", + "# Show the plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "06cbb0f4-d026-4a0b-8ab8-6982b7f7777a", + "metadata": {}, + "source": [ + "

Save the results back to Vantage.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b9a58e0-ec2e-4e76-8f51-f1757ca5a8b5", + "metadata": {}, + "outputs": [], + "source": [ + "copy_to_sql(df = df, table_name = 'Complaints_Summaries', if_exists = 'replace')" + ] + }, + { + "cell_type": "markdown", + "id": "561ff317-6468-4941-bf9b-840849bfb09d", + "metadata": {}, + "source": [ + "
\n", + "5. Cleanup" + ] + }, + { + "cell_type": "markdown", + "id": "476fb82b-d7a0-4e29-a6d4-8372a247cea8", + "metadata": {}, + "source": [ + "

Databases and Tables

\n", + "

The following code will clean up tables and databases created above.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9472c2ca-65d0-4116-9c45-6411fcea52ef", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../run_procedure.py \"call remove_data('DEMO_ComplaintAnalysis');\" # Takes 10 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "358f56a0-ad97-4317-ab4c-88be00b8d179", + "metadata": {}, + "outputs": [], + "source": [ + "remove_context()" + ] + }, + { + "cell_type": "markdown", + "id": "6cf670cd-4594-458d-af98-8efff5a72f73", + "metadata": {}, + "source": [ + "
\n", + "Dataset:\n", + "
\n", + "
\n", + "

The dataset is sourced from Consumer Financial Protection Bureau

" + ] + }, + { + "cell_type": "markdown", + "id": "eeebf3ab-357c-488e-ba9d-78bf82f4d0dd", + "metadata": {}, + "source": [ + "
\n", + "
ClearScape Analytics™
\n", + "
\n", + "
\n", + " Copyright © Teradata Corporation - 2024. All Rights Reserved\n", + "
\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff3c6ae7-6de8-4d81-9aba-e1cd28fc7dd7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb new file mode 100644 index 00000000..df966f50 --- /dev/null +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb @@ -0,0 +1,538 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "09c89842-4ea3-425d-ac2a-0ef9c00983b7", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Complaints Classification using Vantage and Google Gemini\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "bbae5cd8-f5bd-4b0c-9ae6-ff8ef4bdd737", + "metadata": {}, + "source": [ + "

Introduction:

\n", + "\n", + "

Revolutionize customer complaint resolution with our pioneering solution, which seamlessly integrates the capabilities of Teradata Vantage and Google Gemini model as LLM. This powerful synergy enables businesses to classify customer complaints with unmatched precision and speed, allowing them to swiftly identify and address concerns, thereby elevating overall customer satisfaction and loyalty.

\n", + "\n", + "

Key Features:

\n", + "
    \n", + "
  • Automated Classification: Our AI-driven model categorizes complaints into predefined categories, ensuring consistency and reducing manual effort.
    • \n", + "
  • Contextual Understanding: The system comprehends the nuances of customer feedback, capturing subtle differences in tone and language.
    • \n", + "
  • Real-time Insights: Generate instant reports and analytics to identify trends, patterns, and areas for improvement.
    • \n", + "
\n", + "\n", + "\n", + "

Benefits:

\n", + "
    \n", + "
  • Enhanced Customer Experience: Swiftly address customer concerns, fostering trust and loyalty.
    • \n", + "
  • Improved Operational Efficiency: Reduce manual processing time, allowing teams to focus on high-value tasks.
    • \n", + "
  • Data-Driven Decision Making: Make informed decisions with actionable insights from complaint data.
    • \n", + "
\n", + "\n", + "

Experience the transformative power of Generative AI in complaints classification.

\n", + "\n", + "

Steps in the analysis:

\n", + "
    \n", + "
  1. Configuring the environment
    2. \n", + "
  2. Connect to Vantage
    3. \n", + "
  3. Setup API key for Google Gemini
    4. \n", + "
  4. Classify Compalints
    5. \n", + "
  5. Cleanup
    6. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "fb9f91d3-a441-4265-9444-6fca6dff0ba5", + "metadata": {}, + "source": [ + "
\n", + "1. Configuring the environment\n", + "\n", + "

1.1 Install the required libraries

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74720dfc-1195-403e-a596-c9a55773fedc", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -r requirements.txt --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e477ccac-700d-4c04-b824-c05a808b0c13", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install --upgrade Pillow --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "3e8d47d7-1a44-4da1-afa5-46ce90312021", + "metadata": {}, + "source": [ + "
\n", + "

Note: Please restart the kernel after executing these two lines. The simplest way to restart the Kernel is by typing zero zero: 0 0

" + ] + }, + { + "cell_type": "markdown", + "id": "b8408409-7318-4481-9944-690f124cf969", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

1.2 Import the required libraries

\n", + "\n", + "

Here, we import the required libraries, set environment variables and environment paths (if required).

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83dc0922-f932-4378-a505-3bb2d1f1243b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import timeit\n", + "import boto3\n", + "from tqdm import tqdm\n", + "from teradataml import *\n", + "import plotly.express as px\n", + "from wordcloud import WordCloud\n", + "import matplotlib.pyplot as plt\n", + "import plotly.subplots as subplots\n", + "\n", + "# GenAI libs\n", + "import google.generativeai as genai\n", + "from google.generativeai import protos\n", + "\n", + "\n", + "display.max_rows = 5\n", + "pd.set_option('display.max_colwidth', 50)\n", + "pd.set_option('display.max_rows', 5)" + ] + }, + { + "cell_type": "markdown", + "id": "e365debb-d67d-45b9-aaad-e54bcc474818", + "metadata": {}, + "source": [ + "
\n", + "2. Connect to Vantage\n", + "

2.1 Connect to Vantage

\n", + "

We will be prompted to provide the password. We will enter the password, press the Enter key, and then use the down arrow to go to the next cell.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "454a2e81-c377-4058-9e68-78abd801ad9c", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../startup.ipynb\n", + "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", + "print(eng)\n", + "execute_sql('''SET query_band='DEMO=Text_Classification.ipynb;' UPDATE FOR SESSION;''')" + ] + }, + { + "cell_type": "markdown", + "id": "dee44870-6034-4353-96df-f5bd00970fa9", + "metadata": {}, + "source": [ + "

Begin running steps with Shift + Enter keys.

" + ] + }, + { + "cell_type": "markdown", + "id": "e9b5b39e-b3e1-45b8-a31a-4586872f93d1", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

2.2 Getting Data for This Demo

\n", + "

We have provided data for this demo on cloud storage. We have the option of either running the demo using foreign tables to access the data without using any storage on our environment or downloading the data to local storage, which may yield somewhat faster execution. However, we need to consider available storage. There are two statements in the following cell, and one is commented out. We may switch which mode we choose by changing the comment string.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65bf814d-4783-4213-936f-886667adf4a7", + "metadata": {}, + "outputs": [], + "source": [ + "# %run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_cloud');\" # Takes 1 minute\n", + "%run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_local');\" # Takes 2 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "4fe07fb7-5aa1-45c2-b66d-13e5ca8f7ce3", + "metadata": {}, + "source": [ + "
\n", + "3. Setup API key for Google Gemini\n", + "

Please enter the Google API Key, if you don't have one, please get it from here

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49e92709-9a74-4372-a57a-7b078fe2ae2c", + "metadata": {}, + "outputs": [], + "source": [ + "GOOGLE_API_KEY = getpass.getpass(prompt = 'Please enter GOOGLE_API_KEY: ')\n", + "genai.configure(api_key = GOOGLE_API_KEY)" + ] + }, + { + "cell_type": "markdown", + "id": "4e0d9505-20b1-4c9a-9efb-b2195d5d0362", + "metadata": {}, + "source": [ + "
\n", + "3.1. Define the Gemini model and Prompt\n", + "

The following section defines the type of Gemini model used. Here we use gemini-1.5-pro-latest

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35f19b6e-294a-4667-b894-93ad62c07139", + "metadata": {}, + "outputs": [], + "source": [ + "from google.generativeai.types import HarmCategory, HarmBlockThreshold\n", + "\n", + "model = genai.GenerativeModel(\n", + " model_name = \"models/gemini-1.5-pro-latest\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3d1a07c7-a9d7-49a7-a2ac-1e68c0a1282c", + "metadata": {}, + "source": [ + "
\n", + "4. Classify Complaints\n", + "

We'll use a sample of the data to classify complaints

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cac31f37-2eab-410a-b1ec-9fda12f141c5", + "metadata": {}, + "outputs": [], + "source": [ + "tdf = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Consumer_Complaints'))\n", + "tdf = tdf.assign(id = tdf.complaint_id).drop('complaint_id', axis = 1)\n", + "tdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0174db51-edcc-42d2-b251-c3171648a0e2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# take sample of records to go quickly\n", + "df = tdf.sample(50).to_pandas()\n", + "df['Prediction'] = \"\"\n", + "df['Reasoning with Chain of Thought'] = \"\"\n", + "\n", + "for i in tqdm(range(len(df))):\n", + " prompt = f'''\n", + " User prompt:\n", + " The following is text from a review:\n", + "\n", + " “{df['consumer_complaint_narrative'][i]}”\n", + "\n", + " Give me reasoning as well as Category for this review\n", + " \n", + " Instructions for Reasoning:\n", + " - Give me Reasoning in detail\n", + " - Only one sentence reasoning\n", + " Instructions for Category:\n", + " - The review falls into one of the following categories: Complaint, Non-Complaint\n", + " - Select one category from the given ones\n", + " - Important: Do not add any formating into the output. For example ** Complaint or **Complaint** refrain from such format\n", + " \n", + " Final output comes in the format:\n", + " Category: \n", + " Reasoning: \n", + " '''\n", + " output = model.generate_content([prompt])\n", + " finish_reason = output.candidates[0].finish_reason\n", + " \n", + " if finish_reason == protos.Candidate.FinishReason.STOP:\n", + " output = output.candidates[0].content.parts[0].text\n", + " # apply regex\n", + " category = re.search('Category:(.*)', output).group(1)\n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " else:\n", + " category = \"Non-Complaint\"\n", + " reasoning = \"\"\n", + " \n", + " df['Prediction'][i] = category\n", + " df['Reasoning with Chain of Thought'][i] = reasoning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5977498-de10-4392-888d-5541dae6801f", + "metadata": {}, + "outputs": [], + "source": [ + "df['Prediction'] = df['Prediction'].apply(lambda x: x.strip())\n", + "df['Reasoning with Chain of Thought'] = df['Reasoning with Chain of Thought'].apply(lambda x: x.strip())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23626789-0317-4edb-856c-cb5ab05cb1fb", + "metadata": {}, + "outputs": [], + "source": [ + "df[['id', 'consumer_complaint_narrative', 'Prediction', 'Reasoning with Chain of Thought']].head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "e8daa142-e48f-4caf-bd3a-4e85e93d2e6e", + "metadata": {}, + "source": [ + "
\n", + "

4.1 Consumer Complaints Prediction vs Occurrences

\n", + "\n", + "

A graph illustrating the relationship between consumer complaints prediction and the number of occurrences. This visual representation helps identify trends, patterns, and areas for improvement, enabling data-driven decision making.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "141400a2-a4a6-42ea-9f44-651534e474d6", + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "data = Counter(df['Prediction'])\n", + "\n", + "# Convert Counter data to DataFrame\n", + "viz_df = pd.DataFrame.from_dict(data, orient='index', columns=['Count']).reset_index()\n", + "\n", + "# Rename columns\n", + "viz_df.columns = ['Prediction', 'Count']\n", + "\n", + "# Create bar graph using Plotly Express\n", + "fig = px.bar(viz_df, x='Prediction', y='Count', color='Prediction',\n", + " labels={'Count': 'Number of Occurrences', 'Prediction': 'Prediction'})\n", + "\n", + "# Show the plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5cc01cc7-269f-4136-a945-fcc149b04e37", + "metadata": {}, + "source": [ + "
\n", + "

4.2 Word Cloud for Consumer Complaints Prediction

\n", + "\n", + "

A visual representation of consumer complaints prediction, highlighting the most frequent words and pain points in customer feedback. This word cloud helps identify trends, sentiment, and areas for improvement, enabling data-driven decision making.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "df58cb69-27f5-4878-a0d3-132c501a95e8", + "metadata": {}, + "outputs": [], + "source": [ + "complaint = df[df['Prediction'] == 'Complaint']\n", + "complaint_text = ' '.join(complaint['consumer_complaint_narrative'])\n", + "\n", + "# Replace 'X' with blank space\n", + "modified_string = complaint_text.replace('X', '')\n", + "\n", + "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "\n", + "# Display the word cloud\n", + "plt.imshow(wordcloud, interpolation='bilinear')\n", + "plt.title(\"Complaints\")\n", + "plt.tight_layout()\n", + "plt.axis(\"off\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "afe85dc8-e179-4f31-93db-1ba497724aa4", + "metadata": {}, + "source": [ + "
\n", + "

4.3 Word Cloud for Non-Complaints Prediction

\n", + "\n", + "

A visual representation of non-complaints prediction, highlighting the most frequent words and positive sentiments in customer feedback. This word cloud helps identify trends, sentiment, and areas of satisfaction, enabling data-driven decision making.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fef35daa-b6a9-44a8-8562-a464d49d92f5", + "metadata": {}, + "outputs": [], + "source": [ + "non_complaint = df[df['Prediction'] == 'Non-Complaint']\n", + "non_complaint_text = ' '.join(non_complaint['consumer_complaint_narrative'])\n", + "\n", + "# Replace 'X' with blank space\n", + "modified_string = non_complaint_text.replace('X', '')\n", + "if len(modified_string):\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "\n", + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.title(\"Non-Complaints\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Non-Complaint\")" + ] + }, + { + "cell_type": "markdown", + "id": "8522801e-48fb-4c6d-af93-3538e13ea294", + "metadata": {}, + "source": [ + "

Save the results back to Vantage.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b2d6203-9526-43ed-8b25-f5548376a10c", + "metadata": {}, + "outputs": [], + "source": [ + "copy_to_sql(df = df, table_name = 'reviews', if_exists = 'replace')" + ] + }, + { + "cell_type": "markdown", + "id": "d37c5d71-e309-496a-a0d9-5ac9b3567fe0", + "metadata": {}, + "source": [ + "
\n", + "5. Cleanup" + ] + }, + { + "cell_type": "markdown", + "id": "3265d0a0-2b15-4383-b657-c6329d547ece", + "metadata": {}, + "source": [ + "

Databases and Tables

\n", + "

The following code will clean up tables and databases created above.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86457ca1-64f1-4e0d-8c82-139333c6089e", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../run_procedure.py \"call remove_data('DEMO_ComplaintAnalysis');\" # Takes 10 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "358f56a0-ad97-4317-ab4c-88be00b8d179", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "remove_context()" + ] + }, + { + "cell_type": "markdown", + "id": "b82f52a3-2aad-44d8-b4dd-606249c85962", + "metadata": {}, + "source": [ + "
\n", + "Dataset:\n", + "
\n", + "
\n", + "

The dataset is sourced from Consumer Financial Protection Bureau

" + ] + }, + { + "cell_type": "markdown", + "id": "ffa325e2-7b1b-49c1-a932-e75f71bf002d", + "metadata": {}, + "source": [ + "
\n", + "
ClearScape Analytics™
\n", + "
\n", + "
\n", + " Copyright © Teradata Corporation - 2024. All Rights Reserved\n", + "
\n", + "
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb new file mode 100644 index 00000000..0772a035 --- /dev/null +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb @@ -0,0 +1,845 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "473a7d4a-dc19-4b44-a062-7da705197114", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Complaints Clustering using Vantage and Google Gemini\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "0b39f161-0198-4044-9956-3dc9c9b304b0", + "metadata": {}, + "source": [ + "

Introduction:

\n", + "\n", + "

This feature uses advanced clustering techniques powered by Teradata Vantage and Google Gemini's Generative AI Embeddings model to group similar customer complaints together. By identifying common themes and patterns, this functionality provides valuable insights into the key issues and pain points experienced by customers.

\n", + "\n", + "\n", + "

Key Features of Complaints Clustering:

\n", + "
    \n", + "
  • Leverages advanced clustering algorithms powered by Teradata Vantage and Google Gemini's Generative AI Embeddings.
    • \n", + "
  • Groups similar customer complaints together, revealing common themes and pain points.
    • \n", + "
  • Provides clients with a deeper understanding of the key issues affecting their customers.
    • \n", + "
  • Enables clients to prioritize and address the most pressing concerns more effectively.
    • \n", + "
  • Helps clients identify opportunities for product improvements and enhanced customer experience.
    • \n", + "
\n", + "\n", + "\n", + "

Unlock the revolutionary potential of Generative AI to categorize and analyze complaints with unparalleled efficiency.

\n", + "\n", + "

Steps in the analysis:

\n", + "
    \n", + "
  1. Configuring the environment
    2. \n", + "
  2. Connect to Vantage
    3. \n", + "
  3. Data Exploration
    4. \n", + "
  4. Setup API key for Google Gemini
    5. \n", + "
  5. Cluster the Complaints
    6. \n", + "
  6. Cleanup
    7. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "26a0c08c-7782-40f2-af3e-27d1adc952fa", + "metadata": {}, + "source": [ + "
\n", + "1. Configuring the environment\n", + "\n", + "

1.1 Install the required libraries

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e97af3c-0907-4a05-adf1-40b4ea1cded0", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "3a81070d-da02-49db-a422-c36143018277", + "metadata": {}, + "source": [ + "
\n", + "

Note: Please restart the kernel after executing these two lines. The simplest way to restart the Kernel is by typing zero zero: 0 0

" + ] + }, + { + "cell_type": "markdown", + "id": "13c3807b-2c66-4726-9182-5d15dc3e2051", + "metadata": {}, + "source": [ + "
\n", + "

Here, we import the required libraries, set environment variables and environment paths (if required).

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9f49cf5-f7b9-461a-a59c-867a414d3bcc", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import timeit\n", + "import tqdm\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "\n", + "from tqdm.notebook import *\n", + "tqdm_notebook.pandas()\n", + "\n", + "# plotting packages\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set(style = \"whitegrid\")\n", + "\n", + "# teradata lib\n", + "from teradataml import *\n", + "import teradataml\n", + "from teradataml import KMeans\n", + "from teradataml.analytics.valib import *\n", + "from teradataml import configure\n", + "from sqlalchemy import func\n", + "configure.val_install_location = \"val\"\n", + "configure.byom_install_location = \"byom\"\n", + "\n", + "\n", + "# genai\n", + "import getpass\n", + "import os\n", + "import google.generativeai as genai\n", + "\n", + "# Suppress warnings\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n", + "display.max_rows = 5\n", + "display.max_rows = 5\n", + "pd.set_option('display.max_colwidth', 50)\n", + "pd.set_option('display.max_rows', 5)\n", + "\n", + "display.print_sqlmr_query = False\n", + "display.suppress_vantage_runtime_warnings = True" + ] + }, + { + "cell_type": "markdown", + "id": "8452ad4b-84f7-4b7d-b523-5f24125264c7", + "metadata": {}, + "source": [ + "
\n", + "2. Connect to Vantage\n", + "

2.1 Connect to Vantage

\n", + "

We will be prompted to provide the password. We will enter the password, press the Enter key, and then use the down arrow to go to the next cell.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c25f3d12-a0db-43ce-8244-294ce0132097", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../startup.ipynb\n", + "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", + "print(eng)\n", + "execute_sql('''SET query_band='DEMO=Complaints_Analysis_GenAI.ipynb;' UPDATE FOR SESSION;''')" + ] + }, + { + "cell_type": "markdown", + "id": "11417221-3825-471d-af67-2f38de4059b7", + "metadata": {}, + "source": [ + "

Begin running steps with Shift + Enter keys.

" + ] + }, + { + "cell_type": "markdown", + "id": "1bf3f8c4-1b5f-4378-bf2a-594e50b471f2", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

2.2 Getting Data for This Demo

\n", + "

We have provided data for this demo on cloud storage. We have the option of either running the demo using foreign tables to access the data without using any storage on our environment or downloading the data to local storage, which may yield somewhat faster execution. However, we need to consider available storage. There are two statements in the following cell, and one is commented out. We may switch which mode we choose by changing the comment string.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "151fde7f-77b5-4c50-87b7-b182a07d19c0", + "metadata": {}, + "outputs": [], + "source": [ + "# %run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_cloud');\" # Takes 1 minute\n", + "%run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_local');\" # Takes 2 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "62bbb47c-6490-45a9-b3c0-f9281f49ae35", + "metadata": {}, + "source": [ + "
\n", + "3. Data Exploration\n", + "\n", + "

Now, We will load the data and explore it for further steps/

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "09c6a41f-08af-4c99-8b02-ef1e9e6759e4", + "metadata": {}, + "outputs": [], + "source": [ + "df = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Consumer_Complaints'))" + ] + }, + { + "cell_type": "markdown", + "id": "9cb7fca4-365a-41b3-a2f8-feb472be6786", + "metadata": {}, + "source": [ + "
\n", + "

3.1 Graph for Count of Product Complaints Over Years

\n", + "\n", + "

The provided graph visualizes the count of complaints over the past few years, categorized by product names.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ab30b1b-13e4-4720-9df2-a2901cf92785", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "viz_df = df.assign(year = func.td_year_of_calendar(df.date_received.expression))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b692e80-8976-4240-9599-5aac385c94ef", + "metadata": {}, + "outputs": [], + "source": [ + "pd_df = viz_df.select(['product','year','complaint_id']).groupby(['product', 'year']).agg(['count']).to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4161f6ec-4250-4c0c-823f-c832daea1192", + "metadata": {}, + "outputs": [], + "source": [ + "# Sorting the DataFrame by year for each product\n", + "pd_df_sorted = pd_df.sort_values(by=['product', 'year'])\n", + "\n", + "# Plotting using Plotly\n", + "fig = px.line(pd_df_sorted, x='year', y='count_complaint_id', color='product', markers=True, title='Count of Product Complaints Over Years')\n", + "fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1200, height=600)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d595d54c-e906-4411-b744-3cc61585dd5e", + "metadata": {}, + "source": [ + "
\n", + "

3.2 Graph for Count of Complaints by Months

\n", + "

The provided graph visualizes the count of complaints by months. We can see that the mean count is above 500, and the July and August months have the maximum complaints count.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d99c576-05c8-470c-b7a5-afaa1c71086c", + "metadata": {}, + "outputs": [], + "source": [ + "df = df.assign(complaint_month = func.td_month_of_year(df.date_received.expression))\n", + "grp_gen = df.select(['complaint_month','complaint_id']).groupby(['complaint_month']).agg(['count']).to_pandas()\n", + "\n", + "# Define a reverse mapping dictionary\n", + "reverse_month_mapping = {1: 'January', 2: 'February', 3: 'March', 4: 'April', 5: 'May', 6: 'June',\n", + " 7: 'July', 8: 'August', 9: 'September', 10: 'October', 11: 'November', 12: 'December'}\n", + "\n", + "# Create a new column with month names based on reverse mapping\n", + "grp_gen['month'] = grp_gen['complaint_month'].map(reverse_month_mapping)\n", + "\n", + "\n", + "fig = px.bar(grp_gen.sort_values(by = 'complaint_month'), x='month', y='count_complaint_id',\n", + " labels={'count_complaint_id': 'Number of Complaints', 'month': 'Complaint Month'},\n", + " title='Number of Complaints by Month')\n", + "\n", + "# Add hover information\n", + "fig.update_traces(hovertemplate='Month: %{x}
Number of Complaints: %{y:,}')\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5c43d615-4487-4b26-9339-c5a26515e949", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

3.3 Graph for Number of Complaints by Product

The graph displays the number of complaints received for different products. The data shows that the highest number of complaints are related to credit cards or prepaid cards, as well as credit reporting and credit repair services.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1797ad2-aa25-4945-aa85-e30611ae96da", + "metadata": {}, + "outputs": [], + "source": [ + "# Assuming df is your DataFrame and sns is seaborn\n", + "\n", + "grp_gen = df.select(['product','complaint_id']).groupby(['product']).agg(['count']).to_pandas()\n", + "\n", + "fig = px.bar(grp_gen, x='product', y='count_complaint_id',\n", + " labels={'count_complaint_id': 'Number of Complaints', 'product': 'Product'},\n", + " title='Number of Complaints by Product')\n", + "\n", + "# Add hover information\n", + "fig.update_traces(hovertemplate='Product: %{x}
Number of Complaints: %{y:,}')\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2b83e400-c23c-438f-8e40-218e90e36e7f", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

3.4 Graph for Number of Complaints by Issue

The graph displays the number of complaints received for different issues. The data shows that the highest number of complaints are related to issue of incorrect information on your report.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eab68ba2-e74b-418d-bee4-46750a90a444", + "metadata": {}, + "outputs": [], + "source": [ + "# Assuming df is your DataFrame and sns is seaborn\n", + "\n", + "grp_gen = df.select(['issue','complaint_id']).groupby(['issue']).agg(['count']).to_pandas()\n", + "\n", + "grp_gen = grp_gen.sort_values('count_complaint_id', ascending = False)[:10]\n", + "\n", + "fig = px.bar(grp_gen, x='issue', y='count_complaint_id',\n", + " labels={'count_complaint_id': 'Number of Complaints', 'issue': 'Issue'},\n", + " title='Number of Complaints by Issue(Top 10)')\n", + "\n", + "# Add hover information\n", + "fig.update_traces(hovertemplate='Issue: %{x}
Number of Complaints: %{y:,}')\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9655211c-4ba2-4a33-b3f2-777633fbc889", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

3.5 Graph for Number of Complaints by Sub-Issue

\n", + "\n", + "

The graph displays the number of complaints received for different sub-issues. The data shows that the highest number of complaints are related to issue of information belongs to someone else.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d456215-beee-4133-9ff5-70131299bdf0", + "metadata": {}, + "outputs": [], + "source": [ + "# Assuming df is your DataFrame and sns is seaborn\n", + "\n", + "grp_gen = df.select(['sub_issue','complaint_id']).groupby(['sub_issue']).agg(['count']).to_pandas()\n", + "\n", + "grp_gen = grp_gen.sort_values('count_complaint_id', ascending = False)[:10]\n", + "\n", + "fig = px.bar(grp_gen, x='sub_issue', y='count_complaint_id',\n", + " labels={'count_complaint_id': 'Number of Complaints', 'sub_issue': 'Sub-Issue'},\n", + " title='Number of Complaints by Sub-Issue(Top 10)')\n", + "\n", + "# Add hover information\n", + "fig.update_traces(hovertemplate='Sub-Issue: %{x}
Number of Complaints: %{y:,}')\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "17a98018-d49e-49a3-9e98-b0e5cd2bdc86", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

3.6 Graph for Number of Complaints by Channel

\n", + "\n", + "

The graph displays the number of complaints received for different issues. The data shows that the all the complaints are submitted by web channel.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c63e282f-4e6f-40d5-8edf-852c07b1cb4c", + "metadata": {}, + "outputs": [], + "source": [ + "grp_gen = df.select(['submitted_via','complaint_id']).groupby(['submitted_via']).agg(['count']).to_pandas()\n", + "\n", + "# Create a mapping of numbers to product names\n", + "product_mapping = {i: product for i, product in enumerate(grp_gen['submitted_via'])}\n", + "\n", + "# Replace product names with numbers in the DataFrame\n", + "grp_gen['product_num'] = grp_gen['submitted_via'].map({product: i for i, product in enumerate(grp_gen['submitted_via'])})\n", + "\n", + "fig = px.bar(grp_gen, x='submitted_via', y='count_complaint_id',\n", + " labels={'count_complaint_id': 'Number of Complaints', 'submitted_via': 'Submitted Via'},\n", + " title='Number of Complaints by Channel')\n", + "\n", + "# Add hover information\n", + "fig.update_traces(hovertemplate='Submitted Via: %{x}
Number of Complaints: %{y:,}')\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "03493ca9-52b0-4546-96d8-e4d26485f557", + "metadata": {}, + "source": [ + "
\n", + "4. Setup API key for Google Gemini\n", + "

Please enter the Google API Key, if you don't have one, please get it from here

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b630738-a5b1-4815-afcc-b06444ffc2e0", + "metadata": {}, + "outputs": [], + "source": [ + "GOOGLE_API_KEY = getpass.getpass(prompt = 'Please enter GOOGLE_API_KEY: ')\n", + "genai.configure(api_key = GOOGLE_API_KEY)" + ] + }, + { + "cell_type": "markdown", + "id": "f279ee0c-6f51-4f8c-854d-b61d01278dcc", + "metadata": {}, + "source": [ + "
\n", + "5. Cluster the Complaints\n", + "\n", + "

For our complaint clustering task, we'll be using a sample of the data to cluster the complaints. This approach will allow us to effectively analyze and categorize the complaints without using the entire dataset.

\n", + "\n", + "

Here we have used text-embedding-004 model google gemini offerings. please click here to learn more about this embeddings model embeddings

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81fc5101-9387-4846-ac0e-23f23495502c", + "metadata": {}, + "outputs": [], + "source": [ + "from google.api_core import retry\n", + "\n", + "def make_embed_text_fn(model):\n", + "\n", + " @retry.Retry(timeout=300.0)\n", + " def embed_fn(text: str) -> list[float]:\n", + " # Set the task_type to CLUSTERING.\n", + " embedding = genai.embed_content(model=model,\n", + " content=text,\n", + " task_type=\"clustering\")\n", + " return embedding[\"embedding\"]\n", + "\n", + " return embed_fn\n", + "\n", + "def create_embeddings(df):\n", + " model = 'models/text-embedding-004'\n", + " df['Embeddings'] = df['consumer_complaint_narrative'].progress_apply(make_embed_text_fn(model))\n", + " \n", + " # Generate all the embeddings columns from the \"embeddings\" column.\n", + " for i in range(len(df['Embeddings'][0])):\n", + " df[\"embeddings_{}\".format(i)] = df[\"Embeddings\"].apply(lambda x: x[i])\n", + " \n", + " # drop embedding \n", + " df = df.drop(\"Embeddings\", axis=1)\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7be29ef-8877-40a9-9c83-bf7784bc0aa8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "embedded_df = create_embeddings(df.to_pandas())" + ] + }, + { + "cell_type": "markdown", + "id": "75378143-dcba-47ba-a5a7-e434984423d7", + "metadata": {}, + "source": [ + "

Save the results back to Vantage.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4aa8b2b9-0ce8-4790-aca0-9747123f2002", + "metadata": {}, + "outputs": [], + "source": [ + "copy_to_sql(embedded_df, table_name ='complaints_embeddings', if_exists = 'replace')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "141d4c41-6cad-4491-84f9-7d37e18444e1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "embedded_df = DataFrame('complaints_embeddings')\n", + "embedded_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9b99852-9bc1-4ae5-b98c-21b1096fc9c8", + "metadata": {}, + "outputs": [], + "source": [ + "cols = list(embedded_df.columns[:17]) + ['consumer_complaint_narrative']\n", + "embedded_df = embedded_df.drop(columns=cols)\n", + "copy_to_sql(df = embedded_df, table_name = 'kmeans_features', if_exists = 'replace')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5e1fa67-cd5c-4503-961c-a53d79159c4a", + "metadata": {}, + "outputs": [], + "source": [ + "no_clusters = 5\n", + "\n", + "# drop existing model\n", + "try:\n", + " db_drop_table(table_name = \"KMeans_Model\")\n", + "except:\n", + " pass\n", + "\n", + "# Model Table\n", + "query = f'''\n", + " SELECT * from TD_KMeans (\n", + " ON kmeans_features as InputTable\n", + " OUT TABLE ModelTable(KMeans_Model)\n", + " USING\n", + " IdColumn('complaint_id')\n", + " TargetColumns{tuple(embedded_df.columns[19:])}\n", + " NumClusters({no_clusters})\n", + " OutputClusterAssignment('true')\n", + ") AS dt;\n", + "'''\n", + "\n", + "execute_sql(query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63a55e88-1632-46eb-b86c-3ee3d16edd8a", + "metadata": {}, + "outputs": [], + "source": [ + "KMeans_Model = DataFrame(\"KMeans_Model\")\n", + "print(\"Data information: \\n\", KMeans_Model.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55d1616f-da1a-4b6e-9824-1262d2eb0d70", + "metadata": {}, + "outputs": [], + "source": [ + "no_clusters = 5\n", + "\n", + "# for Cluster info, used later for filtering\n", + "query = f'''\n", + " SELECT * from TD_KMeans (\n", + " ON kmeans_features as InputTable\n", + " USING\n", + " IdColumn('complaint_id')\n", + " TargetColumns{tuple(embedded_df.columns[19:])}\n", + " NumClusters({no_clusters})\n", + " OutputClusterAssignment('true')\n", + ") AS dt;\n", + "'''\n", + "\n", + "kmeans_out_sql = DataFrame.from_query(query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d5a3743-a51a-46cd-888c-eecd85e3f3ab", + "metadata": {}, + "outputs": [], + "source": [ + "kmeans_out_sql" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cd95cbc-7434-4248-84f8-20260410bd7e", + "metadata": {}, + "outputs": [], + "source": [ + "embeddings_cluster = DataFrame('complaints_embeddings').join(other=kmeans_out_sql, how=\"full\", on=\"complaint_id=complaint_id\", lprefix= \"L_\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94563f0e-6d80-4b73-ac5a-8aa787ddfc9b", + "metadata": {}, + "outputs": [], + "source": [ + "# View complaints in cluster 1\n", + "embeddings_cluster[['td_clusterid_kmeans','complaint_id','consumer_complaint_narrative']].loc[embeddings_cluster.td_clusterid_kmeans==1]" + ] + }, + { + "cell_type": "markdown", + "id": "83a1dec8-8fe4-4afa-833f-9739dd76297e", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

5.1 Visualization of Clusters with Complaints

\n", + "\n", + "

The graph displays the clustering of complaints into distinct groups. Based on the analysis, the data has been divided into 5 optimal clusters, each representing a unique pattern or category of complaints. This clustering approach helps to identify the key areas or types of complaints that are most prevalent, allowing for more targeted investigation and resolution efforts.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fa92119-3d20-4f89-909c-9a12c96eceb2", + "metadata": {}, + "outputs": [], + "source": [ + "# emb = DataFrame('kmeans_features').to_pandas()\n", + "clus = embeddings_cluster.to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4f83453-3872-4001-b5b3-ac442984ba0a", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.manifold import TSNE\n", + "\n", + "tsne = TSNE(n_components=2, random_state=42)\n", + "tsne_result = tsne.fit_transform(clus.iloc[:, 20:-1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ebb8195-da62-47bc-b5bf-5d8868def61a", + "metadata": {}, + "outputs": [], + "source": [ + "tsne_df = pd.DataFrame(tsne_result, columns=['tsne_1', 'tsne_2'])\n", + "tsne_df['cluster_id'] = clus['td_clusterid_kmeans']\n", + "tsne_df['complaint_id'] = clus['consumer_complaint_narrative']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "244cbc6d-6596-4daf-8bea-f8fb4fb08f9f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import plotly.express as px\n", + "\n", + "# Assuming you have already computed tsne_df as per the previous example\n", + "\n", + "# Create a new DataFrame combining t-SNE results with complaint information\n", + "tsne_complaint_df = pd.DataFrame(tsne_result, columns=['tsne_1', 'tsne_2'])\n", + "tsne_complaint_df['cluster_id'] = clus['td_clusterid_kmeans']\n", + "tsne_complaint_df['complaint_id'] = clus['complaint_id']\n", + "tsne_complaint_df['complaint'] = clus['consumer_complaint_narrative']\n", + "\n", + "# Truncate text for hover data\n", + "max_chars = 50 # Maximum characters to display\n", + "tsne_complaint_df['truncted_complaint'] = clus['consumer_complaint_narrative'].apply(lambda x: x[:max_chars] + '...' if len(x) > max_chars else x)\n", + "\n", + "# Plot using Plotly Express\n", + "fig = px.scatter(tsne_complaint_df, x='tsne_1', y='tsne_2', color='cluster_id',\n", + " hover_data=['complaint_id', 'truncted_complaint', 'cluster_id'])\n", + "\n", + "fig.update_traces(marker=dict(size=15))\n", + "fig.update_layout(\n", + " title='t-SNE Visualization of Clusters with Complaints',\n", + " xaxis_title='dimension-1',\n", + " yaxis_title='dimension-2',\n", + " xaxis=dict(tickangle=45),\n", + " width=1000,\n", + " height=800,\n", + " hoverlabel=dict(\n", + " bgcolor=\"white\",\n", + " font_size=16,\n", + " font_family=\"Rockwell\"\n", + " ),\n", + " autosize=False,\n", + ")\n", + "\n", + "# Customize the hovertemplate\n", + "fig.update_traces(hovertemplate=\"Complaint ID: %{customdata[0]}
\"\n", + " \"Complaint: %{customdata[1]}
\"\n", + " \"Cluster ID: %{customdata[2]}
\")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f8f29be4-9609-4c96-a7ab-e2f4a60ec106", + "metadata": {}, + "source": [ + "
\n", + "6. Cleanup" + ] + }, + { + "cell_type": "markdown", + "id": "73a8ead8-0698-4449-bc11-5a07351e3fb6", + "metadata": {}, + "source": [ + "

Databases and Tables

\n", + "

The following code will clean up tables and databases created above.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc34cf17-ecc6-441b-a69d-21a34de7205d", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../run_procedure.py \"call remove_data('DEMO_ComplaintAnalysis');\" # Takes 10 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e697922-8d63-4709-b7b3-0f705a938255", + "metadata": {}, + "outputs": [], + "source": [ + "remove_context()" + ] + }, + { + "cell_type": "markdown", + "id": "75e7e6a6-1413-4b18-b800-4899bcdfb31a", + "metadata": {}, + "source": [ + "
\n", + "Dataset:\n", + "
\n", + "
\n", + "

The dataset is sourced from Consumer Financial Protection Bureau

" + ] + }, + { + "cell_type": "markdown", + "id": "f9586bbb-6521-4988-bc00-40a67b08b695", + "metadata": {}, + "source": [ + "
\n", + "
ClearScape Analytics™
\n", + "
\n", + "
\n", + " Copyright © Teradata Corporation - 2024. All Rights Reserved\n", + "
\n", + "
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb new file mode 100644 index 00000000..586541d1 --- /dev/null +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb @@ -0,0 +1,733 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5ce3dd8a-f3e5-40d5-ab4c-d40cc358cf7f", + "metadata": {}, + "source": [ + "
\n", + "

\n", + " Sentiment Analysis Using Vantage and Google Gemini\n", + "
\n", + " \"Teradata\"\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "57054f7e-befe-4029-b4ac-b82019240b1f", + "metadata": {}, + "source": [ + "

Introduction:

\n", + "\n", + "

Sentiment analysis using Teradata Vantage and the advanced Google Gemini model involves leveraging cutting-edge technologies to extract insights from unstructured data. This process empowers businesses to swiftly identify and address customer concerns, enhancing overall customer satisfaction and loyalty.

\n", + "\n", + "

Key Features:

\n", + "
    \n", + "
  • Polarity Classification: Identifies specific emotions such as happiness, anger, sadness, and more.
    • \n", + "
  • Emotion Detection: The system comprehends the nuances of customer feedback, capturing subtle differences in tone and language.
    • \n", + "
  • Aspect-Based Sentiment Analysis: Analyzes sentiment towards specific features or aspects of a product or service.
    • \n", + "
  • Fine-Grained Sentiment Analysis: Provides detailed sentiment analysis at the phrase or clause level.
    • \n", + "
  • Subjectivity Classification: Distinguishes between objective and subjective text.
    • \n", + "\n", + "
\n", + "\n", + "\n", + "

Benefits:

\n", + "
    \n", + "
  • Improved Customer Satisfaction: Enhances customer experience by addressing concerns and improving products.
    • \n", + "
  • Competitive Advantage: Provides valuable insights to stay ahead of competitors.
    • \n", + "
  • Objective Insights: Offers unbiased and accurate sentiment analysis.
    • \n", + "
  • Real-Time Decision Making: Enables swift responses to customer concerns and market trends.
    • \n", + "
  • Scalability: Handles large volumes of data efficiently.
    • \n", + "
\n", + "\n", + "

Experience the transformative power of Generative AI in complaints classification.

\n", + "\n", + "

Steps in the analysis:

\n", + "
    \n", + "
  1. Configuring the environment
    2. \n", + "
  2. Connect to Vantage
    3. \n", + "
  3. Setup API key for Google Gemini
    4. \n", + "
  4. Complaints Sentiment Analysis
    5. \n", + "
  5. Cleanup
    6. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "4e457db5-d4df-4e7f-a11a-fcb1416e462d", + "metadata": {}, + "source": [ + "
\n", + "1. Configuring the environment\n", + "\n", + "

1.1 Install the required libraries

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03eb4587-6632-4b08-a121-c98ea5a8fc48", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "c1fd81e4-0df9-4360-b3ac-72214f135296", + "metadata": {}, + "source": [ + "
\n", + "

Note: Please restart the kernel after executing these two lines. The simplest way to restart the Kernel is by typing zero zero: 0 0

" + ] + }, + { + "cell_type": "markdown", + "id": "dc278740-1511-4423-bf2b-92a5109a47bf", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

1.2 Import the required libraries

\n", + "\n", + "

Here, we import the required libraries, set environment variables and environment paths (if required).

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f183178-9807-4538-b794-922820346ebc", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import timeit\n", + "from tqdm import tqdm\n", + "from teradataml import *\n", + "from sqlalchemy import func\n", + "import plotly.express as px\n", + "from wordcloud import WordCloud\n", + "import matplotlib.pyplot as plt\n", + "import plotly.subplots as subplots\n", + "\n", + "# GenAI libs\n", + "import google.generativeai as genai\n", + "from google.generativeai import protos\n", + "\n", + "display.max_rows = 5\n", + "pd.set_option('display.max_colwidth', 50)\n", + "pd.set_option('display.max_rows', 5)" + ] + }, + { + "cell_type": "markdown", + "id": "1763fd95-5000-4a2d-8b86-7be261e20847", + "metadata": {}, + "source": [ + "
\n", + "2. Connect to Vantage\n", + "

2.1 Connect to Vantage

\n", + "

We will be prompted to provide the password. We will enter the password, press the Enter key, and then use the down arrow to go to the next cell.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de5eba56-d38f-4204-b30d-232c7d894eb0", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../startup.ipynb\n", + "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", + "print(eng)\n", + "execute_sql('''SET query_band='DEMO=Sentiment_Analysis_Bedrock.ipynb;' UPDATE FOR SESSION;''')" + ] + }, + { + "cell_type": "markdown", + "id": "0ce81699-6345-4dbf-a3a8-b9402c7b6a98", + "metadata": {}, + "source": [ + "

Begin running steps with Shift + Enter keys.

" + ] + }, + { + "cell_type": "markdown", + "id": "8dca7332-48f5-4702-8b56-d8a03fbb87a2", + "metadata": {}, + "source": [ + "
\n", + "\n", + "

2.2 Getting Data for This Demo

\n", + "

We have provided data for this demo on cloud storage. We have the option of either running the demo using foreign tables to access the data without using any storage on our environment or downloading the data to local storage, which may yield somewhat faster execution. However, we need to consider available storage. There are two statements in the following cell, and one is commented out. We may switch which mode we choose by changing the comment string.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c87c4008-c993-4109-be9d-191a15ded026", + "metadata": {}, + "outputs": [], + "source": [ + "# %run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_cloud');\" # Takes 1 minute\n", + "%run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_local');\" # Takes 2 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "d556a860-e27f-432d-a4d1-f4bd903e34fd", + "metadata": {}, + "source": [ + "
\n", + "3. Setup API key for Google Gemini\n", + "

Please enter the Google API Key, if you don't have one, please get it from here

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0f2a8a6-5a5b-483d-81e7-46682c764bec", + "metadata": {}, + "outputs": [], + "source": [ + "GOOGLE_API_KEY = getpass.getpass(prompt = 'Please enter GOOGLE_API_KEY: ')\n", + "genai.configure(api_key = GOOGLE_API_KEY)" + ] + }, + { + "cell_type": "markdown", + "id": "6e3b179f-4649-483a-a470-db064266c3b6", + "metadata": {}, + "source": [ + "
\n", + "3.1. Define the Gemini model and Prompt\n", + "

The following section defines the type of Gemini model used. Here we use gemini-1.5-pro-latest

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6feda0d-d9da-4e57-9fdb-00635c6bdf2a", + "metadata": {}, + "outputs": [], + "source": [ + "from google.generativeai.types import HarmCategory, HarmBlockThreshold\n", + "\n", + "model = genai.GenerativeModel(\n", + " model_name = \"models/gemini-1.5-pro-latest\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4332348c-f45e-4f03-9184-81746d39f566", + "metadata": {}, + "source": [ + "
\n", + "4. Complaints Sentiment Analysis\n", + "

We'll analyze the sentiments of a sample of customer complaints data.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff462b32-8742-4ac0-a40f-3e201120b3bc", + "metadata": {}, + "outputs": [], + "source": [ + "tdf = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Consumer_Complaints'))\n", + "tdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fee7277b-14ed-489e-8ffd-0a59bf2f0ca6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pd_df = tdf.sample(20).to_pandas()\n", + "pd_df['Sentiment'] = \"\"\n", + "pd_df['Reasoning with Chain of Thought'] = ''" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d091617-4179-4953-ac50-7f9e966e4c0d", + "metadata": {}, + "outputs": [], + "source": [ + "# Sentiment\n", + "for i in tqdm(range(len(pd_df))):\n", + " prompt = f'''Analyze the following customer review:\n", + " \"{pd_df['consumer_complaint_narrative'][i]}\"\n", + "\n", + " Classify the sentiment as Positive, Negative or Neutral.\n", + "\n", + " Provide a detailed single-sentence explanation for your classification without using any special formatting (no commas or asterisks).\n", + "\n", + " Your response should follow this format:\n", + " Sentiment: [Your classification]\n", + " Reasoning: [Your explanation]'''\n", + " \n", + " output = model.generate_content([prompt])\n", + " finish_reason = output.candidates[0].finish_reason\n", + " \n", + " if finish_reason == protos.Candidate.FinishReason.STOP:\n", + " print(f'\\n\\n ---- Row op ---- \\n {output} \\n\\n')\n", + " output = output.candidates[0].content.parts[0].text\n", + "\n", + " sentiment = re.search('Sentiment:(.*)', output).group(1)\n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " else:\n", + " sentiment = 'Neutral'\n", + " reasoning = ''\n", + " \n", + " pd_df['Sentiment'][i] = sentiment\n", + " pd_df['Reasoning with Chain of Thought'][i] = reasoning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f4f878b-c2e3-45a3-b7f4-1e4927f569a2", + "metadata": {}, + "outputs": [], + "source": [ + "pd_df['Sentiment'] = pd_df['Sentiment'].apply(lambda x: x.strip())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf52600c-81d5-4b26-8c61-616b90f78f7b", + "metadata": {}, + "outputs": [], + "source": [ + "pd_df[['complaint_id', 'consumer_complaint_narrative', 'Sentiment', 'Reasoning with Chain of Thought']]" + ] + }, + { + "cell_type": "markdown", + "id": "9162a5c4-a3ee-44c0-be09-5bfaa9b5d827", + "metadata": {}, + "source": [ + "

Save the results back to Vantage.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1626db2a-1159-4560-8ae3-704abcd9e081", + "metadata": {}, + "outputs": [], + "source": [ + "copy_to_sql(df = pd_df, table_name = 'complaints_sentiment', if_exists = 'replace')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5245af62-abf4-4ed5-b4c2-7f06620587a8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sentiment_df = DataFrame('complaints_sentiment')\n", + "sentiment_df = sentiment_df.assign(date_received = sentiment_df.date_received.cast(type_=DATE))\n", + "print('Before: ', sentiment_df.shape)\n", + "sentiment_df = sentiment_df.loc[sentiment_df.Sentiment.isin(['Positive', 'Negative', 'Neutral'])]\n", + "print('After: ', sentiment_df.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "c29ca4fd-3282-4eac-a402-0c72542a5a30", + "metadata": {}, + "source": [ + "
\n", + "

4.1 Consumer Sentiments Prediction vs Occurrences

\n", + "\n", + "

A graph illustrating the relationship between consumer sentiments (positive, negative, neutral) prediction and the number of occurrences. This visual representation helps identify trends, patterns, and areas for improvement, enabling data-driven decision making.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "faa3be08-0b27-4748-a7d8-32023b183761", + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "data = Counter(sentiment_df[['Sentiment']].get_values().flatten())\n", + "\n", + "# Convert Counter data to DataFrame\n", + "df = pd.DataFrame.from_dict(data, orient='index', columns=['Count']).reset_index()\n", + "\n", + "# Rename columns\n", + "df.columns = ['Sentiment', 'Count']\n", + "\n", + "# Create bar graph using Plotly Express\n", + "fig = px.bar(df, x='Sentiment', y='Count', color='Sentiment',\n", + " labels={'Count': 'Number of Occurrences', 'Sentiment': 'Sentiment'})\n", + "\n", + "# Show the plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1211a149-ec81-4cd3-8423-963952da4d01", + "metadata": {}, + "source": [ + "
\n", + "

4.2 Word Cloud for Negative Consumer Sentiment Prediction

\n", + "\n", + "

Unlock the power of customer feedback with our intuitive word cloud visualization, which provides a comprehensive snapshot of negative consumer complaints sentiment. This innovative tool highlights the most frequently occurring words and pain points in customer feedback, empowering businesses to:

  1. Identify trends and sentiment patterns
  2. Pinpoint areas for improvement
  3. Make data-driven decisions to enhance customer satisfaction and loyalty

By leveraging this word cloud, businesses can proactively address customer concerns, refine their products and services, and ultimately drive growth through a deeper understanding of their customers' needs and preferences.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7f3644c-c55d-4864-87ee-57403c86e9b7", + "metadata": {}, + "outputs": [], + "source": [ + "neg = sentiment_df[sentiment_df['Sentiment'] == 'Negative'].to_pandas()\n", + "neg_text = ' '.join(neg['consumer_complaint_narrative'])\n", + "\n", + "# Replace 'X' with blank space\n", + "modified_string = neg_text.replace('X', '')\n", + "\n", + "if len(modified_string) > 0:\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "\n", + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Negative Sentiments\")" + ] + }, + { + "cell_type": "markdown", + "id": "a3c9017a-4470-4184-b7c3-334a2dd7ef4d", + "metadata": {}, + "source": [ + "
\n", + "

4.3 Word Cloud for Neutral Consumer Sentiment Prediction

\n", + "\n", + "

Tap into the insights of customer feedback with our intuitive word cloud visualization, which offers a detailed overview of neutral consumer complaints sentiment

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02aede14-b229-4835-a878-aed4bf1fef94", + "metadata": {}, + "outputs": [], + "source": [ + "neu = sentiment_df[sentiment_df['Sentiment'] == 'Neutral'].to_pandas()\n", + "neu_text = ' '.join(neu['consumer_complaint_narrative'])\n", + "\n", + "# Replace 'X' with blank space\n", + "modified_string = neu_text.replace('X', '')\n", + "\n", + "if len(modified_string) > 0:\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "\n", + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Neutral Sentiments\")" + ] + }, + { + "cell_type": "markdown", + "id": "d63d3d37-cf05-4497-9d74-a612762fe3e7", + "metadata": {}, + "source": [ + "
\n", + "

4.4 Word Cloud for Positive Consumer Sentiment Prediction

\n", + "\n", + "

Explore customer feedback insights with our intuitive word cloud visualization, providing a detailed overview of consumer sentiment.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f881a57-4421-436b-9d2b-1103ba6ad005", + "metadata": {}, + "outputs": [], + "source": [ + "pos = sentiment_df[sentiment_df['Sentiment'] == 'Positive'].to_pandas()\n", + "pos_text = ' '.join(pos['consumer_complaint_narrative'])\n", + "\n", + "# Replace 'X' with blank space\n", + "modified_string = pos_text.replace('X', '')\n", + "\n", + "if len(modified_string) > 0:\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "\n", + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Positive Sentiments\")" + ] + }, + { + "cell_type": "markdown", + "id": "3f81682d-268c-498a-b7d6-fa3d57698fd8", + "metadata": {}, + "source": [ + "
\n", + "

4.5 Negative Sentiment per Product Over Years

\n", + "\n", + "

This graph tracks the negative sentiment associated with different products over time, offering valuable insights into evolving customer perceptions and pain points.

\n", + "\n", + "

We will use Vantage in-db function OrdinalEncodingFit which will identifies distinct categorical values from the input data or a user-defined list and generates the distinct categorical values along with the ordinal value for each category. 0:\n", + " viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", + "\n", + " # Sorting the DataFrame by year for each product\n", + " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", + "\n", + " # Plotting using Plotly\n", + " fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Negative Sentiment per Product Over Years')\n", + " fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", + "\n", + " fig.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Negative Sentiments\")" + ] + }, + { + "cell_type": "markdown", + "id": "8fc49e14-3beb-4206-811e-558026246e50", + "metadata": {}, + "source": [ + "

\n", + "

4.6 Neutral Sentiment per Product Over Years

\n", + "\n", + "

This graph tracks the neutral sentiment associated with different products over time, offering valuable insights into evolving customer perceptions and pain points.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04da72eb-c4c4-4087-9a1f-f47f0dc31e23", + "metadata": {}, + "outputs": [], + "source": [ + "viz_neg = result[result['Sentiment'] == 0]\n", + "\n", + "if viz_neg.shape[0] > 0:\n", + " viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", + "\n", + " # Sorting the DataFrame by year for each product\n", + " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", + "\n", + " # Plotting using Plotly\n", + " fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Neutral Sentiment per Product Over Years')\n", + " fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", + "\n", + " fig.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Neutral Sentiments\")" + ] + }, + { + "cell_type": "markdown", + "id": "389e811a-737d-4953-965b-56b591409aad", + "metadata": {}, + "source": [ + "
\n", + "

4.7 Positive Sentiment per Product Over Years

\n", + "\n", + "

This graph tracks the positive sentiment associated with different products over time, offering valuable insights into evolving customer perceptions and pain points.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b218073f-c7b5-4971-ac96-5bd2217e80dd", + "metadata": {}, + "outputs": [], + "source": [ + "viz_neg = result[result['Sentiment'] == 1]\n", + "\n", + "if viz_neg.shape[0] > 0:\n", + " viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", + "\n", + " # Sorting the DataFrame by year for each product\n", + " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", + "\n", + " # Plotting using Plotly\n", + " fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Positive Sentiment per Product Over Years')\n", + " fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", + "\n", + " fig.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Positive Sentiments\")" + ] + }, + { + "cell_type": "markdown", + "id": "a44bc090-a387-41b6-90a5-638a16dc3d4f", + "metadata": {}, + "source": [ + "
\n", + "5. Cleanup" + ] + }, + { + "cell_type": "markdown", + "id": "3b6ba28d-07d2-4322-9f59-e81855e1389c", + "metadata": {}, + "source": [ + "

Databases and Tables

\n", + "

The following code will clean up tables and databases created above.

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e572167a-a4db-42e5-8881-578dfbaa0c09", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../run_procedure.py \"call remove_data('DEMO_ComplaintAnalysis');\" # Takes 10 seconds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7e82715-0855-4d59-990c-481d3a9d3f1b", + "metadata": {}, + "outputs": [], + "source": [ + "remove_context()" + ] + }, + { + "cell_type": "markdown", + "id": "ebbd137a-7bfc-49fa-a18e-ca34ba68919d", + "metadata": {}, + "source": [ + "
\n", + "Dataset:\n", + "
\n", + "
\n", + "

The dataset is sourced from Consumer Financial Protection Bureau

" + ] + }, + { + "cell_type": "markdown", + "id": "19bdc0df-dc13-4a13-bf73-c7039a54c3ba", + "metadata": {}, + "source": [ + "
\n", + "
ClearScape Analytics™
\n", + "
\n", + "
\n", + " Copyright © Teradata Corporation - 2024. All Rights Reserved\n", + "
\n", + "
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/UseCases/Complaints_Analysis_GenAI/Speech_Recognition.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Speech_Recognition.ipynb similarity index 94% rename from UseCases/Complaints_Analysis_GenAI/Speech_Recognition.ipynb rename to UseCases/Complaints_Analysis_GenAI_Gemini/Speech_Recognition.ipynb index 55da8371..6ce4ff15 100644 --- a/UseCases/Complaints_Analysis_GenAI/Speech_Recognition.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Speech_Recognition.ipynb @@ -42,6 +42,7 @@ "\n", "

Steps in the analysis:

\n", "
    \n", + "
  1. Configuring the environment
    2. \n", "
  2. Connect to Vantage
    3. \n", "
  3. Setup API key for Google Gemini
    4. \n", "
  4. Define the Gemini model and Prompt
    5. \n", @@ -55,8 +56,10 @@ "id": "4292dcda-26f7-4a06-a471-19cd09b32d00", "metadata": {}, "source": [ - "
    \n", - "

    Downloading and installing additional software needed" + "

    \n", + "1. Configuring the environment\n", + "\n", + "

    1.1 Install the required libraries

    " ] }, { @@ -68,7 +71,8 @@ }, "outputs": [], "source": [ - "!pip install -q -U google-generativeai" + "%%capture\n", + "!pip install -r requirements.txt --quiet" ] }, { @@ -85,7 +89,10 @@ "id": "c233ee65-c69c-4ea4-8964-d4b53acdb5cd", "metadata": {}, "source": [ - "
    \n", + "
    \n", + "\n", + "

    1.2 Import the required libraries

    \n", + "\n", "

    Here, we import the required libraries, set environment variables and environment paths (if required).

    " ] }, @@ -110,7 +117,8 @@ "metadata": {}, "source": [ "
    \n", - "1. Connect to Vantage\n", + "2. Connect to Vantage\n", + "

    2.1 Connect to Vantage

    \n", "

    We will be prompted to provide the password. We will enter the password, press the Enter key, and then use the down arrow to go to the next cell.

    " ] }, @@ -139,7 +147,9 @@ "id": "b562b3e9-d58c-4220-b23f-3d22998cf5e0", "metadata": {}, "source": [ - "

    Getting Data for This Demo

    \n", + "
    \n", + "\n", + "

    2.2 Getting Data for This Demo

    \n", "

    We have provided data for this demo on cloud storage. We have the option of either running the demo using foreign tables to access the data without using any storage on our environment or downloading the data to local storage, which may yield somewhat faster execution. However, we need to consider available storage. There are two statements in the following cell, and one is commented out. We may switch which mode we choose by changing the comment string.

    " ] }, @@ -160,7 +170,7 @@ "metadata": {}, "source": [ "
    \n", - "2. Setup API key for Google Gemini\n", + "3. Setup API key for Google Gemini\n", "

    Please enter the Google API Key, if you don't have one, please get it from here

    " ] }, @@ -181,7 +191,7 @@ "metadata": {}, "source": [ "
    \n", - "3. Define the Gemini model and Prompt\n", + "4. Define the Gemini model and Prompt\n", "

    The following section defines the type of Gemini model used. Here we use gemini-1.5-pro-latest

    " ] }, @@ -285,7 +295,7 @@ "metadata": {}, "source": [ "
    \n", - "4. Speech Recognition\n", + "5. Speech Recognition\n", "

    The following section calls Google Gemini with the above prompt and analyzes the audio file that we uploaded.

    \n", "\n", "

    Speech Recognition using Large Language Models (LLMs) is a revolutionary technology that leverages the power of artificial intelligence to accurately recognize and transcribe spoken words. By harnessing the capabilities of LLMs, this system can learn from vast amounts of data, adapt to various speech patterns, and improve its accuracy over time. With its ability to recognize speech in real-time, this technology has numerous applications, including virtual assistants, customer service chatbots, and live captioning, making it an essential tool for enhancing user experiences and improving communication.

    " @@ -322,7 +332,7 @@ "metadata": {}, "source": [ "
    \n", - "5. Cleanup\n", + "6. Cleanup\n", "\n", "

    The following code will clean up the audio files uploaded to Google Gemini.

    " ] diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb new file mode 100644 index 00000000..f186d3c4 --- /dev/null +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb @@ -0,0 +1,513 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "877a4d39-8348-4330-9121-ec41f89e0b66", + "metadata": {}, + "source": [ + "
    \n", + "

    \n", + " Topic Modelling using Vantage and Google Gemini\n", + "
    \n", + " \"Teradata\"\n", + "

    \n", + "
    " + ] + }, + { + "cell_type": "markdown", + "id": "a1cca519-4be2-49f4-85f6-c81f26a6c4fe", + "metadata": {}, + "source": [ + "

    Introduction:

    \n", + "\n", + "

    In this comprehensive user demo, we will delve into the world of topic modeling using Teradata Vantage and Google Gemini LLM model. This cutting-edge technology empowers businesses to uncover hidden insights from vast amounts of consumer complaints data, enabling them to identify trends, improve customer satisfaction, and enhance their overall brand reputation.

    \n", + "\n", + "

    Key Features:

    \n", + "\n", + "
      \n", + "
    1. Scalable Data Ingestion: Seamlessly integrate and process large volumes of consumer complaints data from various sources, including Google Gemini, into Teradata Vantage.
      2. \n", + "
    2. Advanced Topic Modelling: Utilize state-of-the-art topic modeling algorithms to identify and categorize underlying themes and sentiments within the complaints data, providing actionable insights.
      3. \n", + "
    3. Real-time Analytics: Leverage Teradata Vantage's real-time analytics capabilities to monitor and respond to emerging trends and issues in consumer complaints.
      4. \n", + "\t
    4. Customizable Dashboards: Create tailored dashboards to visualize and track key performance indicators (KPIs) and metrics specific to your business needs.
      5. \n", + "\t
    5. Integration with Google Gemini: Seamlessly integrate with Google Gemini to collect and analyze consumer complaints data from these platforms.
    \n", + "\t\n", + "

    Benefits:

    \n", + "\t\n", + "
      \n", + "
    1. Enhanced Customer Insights: Gain a deeper understanding of customer concerns and preferences, enabling data-driven decision-making.
      2. \n", + "
    2. Improved Customer Satisfaction: Identify and address recurring issues, leading to increased customer satisfaction and loyalty.
      3. \n", + "
    3. Competitive Advantage: Stay ahead of the competition by proactively addressing consumer complaints and improving brand reputation.
      4. \n", + "
    4. Cost Savings: Reduce the financial burden of handling and resolving consumer complaints by identifying and addressing root causes.
      5. \n", + "
    5. Data-Driven Decision-Making: Make informed business decisions based on actionable insights derived from topic modeling and real-time analytics.
    \n", + "\n", + "

    Steps in the analysis:

    \n", + "
      \n", + "
    1. Configuring the environment
      2. \n", + "
    2. Connect to Vantage
      3. \n", + "
    3. Setup API key for Google Gemini
      4. \n", + "
    4. Exploring the data
      5. \n", + "
    5. Topic Modelling
      6. \n", + "
    6. Cleanup
      7. \n", + "
    " + ] + }, + { + "cell_type": "markdown", + "id": "6d7d46c0-b2e2-4ffd-80ad-255bfff17086", + "metadata": {}, + "source": [ + "
    \n", + "1. Configuring the environment\n", + "\n", + "

    1.1 Install the required libraries

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef4ea764-3ec3-43bf-ae6a-528ab9fd5883", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install -r requirements.txt --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "d700c07d-c611-455f-9335-35312dbc12a2", + "metadata": {}, + "source": [ + "
    \n", + "

    Note: Please restart the kernel after executing these two lines. The simplest way to restart the Kernel is by typing zero zero: 0 0

    " + ] + }, + { + "cell_type": "markdown", + "id": "b41b93c3-f22d-4017-9e93-749f7492a6b3", + "metadata": {}, + "source": [ + "
    \n", + "\n", + "

    1.2 Import the required libraries

    \n", + "\n", + "

    Here, we import the required libraries, set environment variables and environment paths (if required).

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8906f401-d098-404d-b7c5-e6289189d1a4", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import timeit\n", + "from tqdm import tqdm\n", + "from teradataml import *\n", + "import plotly.express as px\n", + "\n", + "# GenAI\n", + "import google.generativeai as genai\n", + "from google.generativeai import protos\n", + "\n", + "\n", + "display.max_rows = 5\n", + "pd.set_option('display.max_colwidth', None)\n", + "pd.set_option('display.max_rows', 5)" + ] + }, + { + "cell_type": "markdown", + "id": "3cc65823-8d76-4ce4-90c0-7005679e8dcc", + "metadata": {}, + "source": [ + "
    \n", + "2. Connect to Vantage\n", + "

    2.1 Connect to Vantage

    \n", + "

    We will be prompted to provide the password. We will enter the password, press the Enter key, and then use the down arrow to go to the next cell.

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bec3d84c-b60d-4bd8-9d85-9a905d1c01fa", + "metadata": {}, + "outputs": [], + "source": [ + "%run -i ../startup.ipynb\n", + "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", + "print(eng)\n", + "execute_sql('''SET query_band='DEMO=Topic_Modelling.ipynb;' UPDATE FOR SESSION;''')" + ] + }, + { + "cell_type": "markdown", + "id": "cdd806c9-dcd4-4b6a-ae42-854b9cc74187", + "metadata": {}, + "source": [ + "

    Begin running steps with Shift + Enter keys.

    " + ] + }, + { + "cell_type": "markdown", + "id": "afb8acf9-78d7-4bdd-888d-9585ccce9907", + "metadata": {}, + "source": [ + "
    \n", + "\n", + "

    2.2 Getting Data for This Demo

    \n", + "

    We have provided data for this demo on cloud storage. We have the option of either running the demo using foreign tables to access the data without using any storage on our environment or downloading the data to local storage, which may yield somewhat faster execution. However, we need to consider available storage. There are two statements in the following cell, and one is commented out. We may switch which mode we choose by changing the comment string.

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99cdf312-e27c-44df-8810-3bf49c39d375", + "metadata": {}, + "outputs": [], + "source": [ + "# %run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_cloud');\" # Takes 1 minute\n", + "%run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_local');\" # Takes 2 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "d9ad01a0-8bc7-45b8-a137-11470fd4f997", + "metadata": {}, + "source": [ + "
    \n", + "3. Setup API key for Google Gemini\n", + "

    Please enter the Google API Key, if you don't have one, please get it from here

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0f2a8a6-5a5b-483d-81e7-46682c764bec", + "metadata": {}, + "outputs": [], + "source": [ + "GOOGLE_API_KEY = getpass.getpass(prompt = 'Please enter GOOGLE_API_KEY: ')\n", + "genai.configure(api_key = GOOGLE_API_KEY)" + ] + }, + { + "cell_type": "markdown", + "id": "04a3a68e-0217-47b1-8402-9670c45098bf", + "metadata": {}, + "source": [ + "
    \n", + "3.1. Define the Gemini model and Prompt\n", + "

    The following section defines the type of Gemini model used. Here we use gemini-1.5-pro-latest

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6feda0d-d9da-4e57-9fdb-00635c6bdf2a", + "metadata": {}, + "outputs": [], + "source": [ + "from google.generativeai.types import HarmCategory, HarmBlockThreshold\n", + "\n", + "model = genai.GenerativeModel(\n", + " model_name = \"models/gemini-1.5-pro-latest\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a9cabc44-d40d-4f27-ab0a-cfc09315e179", + "metadata": {}, + "source": [ + "
    \n", + "4. Exploring the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4076638-4401-4239-a379-2d7df1b209bd", + "metadata": {}, + "outputs": [], + "source": [ + "df = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Consumer_Complaints'))" + ] + }, + { + "cell_type": "markdown", + "id": "aa2e019c-086c-4574-bda8-27494c64c090", + "metadata": {}, + "source": [ + "

    Here we subset the data to get only the complaints related to Mortgage. We further analyze the issues of those complaints and pick the top 5 topics.

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c11e365c-0c34-447b-9889-8c7681790cce", + "metadata": {}, + "outputs": [], + "source": [ + "df = df[df.product == 'Mortgage']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fd59f8a-349e-4f4f-b689-534b6cb9a97f", + "metadata": {}, + "outputs": [], + "source": [ + "df.select(['issue', 'sub_issue', 'complaint_id']).groupby(['issue', 'sub_issue']).agg(['count']).sort('count_complaint_id', ascending = False)" + ] + }, + { + "cell_type": "markdown", + "id": "deeba736-584f-4fff-8439-fe934764722e", + "metadata": {}, + "source": [ + "

    According to the result above, we can classify the issues into the following topics:

    \n", + "\n", + "
    " - ] - }, - { - "cell_type": "markdown", - "id": "b836f94d-9d43-4ce8-a681-88b8298d3a02", - "metadata": {}, - "source": [ - "\n", - "
    \n", - "

    Speech Analysis with Gemini

    \n", - "

    This demo shows Speech Recognition using Teradata Vantage on Google and Google Gemini LLM model is a cutting-edge technology that leverages the power of cloud computing and advanced algorithms to accurately recognize and transcribe spoken words. This innovative solution integrates the capabilities of Teradata Vantage, Google Cloud, and Gemini to provide a robust and efficient speech recognition system.

    \n", - " \"Speech\n", - "

    Speech Analysis with Google Gemini

    \n", - "
    \n", " \n", " Open Demo\n", " \n", @@ -83,7 +64,7 @@ " \"Sentiment\n", "

    Sentiment Analysis Using Vantage and Amazon Bedrock

    \n", " \n", diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb index 70401d76..8088ad3f 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb @@ -26,7 +26,6 @@ "

    Steps in the analysis:

    \n", "
      \n", "
    1. Configuring the environment
      2. \n", - "
    2. Connect to Vantage
      3. \n", "
    3. Configuring AWS CLI
      4. \n", "
    4. Define Anthropic model and Prompt
      5. \n", "
    " @@ -104,43 +103,13 @@ "pd.set_option('display.max_colwidth', None)" ] }, - { - "cell_type": "markdown", - "id": "3fdd09db-eca3-4970-9bf5-f8d22da9d115", - "metadata": {}, - "source": [ - "
    \n", - "2. Connect to Vantage\n", - "

    We will be prompted to provide the password. We will enter the password, press the Enter key, and then use the down arrow to go to the next cell.

    " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5f75ad0d-6e49-49b6-b580-85bb00d5c608", - "metadata": {}, - "outputs": [], - "source": [ - "%run -i ../startup.ipynb\n", - "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", - "print(eng)" - ] - }, - { - "cell_type": "markdown", - "id": "b5e8fae0-5b96-41df-88a7-0f8b6a36362c", - "metadata": {}, - "source": [ - "

    Begin running steps with Shift + Enter keys.

    " - ] - }, { "cell_type": "markdown", "id": "5a57f400-6d82-4f0c-b9d5-d8048bf48a31", "metadata": {}, "source": [ "
    \n", - "

    3. Configuring AWS CLI

    \n", + "

    2. Configuring AWS CLI

    \n", "

    The following cell will prompt us for the following information:

    \n", "
      \n", "
    1. aws_access_key_id: Enter your AWS access key ID
      2. \n", @@ -198,7 +167,7 @@ "metadata": {}, "source": [ "
      \n", - "4. Define Anthropic model and Prompt\n", + "3. Define Anthropic model and Prompt\n", "

      The following section defines the type of Claude 3 model used. Here we use Claude 3 Sonnet

      \n", "\n", "\n", From 6feb7bb043d0dbcd06353f07d4a48f34678e7376 Mon Sep 17 00:00:00 2001 From: chetan-hirapara Date: Fri, 9 Aug 2024 11:08:31 +0000 Subject: [PATCH 4/7] changes in req install --- .../Complaint_Analysis_Customer360.ipynb | 15 +--- .../Complaint_Summarization.ipynb | 16 +--- .../Complaints_Classification.ipynb | 72 ++++++------------ .../Complaints_Clustering.ipynb | 16 +--- .../Sentiment_Analysis.ipynb | 8 +- .../Speech_Recognition.ipynb | 24 +++--- .../Topic_Modelling.ipynb | 21 ++--- .../audio_files/Conversation_5.mp3 | Bin 213984 -> 0 bytes .../requirements.txt | 6 +- 9 files changed, 58 insertions(+), 120 deletions(-) delete mode 100644 UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_5.mp3 diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb index 96f5db51..4313aaa5 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb @@ -53,19 +53,8 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -r requirements.txt --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61ae8bca-87dd-4771-a7c4-4f7c2c0cc641", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install pyopenssl cryptography --force-reinstall --quiet" + "%%capture\n", + "!pip install --upgrade -r requirements.txt --quiet" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb index f446dc6f..c52eca5d 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb @@ -64,19 +64,9 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -r requirements.txt --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61ae8bca-87dd-4771-a7c4-4f7c2c0cc641", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install pyopenssl cryptography --force-reinstall --quiet" + "%%capture\n", + "\n", + "!pip install --upgrade -r requirements.txt --quiet" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb index d61893b3..9b43333f 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb @@ -65,41 +65,9 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -r requirements.txt --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fd348a1-abab-410b-9365-acaec3d886e6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install pyopenssl cryptography --force-reinstall --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98837361-6976-4ccc-aaef-a432d9c73269", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install wordcloud --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e477ccac-700d-4c04-b824-c05a808b0c13", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade Pillow --quiet" + "%%capture \n", + "\n", + "!pip install --upgrade -r requirements.txt --quiet" ] }, { @@ -433,14 +401,17 @@ "# Replace 'X' with blank space\n", "modified_string = complaint_text.replace('X', '')\n", "\n", - "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "if len(modified_string):\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", - "# Display the word cloud\n", - "plt.imshow(wordcloud, interpolation='bilinear')\n", - "plt.title(\"Complaints\")\n", - "plt.tight_layout()\n", - "plt.axis(\"off\")\n", - "plt.show()" + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.title(\"Complaints\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Complaint\")" ] }, { @@ -467,14 +438,17 @@ "# Replace 'X' with blank space\n", "modified_string = non_complaint_text.replace('X', '')\n", "\n", - "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "if len(modified_string):\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", - "# Display the word cloud\n", - "plt.imshow(wordcloud, interpolation='bilinear')\n", - "plt.title(\"Non-Complaints\")\n", - "plt.tight_layout()\n", - "plt.axis(\"off\")\n", - "plt.show()" + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.title(\"Non-Complaints\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " print(\"Sorry, there are no records with Non-Complaint\")" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb index 28729a08..9cff54a7 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb @@ -62,19 +62,9 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -r requirements.txt --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "433fb7d5-d836-4ada-bad9-1fd83a57e999", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install pyopenssl cryptography --force-reinstall --quiet" + "%%capture\n", + "\n", + "!pip install --upgrade -r requirements.txt --quiet" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb index e2253e3c..b39337ee 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb @@ -70,6 +70,8 @@ "metadata": {}, "outputs": [], "source": [ + "%%capture\n", + "\n", "!pip install -r requirements.txt --quiet" ] }, @@ -82,7 +84,7 @@ }, "outputs": [], "source": [ - "!pip install pyopenssl cryptography --force-reinstall --quiet" + "# !pip install pyopenssl cryptography --force-reinstall --quiet" ] }, { @@ -94,7 +96,7 @@ }, "outputs": [], "source": [ - "!pip install wordcloud --quiet" + "# !pip install wordcloud --quiet" ] }, { @@ -104,7 +106,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install --upgrade Pillow --quiet" + "# !pip install --upgrade Pillow --quiet" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb index 8088ad3f..1b6f787e 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Speech_Recognition.ipynb @@ -47,17 +47,9 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -r requirements.txt --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c66c27fd-1cfe-4bf4-9d15-ce5b8a30a4d1", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install pyopenssl cryptography --force-reinstall --quiet" + "%%capture\n", + "\n", + "!pip install --upgrade -r requirements.txt --quiet" ] }, { @@ -201,6 +193,7 @@ "s3 = boto3.client(\"s3\")\n", "\n", "def upload_files_to_s3(mp3_files):\n", + " print('\\n Step 1. Uploading files.')\n", " for audio_file_name in mp3_files:\n", " file_path = os.path.join(os.getcwd(), \"audio_files\", audio_file_name)\n", "\n", @@ -212,6 +205,11 @@ "\n", "\n", "def retrieve_summary(active_job_dict):\n", + " print('\\n Step 3. Generating the final output, please wait..')\n", + " wait_time = 12 * len(active_job_dict)\n", + " print('wait time: ', wait_time)\n", + " time.sleep(wait_time)\n", + " print('please wait...')\n", " prefix = \"consumer_complaints/speech_analysis/processed/\"\n", "\n", " # Call the list_objects_v2 method with the Prefix parameter\n", @@ -232,7 +230,7 @@ " summaries_dict[file_name] = obj[\"Body\"].read().decode(\"utf-8\")\n", " except Exception as e:\n", " print(f\"An error occurred: {e}\")\n", - "\n", + " print('processing done..')\n", " return summaries_dict\n", "\n", "\n", @@ -269,7 +267,7 @@ "\n", " # Print the list of active jobs\n", " if len(active_jobs) > 0:\n", - " print(\"Active transcription jobs:\")\n", + " print(\"\\n Step 2. Generating the transcription. \\nActive transcription jobs:\")\n", " for job in active_jobs:\n", " _job_name = job[\"TranscriptionJobName\"]\n", " print(f\"- {_job_name} ({job['TranscriptionJobStatus']})\")\n", diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb index c8c497ee..34e59928 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb @@ -67,19 +67,9 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -r requirements.txt --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a445720-fdd9-415b-bad1-8bb87d8f7167", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install pyopenssl cryptography --force-reinstall --quiet" + "%%capture\n", + "\n", + "!pip install --upgrade -r requirements.txt --quiet" ] }, { @@ -117,7 +107,8 @@ "from langchain.llms.bedrock import Bedrock\n", "\n", "display.max_rows = 5\n", - "pd.set_option('display.max_colwidth', None)" + "pd.set_option('display.max_colwidth', None)\n", + "pd.set_option('display.max_rows', 5)" ] }, { @@ -413,7 +404,7 @@ }, "outputs": [], "source": [ - "pd_df[['complaint_id', 'consumer_complaint_narrative', 'Predicted_Topic', 'Reasoning with Chain of Thought']].head(5)" + "pd_df[['complaint_id', 'consumer_complaint_narrative', 'Predicted_Topic', 'Reasoning with Chain of Thought']]" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_5.mp3 b/UseCases/Complaints_Analysis_GenAI_Bedrock/audio_files/Conversation_5.mp3 deleted file mode 100644 index bfd75c350d61eead3aeb2f43659bbb0711bb5864..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 213984 zcmeFZbyQSgyYRgShOU{ROBw+Y0Ridm25CXMM05Z_P?Syqi2>=7?v#>}?k<%w=n$m^ 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zGJ@jQTu4Ik12FwFFg^VA{Nk_u*Mqxv_$_dBXgd5=Z{bRDk=0KuSY$3FMLRf6O!BmS zqjf0AZj-9BYjcyrizzRryiC61Ep+qKrjUPJ)7V-LT$o~_#LMj77|yim(nTHc`gTVl z4M$-9YExN|nYd`Vcg-rEKtriHX7Bu`3-~;|#UnbyTi}VLgq)b%!A{AfmCk3>uHG!% z;-~G7a$3FA+heJ5CkPg3K47R~W0dV) z_V8j&IN$TLhd0&Su~t8O`pJ)ddH&s(i)#Pev)31$cwtXiJonMk3#*7|%puON&K0=8 zcmdejrPcZTHK6mKf#wgb^Z60s13#Y}c)mB$*Aoy_NNe*U4gjSexcdpZiF5_DHXi^- C>Dqb# diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/requirements.txt b/UseCases/Complaints_Analysis_GenAI_Bedrock/requirements.txt index 585af464..e0cbdbe1 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/requirements.txt +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/requirements.txt @@ -1,4 +1,8 @@ langchain boto3 awscli -langchain_community \ No newline at end of file +langchain_community +wordcloud +pyopenssl +cryptography +Pillow \ No newline at end of file From 823be55fe68b551281e5d6d716876590574f1f55 Mon Sep 17 00:00:00 2001 From: chetan-hirapara Date: Tue, 13 Aug 2024 12:48:08 +0000 Subject: [PATCH 5/7] replaced the msg for saving results to vantage --- .../Complaint_Analysis_Customer360.ipynb | 2 +- .../Complaint_Summarization.ipynb | 2 +- .../Complaints_Classification.ipynb | 2 +- .../Complaints_Clustering.ipynb | 2 +- .../Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb | 2 +- UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Customer360.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Customer360.ipynb index 99e772c7..6dcdc0a8 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Customer360.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Analysis_Customer360.ipynb @@ -395,7 +395,7 @@ "id": "06cbb0f4-d026-4a0b-8ab8-6982b7f7777a", "metadata": {}, "source": [ - "

      Save the results back to Vantage.

      " + "

      Now the results can be saved back to Vantage.

      " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Summarization.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Summarization.ipynb index 1e36852d..086fd62d 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Summarization.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaint_Summarization.ipynb @@ -356,7 +356,7 @@ "id": "06cbb0f4-d026-4a0b-8ab8-6982b7f7777a", "metadata": {}, "source": [ - "

      Save the results back to Vantage.

      " + "

      Now the results can be saved back to Vantage.

      " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb index df966f50..41c637af 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb @@ -433,7 +433,7 @@ "id": "8522801e-48fb-4c6d-af93-3538e13ea294", "metadata": {}, "source": [ - "

      Save the results back to Vantage.

      " + "

      Now the results can be saved back to Vantage.

      " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb index 0772a035..a215cd03 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb @@ -522,7 +522,7 @@ "id": "75378143-dcba-47ba-a5a7-e434984423d7", "metadata": {}, "source": [ - "

      Save the results back to Vantage.

      " + "

      Now the results can be saved back to Vantage.

      " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb index 586541d1..4d8b7c31 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb @@ -321,7 +321,7 @@ "id": "9162a5c4-a3ee-44c0-be09-5bfaa9b5d827", "metadata": {}, "source": [ - "

      Save the results back to Vantage.

      " + "

      Now the results can be saved back to Vantage.

      " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb index f186d3c4..563991e5 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb @@ -378,7 +378,7 @@ "id": "00cb0cb4-572d-49c4-9068-c4dcfc326c7e", "metadata": {}, "source": [ - "

      Save the results back to Vantage.

      " + "

      Now the results can be saved back to Vantage.

      " ] }, { From 530c13eb0f200f9ce38bbaf44c661f2aa0a6aba3 Mon Sep 17 00:00:00 2001 From: chetan-hirapara Date: Fri, 16 Aug 2024 10:56:21 +0000 Subject: [PATCH 6/7] added msg for complaints, sentiment and added parquet for cluster --- .../Complaint_Analysis_Customer360.ipynb | 71 +- .../Complaint_Summarization.ipynb | 68 +- .../Complaints_Classification.ipynb | 72 +- .../Complaints_Clustering.ipynb | 231 +- .../Sentiment_Analysis.ipynb | 175 +- .../Topic_Modelling.ipynb | 57 +- .../data/embeddings.parquet.gzip | Bin 0 -> 4633319 bytes .../images/decision_emb_gen_cmt.svg | 4 + .../requirements.txt | 3 +- .../Complaint_Analysis_Customer360.ipynb | 98 +- .../Complaint_Summarization.ipynb | 85 +- .../Complaints_Classification.ipynb | 122 +- .../Complaints_Clustering.ipynb | 6802 ++++++++++++++++- .../Sentiment_Analysis.ipynb | 100 +- .../Topic_Modelling.ipynb | 69 +- .../data/embeddings.parquet.gzip | Bin 0 -> 290815 bytes .../images/decision_emb_gen_cmt.svg | 4 + .../requirements.txt | 3 +- 18 files changed, 7269 insertions(+), 695 deletions(-) create mode 100644 UseCases/Complaints_Analysis_GenAI_Bedrock/data/embeddings.parquet.gzip create mode 100644 UseCases/Complaints_Analysis_GenAI_Bedrock/images/decision_emb_gen_cmt.svg create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/data/embeddings.parquet.gzip create mode 100644 UseCases/Complaints_Analysis_GenAI_Gemini/images/decision_emb_gen_cmt.svg diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb index 4313aaa5..0334ef96 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Analysis_Customer360.ipynb @@ -307,20 +307,23 @@ "source": [ "# Sentiment\n", "for i in tqdm(range(len(pd_df))):\n", - " prompt = f'''\n", - " User prompt: \n", - " The following is text from a review:\n", + " try:\n", + " prompt = f'''\n", + " User prompt: \n", + " The following is text from a review:\n", "\n", - " “{pd_df['consumer_complaint_narrative'][i]}”\n", + " “{pd_df['consumer_complaint_narrative'][i]}”\n", "\n", - " Categorize the review as one of the following:\n", + " Categorize the review as one of the following:\n", "\n", - " Positive\n", - " Negative\n", - " Neutral\n", - " '''\n", + " Positive\n", + " Negative\n", + " Neutral\n", + " '''\n", "\n", - " pd_df['Sentiment'][i] = ai21(prompt = prompt)" + " pd_df['Sentiment'][i] = ai21(prompt = prompt)\n", + " except:\n", + " pass" ] }, { @@ -332,23 +335,26 @@ "source": [ "# Topic\n", "for i in tqdm(range(len(pd_df))):\n", - " prompt = f'''\n", - " User prompt:\n", - " The following is text from a complaint:\n", + " try:\n", + " prompt = f'''\n", + " User prompt:\n", + " The following is text from a complaint:\n", + "\n", + " “{pd_df['consumer_complaint_narrative'][i]}”\n", "\n", - " “{pd_df['consumer_complaint_narrative'][i]}”\n", + " Identify the topic of the complaint and categorize into one of the following topics. Only output one of the following options:\n", "\n", - " Identify the topic of the complaint and categorize into one of the following topics. Only output one of the following options:\n", + " - Mortgage Application\n", + " - Payment Trouble\n", + " - Mortgage Closing\n", + " - Report Inaccuracy\n", + " - Payment Struggle\n", "\n", - " - Mortgage Application\n", - " - Payment Trouble\n", - " - Mortgage Closing\n", - " - Report Inaccuracy\n", - " - Payment Struggle\n", - " \n", - " Return just one of the above options\n", - "'''\n", - " pd_df['Topic'][i] = ai21(prompt = prompt)" + " Return just one of the above options\n", + " '''\n", + " pd_df['Topic'][i] = ai21(prompt = prompt)\n", + " except:\n", + " pass" ] }, { @@ -360,13 +366,16 @@ "source": [ "# Summary\n", "for i in tqdm(range(len(pd_df))):\n", - " prompt = f'''\n", - " The following is text from a Bank Review:\n", - " “{pd_df['consumer_complaint_narrative'][i]}”\n", - " Summarize the Bank Review in one sentence\n", - " '''\n", + " try:\n", + " prompt = f'''\n", + " The following is text from a Bank Review:\n", + " “{pd_df['consumer_complaint_narrative'][i]}”\n", + " Summarize the Bank Review in one sentence\n", + " '''\n", "\n", - " pd_df['Summary'][i] = ai21(prompt = prompt)" + " pd_df['Summary'][i] = ai21(prompt = prompt)\n", + " except:\n", + " pass" ] }, { @@ -424,7 +433,7 @@ "id": "06cbb0f4-d026-4a0b-8ab8-6982b7f7777a", "metadata": {}, "source": [ - "

      Save the results back to Vantage.

      " + "

      Now the results can be saved back to Vantage.

      " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb index c52eca5d..616d7b40 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaint_Summarization.ipynb @@ -125,6 +125,7 @@ "outputs": [], "source": [ "%run -i ../startup.ipynb\n", + "\n", "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", "print(eng)\n", "execute_sql('''SET query_band='DEMO=Complaint_Summarization.ipynb;' UPDATE FOR SESSION;''')" @@ -274,7 +275,7 @@ "metadata": {}, "outputs": [], "source": [ - "df = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Consumer_Complaints')).to_pandas(num_rows = 200)\n", + "df = DataFrame(in_schema('DEMO_ComplaintAnalysis', 'Consumer_Complaints')).to_pandas(num_rows = 20)\n", "df['Summary'] = \"\"\n", "df['Reasoning with Chain of Thought'] = \"\"" ] @@ -287,33 +288,36 @@ "outputs": [], "source": [ "for i in tqdm(range(len(df))):\n", - " prompt = f'''\n", - " The following is text from a Bank Review:\n", - " \n", - " “{df['consumer_complaint_narrative'][i]}”\n", - " \n", - " Give me reasoning as well as summary for this review.\n", - " \n", - " Instructions for Reasoning:\n", - " - Give me Reasoning in short\n", - " - Only one sentence reasoning\n", - " Instructions for Summary:\n", - " - A short one sentence Summary of everything the review states.\n", - " \n", - " My output comes in the format:\n", - " Summary: ,\n", - " Reasoning: ,\n", - " '''\n", + " try:\n", + " prompt = f'''\n", + " The following is text from a Bank Review:\n", + "\n", + " “{df['consumer_complaint_narrative'][i]}”\n", + "\n", + " Give me reasoning as well as summary for this review.\n", "\n", - " output = mistral(prompt = prompt)\n", - " summary = re.search('Summary:(.*)', output).group(1)\n", - " if summary == \"\":\n", - " summary = re.search('Summary:\\n(.*)', output).group(1)\n", - " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", - " if reasoning == \"\":\n", - " reasoning = re.search('Reasoning:\\n(.*)', output).group(1)\n", - " df['Summary'][i] = summary\n", - " df['Reasoning with Chain of Thought'][i] = reasoning" + " Instructions for Reasoning:\n", + " - Give me Reasoning in short\n", + " - Only one sentence reasoning\n", + " Instructions for Summary:\n", + " - A short one sentence Summary of everything the review states.\n", + "\n", + " My output comes in the format:\n", + " Summary: ,\n", + " Reasoning: ,\n", + " '''\n", + "\n", + " output = mistral(prompt = prompt)\n", + " summary = re.search('Summary:(.*)', output).group(1)\n", + " if summary == \"\":\n", + " summary = re.search('Summary:\\n(.*)', output).group(1)\n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " if reasoning == \"\":\n", + " reasoning = re.search('Reasoning:\\n(.*)', output).group(1)\n", + " df['Summary'][i] = summary\n", + " df['Reasoning with Chain of Thought'][i] = reasoning\n", + " except:\n", + " pass" ] }, { @@ -380,7 +384,7 @@ "id": "06cbb0f4-d026-4a0b-8ab8-6982b7f7777a", "metadata": {}, "source": [ - "

      Save the results back to Vantage.

      " + "

      Now the results can be saved back to Vantage.

      " ] }, { @@ -457,14 +461,6 @@ "
    \n", "" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff3c6ae7-6de8-4d81-9aba-e1cd28fc7dd7", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb index 9b43333f..cb8c6800 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Classification.ipynb @@ -108,7 +108,8 @@ "from langchain.llms.bedrock import Bedrock\n", "\n", "display.max_rows = 5\n", - "pd.set_option('display.max_colwidth', None)" + "pd.set_option('display.max_colwidth', None)\n", + "from IPython.display import display, Markdown" ] }, { @@ -295,30 +296,33 @@ "df['Reasoning with Chain of Thought'] = \"\"\n", "\n", "for i in tqdm(range(len(df))):\n", - " prompt = f'''\n", - " User prompt:\n", - " The following is text from a review:\n", + " try:\n", + " prompt = f'''\n", + " User prompt:\n", + " The following is text from a review:\n", "\n", - " “{df['consumer_complaint_narrative'][i]}”\n", + " “{df['consumer_complaint_narrative'][i]}”\n", "\n", - " Give me reasoning as well as Category for this review\n", - " \n", - " Instructions for Reasoning:\n", - " - Give me Reasoning in detail\n", - " - Only one sentence reasoning\n", - " Instructions for Category:\n", - " - The review falls into one of the following categories: Complaint, Non-Complaint\n", - " - Select one category from the given ones\n", - " \n", - " My output comes in the format:\n", - " Category: \n", - " Reasoning: \n", - " '''\n", - " output = mistral(prompt = prompt)\n", - " category = re.search('Category:(.*)', output).group(1)\n", - " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", - " df['Prediction'][i] = category\n", - " df['Reasoning with Chain of Thought'][i] = reasoning" + " Give me reasoning as well as Category for this review\n", + "\n", + " Instructions for Reasoning:\n", + " - Give me Reasoning in detail\n", + " - Only one sentence reasoning\n", + " Instructions for Category:\n", + " - The review falls into one of the following categories: Complaint, Non-Complaint\n", + " - Select one category from the given ones\n", + "\n", + " My output comes in the format:\n", + " Category: \n", + " Reasoning: \n", + " '''\n", + " output = mistral(prompt = prompt)\n", + " category = re.search('Category:(.*)', output).group(1)\n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " df['Prediction'][i] = category\n", + " df['Reasoning with Chain of Thought'][i] = reasoning\n", + " except:\n", + " pass" ] }, { @@ -388,6 +392,20 @@ "

    A visual representation of consumer complaints prediction, highlighting the most frequent words and pain points in customer feedback. This word cloud helps identify trends, sentiment, and areas for improvement, enabling data-driven decision making.

    " ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "f02377c3-4b0d-486a-b0c1-0deeb778de01", + "metadata": {}, + "outputs": [], + "source": [ + "def display_helper(msg):\n", + " return display(Markdown(\n", + " f\"\"\"
    \n", + "

    Note: \n", + " {msg}

    \"\"\"))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -411,7 +429,8 @@ " plt.axis(\"off\")\n", " plt.show()\n", "else:\n", - " print(\"Sorry, there are no records with Complaint\")" + " display_helper(\"\"\"We included complaint and non-complaint options for completeness. \n", + " It's possible that our sample dataset doesn't contain any actual complaints.\"\"\")" ] }, { @@ -448,7 +467,8 @@ " plt.axis(\"off\")\n", " plt.show()\n", "else:\n", - " print(\"Sorry, there are no records with Non-Complaint\")" + " display_helper(\"\"\"We included both complaint and non-complaint options for completeness. \n", + " But since this is a complaints dataset, we don't expect to see any non-complaints.\"\"\")" ] }, { @@ -456,7 +476,7 @@ "id": "8522801e-48fb-4c6d-af93-3538e13ea294", "metadata": {}, "source": [ - "

    Save the results back to Vantage.

    " + "

    Now the results can be saved back to Vantage.

    " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb index 9cff54a7..643b95fe 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Complaints_Clustering.ipynb @@ -132,7 +132,8 @@ "display.max_rows = 5\n", "\n", "display.print_sqlmr_query = False\n", - "display.suppress_vantage_runtime_warnings = True" + "display.suppress_vantage_runtime_warnings = True\n", + "from IPython.display import display, Markdown" ] }, { @@ -499,163 +500,157 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "81fc5101-9387-4846-ac0e-23f23495502c", + "cell_type": "markdown", + "id": "d2a7f7a3-8372-42c9-a4e5-16b8dd0b4e38", "metadata": {}, - "outputs": [], "source": [ - "## Bedrock Clients\n", - "bedrock=boto3.client(service_name=\"bedrock-runtime\", region_name=\"us-east-1\")\n", - "bedrock_embeddings=BedrockEmbeddings(model_id=\"amazon.titan-embed-text-v1\",client=bedrock)\n", + "
    \n", "\n", - "def get_embeddings_bedrock(df, txt_col_name):\n", - " \n", - " # This may take a few minutes\n", - " df['embeddings'] = df[txt_col_name].apply(lambda x: bedrock_embeddings.embed_query(x))\n", - " \n", - " # Generate all the embeddings columns from the \"embeddings\" column.\n", - " for i in range(1536):\n", - " df[\"embeddings_{}\".format(i)] = df[\"embeddings\"].apply(lambda x: x[i])\n", - " \n", - " # drop embedding \n", - " df = df.drop(\"embeddings\", axis=1)\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7be29ef-8877-40a9-9c83-bf7784bc0aa8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "embedded_df = get_embeddings_bedrock(df.to_pandas(), 'consumer_complaint_narrative')\n", - "copy_to_sql(embedded_df, table_name ='complaints_embeddings', if_exists = 'replace')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "141d4c41-6cad-4491-84f9-7d37e18444e1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "embedded_df = DataFrame('complaints_embeddings')\n", - "embedded_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9b99852-9bc1-4ae5-b98c-21b1096fc9c8", - "metadata": {}, - "outputs": [], - "source": [ - "cols = list(embedded_df.columns[:17]) + ['consumer_complaint_narrative']\n", - "embedded_df = embedded_df.drop(columns=cols)\n", - "copy_to_sql(df = embedded_df, table_name = 'kmeans_features', if_exists = 'replace')" + "

    4.1 Do you want to generate the embeddings?

    \n", + "

    We have already generated embeddings for the comment text and stored them in Vantage table.

    \n", + "\n", + "
    \"embeddings_decision\"
    \n", + "\n", + "
    \n", + "

    Note: If you would like to skip the embedding generation step to save the time and move quickly to next step, please enter \"No\" in the next prompt.

    \n", + "
    \n", + "\n", + "

    To save time, you can move to the already generated embeddings section. However, if you would like to see how we generate the embeddings, or if you need to generate the embeddings for a different dataset, then continue to the following section.

    " ] }, { "cell_type": "code", "execution_count": null, - "id": "a5e1fa67-cd5c-4503-961c-a53d79159c4a", + "id": "99a55e3a-9510-4895-a501-9c60775d3ee8", "metadata": {}, "outputs": [], "source": [ - "no_clusters = 5\n", + "# Request user's input\n", + "generate = input(\"Do you want to generate embeddings? ('yes'/'no'): \")\n", + "\n", + "# Check the user's input\n", + "if generate.lower() == 'yes':\n", + " print(\"\\nGreat! We'll start by generating embeddings.\")\n", + "\n", + " print(\"\\nGenerating embeddings, please wait...\")\n", + " \n", + " display(Markdown(\"\"\"
    \n", + "

    Note: The embedding generation step is estimated to take approximately 5 minutes to complete. Please be patient.

    \n", + "
    \"\"\"))\n", + " \n", + " ## Bedrock Clients\n", + " bedrock=boto3.client(service_name=\"bedrock-runtime\", region_name=\"us-east-1\")\n", + " bedrock_embeddings=BedrockEmbeddings(model_id=\"amazon.titan-embed-text-v1\",client=bedrock)\n", + "\n", + " def get_embeddings_bedrock(df, txt_col_name):\n", "\n", - "# drop existing model\n", - "try:\n", - " db_drop_table(table_name = \"KMeans_Model\")\n", - "except:\n", - " pass\n", + " # This may take a few minutes\n", + " df['embeddings'] = df[txt_col_name].apply(lambda x: bedrock_embeddings.embed_query(x))\n", "\n", - "# Model Table\n", - "query = f'''\n", - " SELECT * from TD_KMeans (\n", - " ON kmeans_features as InputTable\n", - " OUT TABLE ModelTable(KMeans_Model)\n", - " USING\n", - " IdColumn('complaint_id')\n", - " TargetColumns{tuple(embedded_df.columns[19:])}\n", - " NumClusters({no_clusters})\n", - " OutputClusterAssignment('true')\n", - ") AS dt;\n", - "'''\n", + " # Generate all the embeddings columns from the \"embeddings\" column.\n", + " for i in range(1536):\n", + " df[\"embeddings_{}\".format(i)] = df[\"embeddings\"].apply(lambda x: x[i])\n", "\n", - "execute_sql(query)" + " # drop embedding \n", + " df = df.drop(\"embeddings\", axis=1)\n", + " return df\n", + "\n", + " # API request for embeddings\n", + " embedding_df = get_embeddings_bedrock(df.iloc[:1000].to_pandas(), 'consumer_complaint_narrative')\n", + "\n", + " # Save embeddings to SQL\n", + " print(\"\\nSaving embeddings to the database...\")\n", + " copy_to_sql(\n", + " df = embedding_df,\n", + " table_name = 'complaints_embeddings',\n", + " if_exists = 'replace'\n", + " )\n", + "\n", + " print(\"\\nEmbeddings generated and saved successfully!\")\n", + "\n", + "elif generate.lower() == 'no':\n", + " print(\"\\nLoading embeddings from the local file 'embeddings.parquet.gzip'.\")\n", + " \n", + " display(Markdown(\"\"\"

    The code above first reads the data from the files. The files contain information about the complaints embeddings. The code then loads the data into a permanent table in SQL. Once the data is loaded, we will use the Vantage in-database function KMeans to find the clusters from the embeddings. The data contains complaints embeddings, which are lists of numerical values, or vectors.

    \n", + "

    The embeddings file contains 1000 records, each with 1536 numerical features. This means that the file is quite large and it may take some time to load it into SQL.

    \n", + "
    \n", + "

    Note: Please be patient. The code above is loading data from files and copying it to SQL. This process may take 10-15 seconds.

    \n", + "
    \"\"\"))\n", + " # Save them to SQL\n", + " copy_to_sql(\n", + " df = pd.read_parquet('./data/embeddings.parquet.gzip'),\n", + " table_name = 'complaints_embeddings',\n", + " if_exists = 'replace'\n", + " )\n", + "\n", + " print(\"\\nEmbeddings loaded and saved successfully!\")\n", + "\n", + "else:\n", + " print(\"\\nInvalid input. Please enter 'yes' or 'no' to proceed.\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "63a55e88-1632-46eb-b86c-3ee3d16edd8a", + "id": "02cd8ce7-6d34-4a1b-b350-1aeef696a6ec", "metadata": {}, "outputs": [], "source": [ - "KMeans_Model = DataFrame(\"KMeans_Model\")\n", - "print(\"Data information: \\n\", KMeans_Model.shape)" + "data_cols = DataFrame('complaints_embeddings').iloc[:, 17].columns + DataFrame('complaints_embeddings').iloc[:, 19:].columns\n", + "KMeans_Model = KMeans(\n", + " data = DataFrame('complaints_embeddings')[data_cols],\n", + " id_column = \"complaint_id\",\n", + " target_columns = DataFrame('complaints_embeddings').columns[21:],\n", + " output_cluster_assignment = True,\n", + " num_clusters = 5\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "55d1616f-da1a-4b6e-9824-1262d2eb0d70", + "id": "a578dc1d-4f92-4eab-a991-9402705f7d3a", "metadata": {}, "outputs": [], "source": [ - "no_clusters = 5\n", - "\n", - "# for Cluster info, used later for filtering\n", - "query = f'''\n", - " SELECT * from TD_KMeans (\n", - " ON kmeans_features as InputTable\n", - " USING\n", - " IdColumn('complaint_id')\n", - " TargetColumns{tuple(embedded_df.columns[19:])}\n", - " NumClusters({no_clusters})\n", - " OutputClusterAssignment('true')\n", - ") AS dt;\n", - "'''\n", - "\n", - "kmeans_out_sql = DataFrame.from_query(query)" + "print(\"Data information: \\n\", KMeans_Model.model_data.shape)" ] }, { "cell_type": "code", "execution_count": null, - "id": "7d5a3743-a51a-46cd-888c-eecd85e3f3ab", + "id": "b3ecce38-ffe3-47c8-a57c-ff0e864f831e", "metadata": {}, "outputs": [], "source": [ - "kmeans_out_sql" + "KMeans_Model.result" ] }, { "cell_type": "code", "execution_count": null, - "id": "7cd95cbc-7434-4248-84f8-20260410bd7e", + "id": "89788c7e-cba9-4f49-849f-e09df207f5b0", "metadata": {}, "outputs": [], "source": [ - "embeddings_cluster = DataFrame('complaints_embeddings').join(other=kmeans_out_sql, how=\"full\", on=\"complaint_id=complaint_id\", lprefix= \"L_\")" + "embeddings_cluster = DataFrame('complaints_embeddings').join(\n", + " other = KMeans_Model.result,\n", + " how = \"inner\",\n", + " on = \"complaint_id=complaint_id\",\n", + " lprefix = \"L_\"\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "id": "94563f0e-6d80-4b73-ac5a-8aa787ddfc9b", + "id": "b302ab51-3118-42de-bcde-8d686aca8c86", "metadata": {}, "outputs": [], "source": [ "# View complaints in cluster 1\n", - "embeddings_cluster[['td_clusterid_kmeans','complaint_id','consumer_complaint_narrative']].loc[embeddings_cluster.td_clusterid_kmeans==1]" + "embeddings_cluster[['td_clusterid_kmeans','complaint_id','consumer_complaint_narrative']] \\\n", + " .loc[embeddings_cluster.td_clusterid_kmeans == 1]" ] }, { @@ -765,13 +760,39 @@ "5. Cleanup" ] }, + { + "cell_type": "markdown", + "id": "2865e766-d244-4821-a3cf-5141986ed5f4", + "metadata": {}, + "source": [ + "
    \n", + "

    5.1 Work Tables

    \n", + "

    We need to clean up our work tables to prevent errors next time.

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e306a46e-c002-41ec-aadc-5d21ba2a4b4c", + "metadata": {}, + "outputs": [], + "source": [ + "for table in db_list_tables()['TableName'].tolist():\n", + " try:\n", + " db_drop_table(table_name=table, schema_name=\"demo_user\")\n", + " except:\n", + " pass" + ] + }, { "cell_type": "markdown", "id": "73a8ead8-0698-4449-bc11-5a07351e3fb6", "metadata": {}, "source": [ - "

    Databases and Tables

    \n", - "

    The following code will clean up tables and databases created above.

    " + "
    \n", + "\n", + "

    5.2 Databases and Tables

    \n", + "

    We will use the following code to clean up tables and databases created for this demonstration.

    " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb index b39337ee..60fac93c 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Sentiment_Analysis.ipynb @@ -75,40 +75,6 @@ "!pip install -r requirements.txt --quiet" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "dfb0f5b6-bf14-477f-8c26-e2652c3f4049", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# !pip install pyopenssl cryptography --force-reinstall --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98837361-6976-4ccc-aaef-a432d9c73269", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# !pip install wordcloud --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e477ccac-700d-4c04-b824-c05a808b0c13", - "metadata": {}, - "outputs": [], - "source": [ - "# !pip install --upgrade Pillow --quiet" - ] - }, { "cell_type": "markdown", "id": "c1fd81e4-0df9-4360-b3ac-72214f135296", @@ -147,7 +113,8 @@ "import plotly.subplots as subplots\n", "from langchain.llms.bedrock import Bedrock\n", "\n", - "display.max_rows = 5" + "display.max_rows = 5\n", + "from IPython.display import display, Markdown" ] }, { @@ -331,7 +298,7 @@ }, "outputs": [], "source": [ - "df = tdf.to_pandas()\n", + "df = tdf.to_pandas(num_rows=10)\n", "df['Sentiment'] = \"\"" ] }, @@ -343,20 +310,23 @@ "outputs": [], "source": [ "for i in tqdm(range(len(df))):\n", - " prompt = f'''\n", - " User prompt: \n", - " The following is text from a review:\n", + " try:\n", + " prompt = f'''\n", + " User prompt: \n", + " The following is text from a review:\n", "\n", - " “{df['consumer_complaint_narrative'][i]}”\n", + " “{df['consumer_complaint_narrative'][i]}”\n", "\n", - " Categorize the review as one of the following:\n", + " Categorize the review as one of the following:\n", "\n", - " Positive\n", - " Negative\n", - " Neutral\n", - " '''\n", + " Positive\n", + " Negative\n", + " Neutral\n", + " '''\n", "\n", - " df['Sentiment'][i] = ai21(prompt = prompt)" + " df['Sentiment'][i] = ai21(prompt = prompt)\n", + " except:\n", + " pass" ] }, { @@ -384,7 +354,7 @@ "id": "9162a5c4-a3ee-44c0-be09-5bfaa9b5d827", "metadata": {}, "source": [ - "

    Save the results back to Vantage.

    " + "

    Now the results can be saved back to Vantage.

    " ] }, { @@ -459,6 +429,20 @@ "

    Unlock the power of customer feedback with our intuitive word cloud visualization, which provides a comprehensive snapshot of negative consumer complaints sentiment. This innovative tool highlights the most frequently occurring words and pain points in customer feedback, empowering businesses to:

    1. Identify trends and sentiment patterns
    2. Pinpoint areas for improvement
    3. Make data-driven decisions to enhance customer satisfaction and loyalty

    By leveraging this word cloud, businesses can proactively address customer concerns, refine their products and services, and ultimately drive growth through a deeper understanding of their customers' needs and preferences.

    " ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "16c94464-2aa0-4220-9f50-0969ba983f93", + "metadata": {}, + "outputs": [], + "source": [ + "def display_helper(msg):\n", + " return display(Markdown(\n", + " f\"\"\"
    \n", + "

    Note: \n", + " {msg}

    \"\"\"))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -472,13 +456,17 @@ "# Replace 'X' with blank space\n", "modified_string = neg_text.replace('X', '')\n", "\n", - "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "if len(modified_string):\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", - "# Display the word cloud\n", - "plt.imshow(wordcloud, interpolation='bilinear')\n", - "plt.tight_layout()\n", - "plt.axis(\"off\")\n", - "plt.show()" + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " display_helper(\"\"\"We included positive, negative, and neutral categories to cover all bases. \n", + " But in this sample, it's possible that none of the complaints are actually negative.\"\"\")" ] }, { @@ -505,13 +493,17 @@ "# Replace 'X' with blank space\n", "modified_string = neu_text.replace('X', '')\n", "\n", - "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "if len(modified_string):\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", - "# Display the word cloud\n", - "plt.imshow(wordcloud, interpolation='bilinear')\n", - "plt.tight_layout()\n", - "plt.axis(\"off\")\n", - "plt.show()" + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " display_helper(\"\"\"To cover all possible scenarios, we included positive, negative, and neutral categories in our analysis. \n", + " However, given that this dataset consists of complaints, it's expected that the model would rarely, if ever, encounter neutral responses.\"\"\")" ] }, { @@ -538,13 +530,18 @@ "# Replace 'X' with blank space\n", "modified_string = pos_text.replace('X', '')\n", "\n", - "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "if len(modified_string):\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", - "# Display the word cloud\n", - "plt.imshow(wordcloud, interpolation='bilinear')\n", - "plt.tight_layout()\n", - "plt.axis(\"off\")\n", - "plt.show()" + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " display_helper(\"\"\"To cover all possible scenarios, we included positive, negative, and neutral categories in our analysis. \n", + " However, given that this dataset consists of complaints, it's expected that the model would rarely, \n", + " if ever, encounter positive responses.\"\"\")" ] }, { @@ -614,14 +611,18 @@ "\n", "viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", "\n", - "# Sorting the DataFrame by year for each product\n", - "pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", + "if viz_senti.shape[0] > 0:\n", + " # Sorting the DataFrame by year for each product\n", + " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", "\n", - "# Plotting using Plotly\n", - "fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Negative Sentiment per Product Over Years')\n", - "fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", + " # Plotting using Plotly\n", + " fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Negative Sentiment per Product Over Years')\n", + " fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", "\n", - "fig.show()" + " fig.show()\n", + "else:\n", + " display_helper(\"\"\"We included positive, negative, and neutral categories to cover all bases. \n", + " But in this sample, it's possible that none of the complaints are actually negative.\"\"\")" ] }, { @@ -646,14 +647,18 @@ "\n", "viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", "\n", - "# Sorting the DataFrame by year for each product\n", - "pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", + "if viz_senti.shape[0] > 0:\n", + " # Sorting the DataFrame by year for each product\n", + " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", "\n", - "# Plotting using Plotly\n", - "fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Neutral Sentiment per Product Over Years')\n", - "fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", + " # Plotting using Plotly\n", + " fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Neutral Sentiment per Product Over Years')\n", + " fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", "\n", - "fig.show()" + " fig.show()\n", + "else:\n", + " display_helper(\"\"\"To cover all possible scenarios, we included positive, negative, and neutral categories in our analysis. \n", + " However, given that this dataset consists of complaints, it's expected that the model would rarely, if ever, encounter neutral responses.\"\"\")" ] }, { @@ -678,14 +683,18 @@ "\n", "viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", "\n", - "# Sorting the DataFrame by year for each product\n", - "pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", + "if viz_senti.shape[0] > 0:\n", + " # Sorting the DataFrame by year for each product\n", + " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", "\n", - "# Plotting using Plotly\n", - "fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Positive Sentiment per Product Over Years')\n", - "fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", + " # Plotting using Plotly\n", + " fig = px.line(pd_df_sorted, x='year', y='sum_Sentiment', color='product', markers=True, title='Positive Sentiment per Product Over Years')\n", + " fig.update_layout(xaxis_title='Year', yaxis_title='Count', legend_title='Product', width=1000, height=600)\n", "\n", - "fig.show()" + " fig.show()\n", + "else:\n", + " display_helper(\"\"\"To cover all possible scenarios, we included positive, negative, and neutral categories in our analysis. \n", + " However, given that this dataset consists of complaints, it's expected that the model would rarely, if ever, encounter positive responses.\"\"\")" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb index 34e59928..19420ea1 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb @@ -342,16 +342,6 @@ "

    Reasoning with a Chain of Thought: Imagine you're trying to solve a problem. With a large language model, you start with an initial idea or question. Then, you use the model's capabilities to explore related concepts, gradually connecting them together. Each step builds upon the previous one, leading you closer to understanding or solving the problem. It's like putting together puzzle pieces, one by one, until you see the whole picture.

    " ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3a61c36-941f-477a-bc7a-074c3a03765a", - "metadata": {}, - "outputs": [], - "source": [ - "pd_df = df.to_pandas()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -359,6 +349,7 @@ "metadata": {}, "outputs": [], "source": [ + "pd_df = df.to_pandas()\n", "pd_df['Predicted_Topic'] = ''\n", "pd_df['Reasoning with Chain of Thought'] = ''" ] @@ -371,28 +362,30 @@ "outputs": [], "source": [ "for i in tqdm(range(len(pd_df))):\n", - " prompt = f'''\n", - " The following is text from a complaint:\n", - " \n", - " “{pd_df['consumer_complaint_narrative'][i]}”\n", - " \n", - " Give me reasoning as well as topic for this complaint\n", - " Instructions for Reasoning:\n", - " - Give me Reasoning in detail\n", - " - Only one sentence reasoning\n", - " Instructions for Topic:\n", - " - The complaint falls into only one of the following Topics: Mortgage Application, Payment Trouble, Mortgage Closing, Report Inaccuracy, Payment Struggle\n", - " - Only select one Topic\n", + " try:\n", + " prompt = f'''\n", + " The following is text from a complaint:\n", + "\n", + " “{pd_df['consumer_complaint_narrative'][i]}”\n", + "\n", + " Give me reasoning as well as topic for this complaint\n", + " Instructions for Reasoning:\n", + " - Give me Reasoning in detail\n", + " - Only one sentence reasoning\n", + " Instructions for Topic:\n", + " - The complaint falls into only one of the following Topics: Mortgage Application, Payment Trouble, Mortgage Closing, Report Inaccuracy, Payment Struggle\n", + " - Only select one Topic\n", "\n", - " My output comes in the format:\n", - " Topic: , \n", - " Reasoning: ,\n", - "'''\n", - " output = ai21(prompt = prompt)\n", - " topic = re.search('Topic:(.*)', output).group(1)\n", - " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", - " pd_df['Predicted_Topic'][i] = topic\n", - " pd_df['Reasoning with Chain of Thought'][i] = reasoning" + " My output comes in the format:\n", + " Topic: , \n", + " Reasoning:'''\n", + " output = ai21(prompt = prompt)\n", + " topic = re.search('Topic:(.*)', output).group(1)\n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " pd_df['Predicted_Topic'][i] = topic\n", + " pd_df['Reasoning with Chain of Thought'][i] = reasoning\n", + " except exception as e:\n", + " pass" ] }, { @@ -412,7 +405,7 @@ "id": "00cb0cb4-572d-49c4-9068-c4dcfc326c7e", "metadata": {}, "source": [ - "

    Save the results back to Vantage.

    " + "

    Now the results can be saved back to Vantage.

    " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/data/embeddings.parquet.gzip b/UseCases/Complaints_Analysis_GenAI_Bedrock/data/embeddings.parquet.gzip new file mode 100644 index 0000000000000000000000000000000000000000..5af84ca2af9168a15da31ba7f4aeb1c13c28eb0f GIT binary patch literal 4633319 zcmZ^JV{k4!v~`VBpW1FuZTpm`wrxGNZQD<6+qP}nw)wsH{=Q#klF81GWKA+z*?VQn z3n?<8gP?z}gQD+Z(xT5I{bxX-!+?MgM*i3QPx-mZkg*}-l~#{z^HX8i!ue8t0HBT(q&+ ztn>jsO?=XujmjKSJD_u6wmm0kYn||$ePrW~A{;V1glvk~L}ZR=cfM1t#M6wn{WmBt zApXU#cjQV>887_BZ}yliyww%1U8>hs)SP8CYOe$30LWs|we2QbiN6>Pwx8DO_qnUf zI`HY^|EOFY(3U0Ps6H~xc}<=e^`2UuU)kJo4ADkuqjfNV)arcaQxmc2CQ}ogZgF%I z@LGQE&=yAgU@1UM5utw!e^)sR<&qjT$N}n1qQ^J<5Us@Hl7zklmTYC_A=;3fi~>ie zQvU<(p2gDnv+w730UX3{~*HmI1|W) zX?j`QBQxSj`&ij<)-Mu`cX0;TxMXZ#3}vjl&!fqnssn^PS?gUmF!Nk<(LIl@p5(B> zO~NIrl=a>#-GwZkt^tI!xZB< zXsrgw1m4up22cPnKX5GhWc{ymuwtL>o@O%A+8=B-Svo*RygO-kT7zg8qurn;8rc2md&)Duk-m*gZ4l??DKMJ_^2S)pB+slP!hL#PPh 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    \n", "" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff3c6ae7-6de8-4d81-9aba-e1cd28fc7dd7", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb index 41c637af..b7671145 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Classification.ipynb @@ -68,19 +68,11 @@ "metadata": {}, "outputs": [], "source": [ + "%%capture\n", + "\n", "!pip install -r requirements.txt --quiet" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "e477ccac-700d-4c04-b824-c05a808b0c13", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install --upgrade Pillow --quiet" - ] - }, { "cell_type": "markdown", "id": "3e8d47d7-1a44-4da1-afa5-46ce90312021", @@ -127,7 +119,8 @@ "\n", "display.max_rows = 5\n", "pd.set_option('display.max_colwidth', 50)\n", - "pd.set_option('display.max_rows', 5)" + "pd.set_option('display.max_rows', 5)\n", + "from IPython.display import display, Markdown" ] }, { @@ -266,40 +259,43 @@ "df['Reasoning with Chain of Thought'] = \"\"\n", "\n", "for i in tqdm(range(len(df))):\n", - " prompt = f'''\n", - " User prompt:\n", - " The following is text from a review:\n", - "\n", - " “{df['consumer_complaint_narrative'][i]}”\n", - "\n", - " Give me reasoning as well as Category for this review\n", - " \n", - " Instructions for Reasoning:\n", - " - Give me Reasoning in detail\n", - " - Only one sentence reasoning\n", - " Instructions for Category:\n", - " - The review falls into one of the following categories: Complaint, Non-Complaint\n", - " - Select one category from the given ones\n", - " - Important: Do not add any formating into the output. For example ** Complaint or **Complaint** refrain from such format\n", - " \n", - " Final output comes in the format:\n", - " Category: \n", - " Reasoning: \n", - " '''\n", - " output = model.generate_content([prompt])\n", - " finish_reason = output.candidates[0].finish_reason\n", - " \n", - " if finish_reason == protos.Candidate.FinishReason.STOP:\n", - " output = output.candidates[0].content.parts[0].text\n", - " # apply regex\n", - " category = re.search('Category:(.*)', output).group(1)\n", - " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", - " else:\n", - " category = \"Non-Complaint\"\n", - " reasoning = \"\"\n", - " \n", - " df['Prediction'][i] = category\n", - " df['Reasoning with Chain of Thought'][i] = reasoning" + " try:\n", + " prompt = f'''\n", + " User prompt:\n", + " The following is text from a review:\n", + "\n", + " “{df['consumer_complaint_narrative'][i]}”\n", + "\n", + " Give me reasoning as well as Category for this review\n", + "\n", + " Instructions for Reasoning:\n", + " - Give me Reasoning in detail\n", + " - Only one sentence reasoning\n", + " Instructions for Category:\n", + " - The review falls into one of the following categories: Complaint, Non-Complaint\n", + " - Select one category from the given ones\n", + " - Important: Do not add any formating into the output. For example ** Complaint or **Complaint** refrain from such format\n", + "\n", + " Final output comes in the format:\n", + " Category: \n", + " Reasoning: \n", + " '''\n", + " output = model.generate_content([prompt])\n", + " finish_reason = output.candidates[0].finish_reason\n", + "\n", + " if finish_reason == protos.Candidate.FinishReason.STOP:\n", + " output = output.candidates[0].content.parts[0].text\n", + " # apply regex\n", + " category = re.search('Category:(.*)', output).group(1)\n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " else:\n", + " category = \"Non-Complaint\"\n", + " reasoning = \"\"\n", + "\n", + " df['Prediction'][i] = category\n", + " df['Reasoning with Chain of Thought'][i] = reasoning\n", + " except:\n", + " pass" ] }, { @@ -369,6 +365,20 @@ "

    A visual representation of consumer complaints prediction, highlighting the most frequent words and pain points in customer feedback. This word cloud helps identify trends, sentiment, and areas for improvement, enabling data-driven decision making.

    " ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "aab01891-49c5-4530-a9ed-b516e7c5bad4", + "metadata": {}, + "outputs": [], + "source": [ + "def display_helper(msg):\n", + " return display(Markdown(\n", + " f\"\"\"
    \n", + "

    Note: \n", + " {msg}

    \"\"\"))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -382,14 +392,18 @@ "# Replace 'X' with blank space\n", "modified_string = complaint_text.replace('X', '')\n", "\n", - "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", + "if len(modified_string):\n", + " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", - "# Display the word cloud\n", - "plt.imshow(wordcloud, interpolation='bilinear')\n", - "plt.title(\"Complaints\")\n", - "plt.tight_layout()\n", - "plt.axis(\"off\")\n", - "plt.show()" + " # Display the word cloud\n", + " plt.imshow(wordcloud, interpolation='bilinear')\n", + " plt.title(\"Complaints\")\n", + " plt.tight_layout()\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "else:\n", + " display_helper(\"\"\"We included complaint and non-complaint options for completeness. \n", + " It's possible that our sample dataset doesn't contain any actual complaints.\"\"\")" ] }, { @@ -415,6 +429,7 @@ "\n", "# Replace 'X' with blank space\n", "modified_string = non_complaint_text.replace('X', '')\n", + "\n", "if len(modified_string):\n", " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", @@ -425,7 +440,8 @@ " plt.axis(\"off\")\n", " plt.show()\n", "else:\n", - " print(\"Sorry, there are no records with Non-Complaint\")" + " display_helper(\"\"\"We included both complaint and non-complaint options for completeness. \n", + " But since this is a complaints dataset, we don't expect to see any non-complaints.\"\"\")" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb index a215cd03..b08d68b5 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Complaints_Clustering.ipynb @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "c9f49cf5-f7b9-461a-a59c-867a414d3bcc", "metadata": {}, "outputs": [], @@ -135,7 +135,8 @@ "pd.set_option('display.max_rows', 5)\n", "\n", "display.print_sqlmr_query = False\n", - "display.suppress_vantage_runtime_warnings = True" + "display.suppress_vantage_runtime_warnings = True\n", + "from IPython.display import display, Markdown" ] }, { @@ -151,10 +152,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "c25f3d12-a0db-43ce-8244-294ce0132097", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Performing setup ...\n", + "Setup complete\n" + ] + }, + { + "name": "stdin", + "output_type": "stream", + "text": [ + "\n", + "Enter password: ·········\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "... Logon successful\n", + "Connected as: teradatasql://demo_user:xxxxx@host.docker.internal/dbc\n", + "Engine(teradatasql://demo_user:***@host.docker.internal)\n" + ] + }, + { + "data": { + "text/plain": [ + "TeradataCursor uRowsHandle=13 bClosed=False" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%run -i ../startup.ipynb\n", "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", @@ -183,10 +220,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "151fde7f-77b5-4c50-87b7-b182a07d19c0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "That ran for 0:00:17.99 with 20 statements and 0 errors. \n" + ] + } + ], "source": [ "# %run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_cloud');\" # Takes 1 minute\n", "%run -i ../run_procedure.py \"call get_data('DEMO_ComplaintAnalysis_local');\" # Takes 2 minutes" @@ -205,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "09c6a41f-08af-4c99-8b02-ef1e9e6759e4", "metadata": {}, "outputs": [], @@ -226,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "4ab30b1b-13e4-4720-9df2-a2901cf92785", "metadata": { "tags": [] @@ -238,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "4b692e80-8976-4240-9599-5aac385c94ef", "metadata": {}, "outputs": [], @@ -248,10 +293,1575 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4161f6ec-4250-4c0c-823f-c832daea1192", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "product=Bank account or service
    year=%{x}
    count_complaint_id=%{y}", + "legendgroup": "Bank account or service", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Bank account or service", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 2015, + 2016, + 2017 + ], + "xaxis": "x", + "y": [ + 22, + 36, + 12 + ], + "yaxis": "y" + }, + { + "hovertemplate": "product=Checking or savings account
    year=%{x}
    count_complaint_id=%{y}", + "legendgroup": "Checking or savings account", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Checking or savings account", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 2017, + 2018, + 2019, + 2020, + 2021, + 2022, + 2023, + 2024 + ], + "xaxis": "x", + "y": [ + 11, + 18, + 20, + 32, + 43, + 113, + 60, + 2 + ], + "yaxis": "y" + }, + { + "hovertemplate": "product=Consumer Loan
    year=%{x}
    count_complaint_id=%{y}", + "legendgroup": "Consumer Loan", + "line": { + "color": "#00cc96", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Consumer Loan", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 2015, + 2016, + 2017 + ], + "xaxis": "x", + "y": [ + 8, + 11, + 5 + ], + "yaxis": "y" + }, + { + "hovertemplate": "product=Credit card
    year=%{x}
    count_complaint_id=%{y}", + "legendgroup": "Credit card", + "line": { + "color": "#ab63fa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Credit card", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 2015, + 2016, + 2017, + 2023, + 2024 + ], + "xaxis": "x", + "y": [ + 151, + 246, + 83, + 228, + 22 + ], + "yaxis": "y" + }, + { + "hovertemplate": "product=Credit card or prepaid card
    year=%{x}
    count_complaint_id=%{y}", + "legendgroup": "Credit card or prepaid card", + "line": { + "color": "#FFA15A", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Credit card or prepaid card", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 2017, + 2018, + 2019, + 2020, + 2021, + 2022, + 2023 + ], + "xaxis": "x", + "y": [ + 143, + 262, + 286, + 387, + 353, + 504, + 358 + ], + "yaxis": "y" + }, + { + "hovertemplate": "product=Credit reporting
    year=%{x}
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CKu7Q567nwIly+OjJq06RaZkpeNW5axIgARIgARIgARIgARIggagjEDJxd/bcBRkxfrZUrVhayt1cPF4z8+enL5FaVW6RSuVLRh1QnhAJkAAJkAAJkAAJkAAJkAAJpASBkIk7GKsMHj1DBvRpp+c1fsoiGTOkh2TPmlm+3LZblq5YL08N6C4ZM6RLifPmPkmABEiABEiABEiABEiABEggqggki7jLkS2zjHnxdRn8SEcVd0jXtIq9qCLKkyEBEiABEiABEiABEiABEiCBFCAQMnFnTcts1bi2IBWzcMG8gv9+a/UG2fT1TkbuUuCCc5ckQAIkQAIkQAIkQAIkQALRSSBk4s6Oy/S/Q9+7fHlyyvRx/aXYDfmjkyrPigRIgARIgARIgARIgARIgASSmUCyibtkPi/ujgRIgARIgARIgARIgARIgARcRYDizlWXmydLAiRAAiRAAiRAAiRAAiQQrQQo7qL1yvK8SIAESIAESIAESIAESIAEXEUgqOLOWleXEEU2MU+IEP+dBEiABEiABEiABEiABEiABAInEFRxF/hu+UkSIAESIAESIAESIAESIAESIIFgEqC4CyZN/hYJkAAJkAAJkAAJkAAJkAAJpBCBkIs79LQbNm625/TYBiGFrjR3SwIkQAIkQAIkQAIkQAIkENUEQiruIOyWrFgvU8f2k+xZMyvIn385JD0HTpQxg3tIpfIloxouT44ESIAESIAESIAESIAESIAEkotAyMSdMVfp37PNVSLuy227ZemK9fLUgO6SMUO65DpX7ocESIAESIAESIAESIAESIAEopZASMXd4NEzZECfdlLshvzxACJ6N37KIhkzpIcnohdKwnYXz/vbN5bHerbx7NL+73MnD4onSK2ppU3qVaUoDeXF4m+TAAmQAAmQAAmQAAmQAAk4IhAycXf23AUZMX62tG5W56rIXXKKO3McVSuWllaNa0tCf8axDR0zQ54d3ENFKaKME6cv8aSWPj99iYK2ikNH5PklEiABEiABEiABEiABEiABEggigZCJOxyjr/RLRML2HziSLALJW3qoVaDZhaZd/OGzhQvmVWFozskq9oJ4LfhTJEACJEACJEACJEACJEACJOCYQMjEXTg1NDdplUi3LF6kgFjTRe2ROZA04q93l5YafTRRP/ybPbLnmDy/SAIkQAIkQAIkQAIkQAIkQAJBJBAycRfEY0zyTxlBhh/avnufWGvuvEUX7eLOmlpKcZfky8EfIAESIAESIAESIAESIAESCAGBqBd3iCBaI3Um7TLvdTk0LTSpkbtDJ8+G4LLwJ0mABEiABEiABEiABEggvAnkz5kxvA/QhUcXUnFnetodPnryKrRlSxWN1/8uVOy9mbcgTXPT1zvV9fLQ0RPxnDsTW3NHcReqK8ffJQESIAESIAESIAESCGcCFHfhd3VCJu7sIimlTt3U/rVpVieeW6aJ3NmPM7FumRR3KXVluV8SIAESIAESIAESIIGUJEBxl5L0ve87ZOLOng6ZkqdujyAGs88dxV1KXlnumwRIgARIgARIgARIIKUIUNylFHnf+w2ZuPPX5y78MDg/Ioo75+z4TRIgARIgARIgARIggcglQHEXftcuZOIOp2qtbcuYIV34nX0QjojiLggQ+RMkQAIkQAIkQAIkQAIRR4DiLvwuWUjFXTgYqoQaOcVdqAnz90mABEiABEiABEiABMKRAMVd+F2VkIm7cDFUCTVyirtQE+bvkwAJkAAJkAAJkAAJhCMBirvwuyohE3fhZKgSSuwUd6Gky98mARIgARIgARIgARIIVwIUd+F3ZUIm7mioEn4Xm0dEAiRAAiRAAiRAAiRAAsEiQHEXLJLB+52QiTscIg1Vgneh+EskQAIkQAIkQAIkQAIkEE4EKO7C6WpcOZaQiTvTPHz7rr1ez7psqaIydWw/yZ41c7JQsZq75MuTU6aP6y/Fbsiv+7Yf69zJg6RS+ZKe44JIHTZutv65Sb2q8tSA7mLcP5mWmSyXjzshARIgARIgARIgARIIMwIUd2F2QUIp7sLpVCHsho6ZIc8O7uERdOb47MYv9s9+uW23TJy+xCNEn5++RL/6WM82+r/+xN3Z86nk2FGRy7HhRCN5jiVTJpE8ueOSZ2fcCwmQAAmQAAmQAAmQQLIToLhLduQJ7jBkkbsE95xMH0io9g9ibvyURTJmSA+NItrFHsRc4YJ5pVXj2nrEdrHnT9z9+ZfI/Ndi5MSJmGQ62/DZTZt7LsvNpeJEUoXPMfFISIAESIAESIAESIAEgkeA4i54LIP1SyEXdxBDXfuOjXe89rTHYJ2Mt9/xlh5qTa20izX8honO9e7SUkaMny1VK5b2iDt7ZC8hcTdvgTvFXdt7Ke5COa752yRAAiRAAiRAAiSQ0gQo7lL6Cly9/5CKO2/CydS+9enSwiOYQonFV2Qu73U5NLUSx7h0xfp4dXR2cde6WR1PDZ5d3J3664LPwz/1R5zMnCuujNy1bx0r5W9JJakYuQvl8OZvkwAJkAAJkAAJkECKEciROV2K7Zs79k4gZOLOXzqkN0EVqgtkF3fYj1V07tl3MF5NXWIjd+cuXPZ56EdPxsrUmbGuFHcd28ZJ1YqpKe5CNbD5uyRAAiRAAiRAAiSQwgQypEudwkfA3dsJhEzc+Wti7k1wherSeDsOq7g8dPRESGvumJYZqivL3yUBEiABEiABEiABEkhJAkzLTEn6Lo3cmUjckWOnNPUSm7WOLpRumTBUobgLv0HPIyIBEiABEiABEiABEkg6AYq7pDMM9i+ELHKHA0V/uCUr1sfrZ5fcNXc4DiPgVn20Sfnd376xp5UB/hyqPncUd3TLDPYNy98jARIgARIgARIggXAhQHEXLlfiv+MIqbjDblLaLTPUyOmW6Z0w3TJDPfL4+yRAAiRAAiRAAiSQsgQo7lKWv7e9h1zchd8pB/eIKO4o7oI7ovhrJEACJEACJEACJBAZBCjuwu86Udwl8ZpQ3FHcJXEI8eskQAIkQAIkQAIkEJEEKO7C77KFRNyZVExvzcr9/Vv44Un4iCjuKO4SHiX8BAmQAAmQAAmQAAlEHwGKu/C7piERd6YJOJqEe9sS+vfww+T7iCjuKO4iabzyWEmABEiABEiABEggWAQo7oJFMni/E3RxZ5wn+/dsI5XKl/R6pNYm4tmzZg7e2aTAL1HcUdylwLDjLkmABEiABEiABEggxQlQ3KX4JbjqAEIi7gaPniED+rSTYjfk93rGydnEPNTIKe4o7kI9xvj7JEACJEACJEACJBCOBCjuwu+qBF3cmZ5yrZvV8Ru5W7pivTYVz5ghXbJSQUrolm274/XeY5+74F+CpLZCiBORVME/LP4iCZAACZAACZAACZBAkAhQ3AUJZBB/JujiDseG5uX7DxyJ1yjceswQWIUL5pVWjWsH8VQS/insd9bC1VK2VFGPuDNitGrF0no8iCoOHTNDnh3cQyOP9hRSe70gI3fBj9xB2H23I0a2bYtJ+KJG2Sdy5YqThvUuS5q0zk7s/PlUcuZvEQFEl23pM8TJtZlcdtI8XRIgARIgARJIQQIUdykI38euQyLujGDCPq3ROfP3vx46Fi9ylhxYjOCsVeUWmTh9iWf/9hRRu9izC1G72KO4C42427wlRla/7z5xV6hgnHTrdFlSp3F2Vxw/kUqWLouRM3+7L+7ZpvVlKVzQharW2VDht0iABEiABEggyQQo7pKMMOg/EBJxZ44SgmrYuNnxDnrUwO7JHrHDcWz6eqcKzR2798YTd97MXUx0rneXljJi/GwxUT2ciD2yR3FHcRfMuzKp4u7Y8VQy/7UYOf2X+8Rdty6XpcgNFHfBHI/8LRIgARIgARLwR4DiLvzGR0jFXTicLsSbtb7PLubs/45jtos7a/2gXdz9ceaiz9M8+UeszJgjcuKE+yJQ7VvHyq3lYiSVA40RGxcnn2wUWfWegy+Hw6BLwjFA3PXqLpIunbNz/+1wnMyeJ64Ud/d3i5VSxd13ryVhuPGrJEACJEACJJAkAtmudVhHkqS98sv+CES9uPMWPQQQU3e3Z9/BeJE8b+LOX+Tun/OXfPI9djJWps2Kc6W469gmVipXTO1M3MXGyYefxMlKl4q7hx9MJRnSOxN3v/x2ZUHBjZG7Ht3jpGxJiju+8kiABEiABEgguQhck95hHUlyHaAL9xP14s5+Te2RO9bchWbUJ8UtE4l1rLlzdl2Ylsm0TGcjh98iARIgARIggcQTYFpm4pmF+huuF3d0ywzNEKO4c8aVNXfOuOFbrLlzzo7fJAESIAESIAEnBCjunFAL7XdcL+6Al33ugj/IKO6cMaW4c8aN4s45N36TBEiABEiABJwSoLhzSi503wuquDMiqX/PNlK8SAEZPHqGDOjTTvvFRetGt0zvV5biztmIp7hzxi2p4i5O4uT06dRy+ZL70jpTxcRJtmwizqo8nV8vfpMESIAESCDyCVDchd81DLq4M4IuR7bMFHd/icxbEONKQxWKO2c3O8WdM25JFXf4/sfrU8vmL53vP1K/eVPJOLm7WSzFXaReQB43CZAACaQgAYq7FITvY9dBFXfW+rU7alSguKO4cxQOoKGK8ybmNFRxHnlb+0Fq+ewL98Wvbr45Ttrcc5niLvzezzwiEiABEgh7AhR34XeJgirucHr2+jVfp2xaEWTPmjn8qCTiiJiW6R0WI3eJGESWjzJy54xbMCJ3FHfO2fObJEACJEAC7iRAcRd+1z3o4s6cIkQea+6YlukkHMDIHSN3Th6VSXXLpLhzQv3Kd1K5L+Cp5x3nPFDsHDa/SQIkQAJhRIDiLowuxr+HEjJxF36nGpojYuSOkbtgjixG7pzTpLhzxi6paZl/nI6RU6fiXCd0IGizZxfJntW5wjt+MkbiYp1dt0j+VuqYOMmRI861iwKRfO147CRgJ0BxF35jIuTiDk3Du/YdG+/M504eJJXKlww/Gg6OiOKO4s7BsPH5FYo75zQp7pyxS6q4O3AQGQqp5cIFd4XvMqQX6XzfZbm+gDNxh2+9tyZG9u5zFzeM0nK3iNSqfgkxX2eDlt8iARIIGwIUd2FzKTwHElJxB2E3cfoSmTq2n5jaup9/OSQ9B06UPl1aSKvGtZOFyPPTl8ishas9+xo1sHu8fbPPXfAvA2vunDGluHPGDd+iuHPGjuLOGbdgiLslS1PL97vcJ3BqVI+ThvWdizv3EftvjCLQ6+bzd3a38luhJEBxF0q6zn47ZOLOOGe2blbnqigdRN/SFevlqQHdJWOGdM6OPMBv4TimzntHurVrpALTiMsxg3vocVkdPiE28e9Dx8yQZwf30P58doEKoYjtsZ5t9H8ZufN+ISjuAhygto9R3DnjRnHnnBvFnTN2FHfOuOFbSRV3v/6aSjZsTO38ACL0mxkzxkntmrGSO5ezaHGEnnZwDht51CySDQ5L269Q3IUEa5J+NGTizp+hCgTU+CmLZMyQHp6IXpLOIhFf9ibmrMdi/3eIucIF83oifXaxR3FHcZeI4ZfgRynuEkTk8wOM3DljR3HnjBvFnTNuwRB3+39JJbPnuU/cZckSJ507xsp1uZ2Ju0uX4uTb7Wnkn7POr12kfjNvnli5sWisc/cnhEudYY9UZJ7jTkgXU9yF3yUOmbgLl8idHblJwezfs41G7ryljproXO8uLWXE+NlStWJpj7izR/Yo7ijugnlbU9w5p0lx54wdxZ0zbhR3zrhR3DnnllRxd/GSyNz5qeXAb+5L7GzUKFaqVop1ltIaJ/LjnhjZvCXG+cWL0G9izN1x+2XJksX3CVDchd/FDZm4w6m+tXqDLFmxPsVr7qzY7WmV3lJE7eLOmlpqF3d//n3R51U98XuszJgjcuKE+x4I7dvESsVyMY7c0GLj4mT9ZyKr3nPfCwjirtf9IunTOTv33w7Fyax5Iqf/cvb98HtEBX5E93eLldI3OrvXYmPjZPlqkU8/dx83iLsu7UVSxzg79z37Y2XW3FSuNFTp3iUwbQUTAAAgAElEQVROihd2xu1ybJzMfV1cWXNXq0actGgUJzExzu7XXT/h3ersu4E/UcLvk5ho399F5Pp8zsbcufNxMm2WuFLcNWkcJ3VqiMQ46NuCbM6vvo2TRUuccQ+/kRT4EV2XW+SBrnGSM7vvc8+aKW3gP8hPJguBkIo7nEE4uWVCtB05diperV9SI3dnzvoWd8dPxcr02e4Udx3axEqlCs7F3boNIitdKu769BDJ4FDc/XowTmbOdae469EtVm4u4WzCB3H39iqRTze67+Vd5uY46dpRJI1Dcffj3liZ6WJxd2NRZ2MG4m7OApeKu5px0rKxc3G38wf3irsHuogUKuBszJ09HydTZrhT3DVtHCd1azkXd1u+iZOFLhV3D3aLk1w5fI+5azNS3CWLYkvETkIu7hJxLCH9qDdhhx3a6/9Ycxecy0BDFWccmZbpjBu+xbRMZ+yYlumMG9MynXFjWqZzbkzLdM4uSWmZIrJjZ4wsedPZ4qHzo075byJy17kj0jJ9FxwyLTPlr5P9CFwh7uypmFYIdMsMzaCkuHPGleLOGTeKO+fcKO6csaO4c8aN4s45N4o75+wo7pyxo7hzxi2lvxX14s7ew84Ab1Kvqic9k33ugj8MKe6cMaW4c8aN4s45N4o7Z+wo7pxxo7hzzo3izjk7ijtn7CjunHFL6W9FvbgLNWC6ZXonTHHnbORR3DnjRnHnnBvFnTN2FHfOuFHcOedGceecHcWdM3YUd864pfS3KO6SeAUo7ijukjiE4n2d4s45TdbcOWNHceeMG8WdM24Ud865Udw5Z0dx54wdxZ0zbin9LYq7JF4BijuKuyQOIYq7IAGkuHMGkuLOGTeKO2fcKO6cc6O4c86O4s4ZO4o7Z9xS+lshE3eoYxs8eoYM6NNOit2QP6XPM2T7p7ijuAvm4GLkzjlNijtn7CjunHGjuHPGjeLOOTeKO+fsKO6csaO4c8Ytpb9FcZfEK0BxR3GXxCHEyF2QAFLcOQNJceeMG8WdM24Ud865Udw5Z0dx54wdxZ0zbin9rZCJO5wYWhDUqnKLVCpfMqXPM2T7p7ijuAvm4GLkzjlNijtn7CjunHGjuHPGjeLOOTeKO+fsKO6csaO4c8Ytpb8VUnGHBuGvv/WhDOjdTjJmSJfS5xqS/VPcUdwFc2BR3DmnSXHnjB3FnTNuFHfOuFHcOedGceecHcWdM3YUd864pfS3QibufPWXMydctlRRmTq2n2TPmjmlGSS4/7dWb5Bh42br56z98fBnijuKuwQHUCI+QHGXCFi2j1LcOWNHceeMG8WdM24Ud865Udw5Z0dx54wdxZ0zbin9rZCJu5Q+sWDt/8ttu2Xi9CUeIYpUU2yP9Wyj/0txR3EXrLGG36G4c06T4s4ZO4o7Z9wo7pxxo7hzzo3izjk7ijtn7CjunHFL6W9R3CVwBSDmChfMK60a19ZP2sUexR3FXTBvYoo75zQp7pyxo7hzxo3izhk3ijvn3CjunLOjuHPGjuLOGbeU/lZIxd3ZcxdkxPjZsuqjTZIvT06ZPq6/5M+TS/+uasXSHsGU0hB87d8cv/VYUUc4dMwMeXZwD23xQHFHcRfM8Utx55wmxZ0zdhR3zrhR3DnjRnHnnBvFnXN2FHfO2FHcOeOW0t8KqbgzUa9GdavK+KmLpGOr+iqIEP1aumK9PDWge1gbrRhx17pZHY/jp13c+buAh45flrdWXJI//0yV0tc52fd/R22Raremk1QOTv1ybJys23hRtnyV7Ied4ju87jqRDq3SSKaMMY6O5edfLsny92Pln38cfT2iv9S4YSqpUDqto3O4dDlOVn5wUb7f5ejrEf2lGwqJtGmRRtKlcTbmduy5JKvei5VLlyIaQ6IPPl1akcZ3xcjNxdMk+rv4woVLsbL4nUvy6wFHX4/oL91cKk6aNkgraVI7G3Pf7Looq9+Pi2gGTg7+mmtEmjeKkWKFnI25M//EysK3Lsmx4072HtnfqVJJ5I7qaSV1TOInJXFxIp9vvSjrN7hvzGXLJtKqaRrJl9vZvRrZoyZyjz5k4s7axBzROqu4g0AaP2WRjBnSI6wNVQKJ3EXupeeRkwAJkAAJkAAJkAAJkAAJRBOBFBF3kRK5w4VOqOYumgYDz4UESIAESIAESIAESIAESCByCYRM3AEJWghs+nqnDH6ko7w0+21Ny8yRLbP0HjRJ2jSrE/Y1dziHhNwyI/fS88hJgARIgARIgARIgARIgASiiUBIxZ0RR137jo3HbO7kQZ4atkiA6a/PXSQcP4+RBEiABEiABEiABEiABEgg+gmEXNxFP0KeIQmQgCEQFxcnp//6R7JkvkZSOXHTIUoSIAESIAESIAESIAHHBCjuHKOLjC/+c/a8TJv/rjS4vZKUKVGYE+7IuGwRe5QwUho+fo7kyZVdenVuLrlyZI3Yc0mJA9+951c5+ftpqVbxZolx4OqWEsfMfZKAGwlcuHBR1qz/UqrddjOfc24cADxnEghjAiEXd9aURnAw/e7QEoFb8hDY+eN++b+hL2pvwUfuv0fy5M6ePDuO8L0cPHJCPtjwlZw5c1Yb2d9erZxkvvaaCD+r0B8+Jj3obTl74WrpeE8DaX5nDbkmY/rQ7zjC9/DP2XMycsJc2f/bEcmdM5v8X/dWUrJ4oQg/q+Q5fCwqrF3/pRw/+afkzplV6tWqyAl3AOhjY+Pkmx0/yZZtV3qA3HZLCalQ9kZJkzp1AN9290c+27JdJkxdrO1+Wje7Q+5tUlvSoT8Gt4AI4D3x0WdbpUzJIlIw/3UBfYcfukJg76+HZd8vh6VWlbIccxwUXgmEVNxB2C1ZsV6mju3naXmANgg9B06UMYN7RFTdXaSOn0uXL2vbCURQ6teqqMLumowZIvV0kuW4kVr43rot8tLsZXJ3o1pStmRR2b57r7y3brMMeeQ+ua1cCUZAE7gSGHcvz35bPv9qh5w9e14GPtRealQqy2iUH25r1m+R1R9tllFP3C+//HZUJkxdJL07t9BFGW6+CXz93Y/y5HMzpX6t26TKraXk0JETsvCdj+T+9k2kcb2qHHM+0GExYfKMZfLdrp/lvnsaSOZM18jaT76U02f+kWF9O8t1ubJx2Pkg8Ofpv2XgM9Okw931dR4zb+n7svPHX2Rk/66SM3sWckuAwJm/z8rYl9+Qb7/fIzcVKyijBt7PBcAARg3mJl98/b08M3mBnD13XsYMfpDvhwC4ufEjIRN3WEmFK2b/nm2uEnGR1Aoh0gfFtzt/VnE3fnhvyXddjkg/nWQ5fjAbNm62jBrYXcqVLubZ52+Hj8uI8XOkR8emfKAmcCWs4+7SpUsybsoiqVSuhLRrUVcuXLwk12bKmCzXMlJ2cuLUnzJo9KvyQPsmnrGFiMqrr62U557sKVkYMfZ6KX89eEz6PzVFenZqJvVq3upZdPnjzzPy7IsLpPptZaTlXTW5GGOjh0nizDdWyY7d++Spx7tJtqzX6ifw9x9v/EYWL/9Ynnnifo0gc7uagHECf2pAd8mYIZ0gAvrmyvWCxesBfdox8uln0Jj3aM3KZeW63Nnlhz2/ymM923CYBbBg+taqDfLumo26WDpvyRp5qFtLYRYch443AiEVd4NHz9AHnX3wRUoT80gfMibNq0blstKiYQ09nfMXLkrq1DF8+fi4uEgVeXrSfCl1YyFdlbWbgnzyxbdy+NhJFSncvBM4e+6CjBg/W0VKq8a19UOY/MTGxcryNRvl+x/2y6CHO0jatGmI8N8JNSbaW77ZLeOH9fJMtDHWXlv2gUwc2YfizstIgRCB+D31x2mvE2qko3/46dfSp2tLPu9s/CCKEXka3q+zlL6pcLx/NVH3RnWrSIliBXmPemE3ZMwMGfJIx3jsUCs7dOxMfbYhjZ/b1QS2794noybNk65tGkmjupXlpdlvyfX5ckdEW6yUvJ7nzl+QafOX6+IBosN/nflHnntloYwe/IAnKy4lj4/7Dj8CIRN3ZoLXulmdqyJ3FHfJMxBMmhdWYFErBuEy5qXXpXiRAtK0fnX9O5o2xL8WWFXEi3tE/65eV8QwoYyLE3LzM4Tt48589PCxUzJs3Cx54qH2cmOR65PnJoiAveB5OHTMDKlya2nZsOlbTSXEvfn6sg9k6KOdGCX2cQ2RPth/5BR58L6mXlP8ea/6HvwwAvno06/FRJ7sn8RiDGrJ6HgbnwyE75S57+hf2hcN/v7nnGZ8dG/fSMqUKBIBT57kO0RT6gBh91C3u3Xh9OKlS1pj3KpxLZbo+LkUWDQYOWGOHD/5h0wY0UfF8NbtP8ridz+WkY9308gxNxKwEwiZuMOOfKVfIqVh/4EjDMWHaDziBYS0JHuaFxrKT575pjrxwZXv73/OakpOkUL5QnQkkfezgSw8QCQfOHxcihTMR5Fnu8Te0gvxETMpypghvTzQoQknjf9ys9bEgsu27/dorWKne++Um4perwZUmGDjc3//fU7rUhjxvAIPqf++skMi78mTvEccyDsYdVGn/vhLCubPzfv138uDaPDoF1+X0YN7SKEC8U1Afvj5gDw1ca6MG9ZLJ+DcrhDA+3LBsg9k/efb5KGuLeX1tz/Uv8fzDtEoLPaZSKd5zmXKlIHRdhHZs++gDB8/W+66o7JkyJBeFixdI93bN9YMrGMnfleDPLNhcQFbpmvoqcB7TySo4s7U2W3ftTdBtmVLFY1ntJLgF/iBgAjgpke4fsfuvXLzTYVl8P91VDclFIAPHvOqdL63oUYCsJL21upP5Yeff9X8bbij4cH6+lsfyuQZb0qFm4ureQiifG7aMJkZ9Ox06d+rrc+UJJMuN6xfZ61J4crZlRGCMfXG2x/qC8mMOzN2MClCbRQiUidPnZb6tSuqWYip9XHTGLOeKxbApr+2QsYOeVBNjxA5RnT96QHd1ZgBTDHeJk5bLBkzpldzGrzcm91Z3fWTH2SHYEW7aYPq6hrnbYO5ClI39+w/KA1uv01aN61D44Z/F15hOoOsDm8GW+ZehklIvwdbS9bMmVy/qIB3KyJNeGb17XGvpLc4Y5pFGkysEdHD4ioio2fPX5AGtW+TAnlzufURp++DZas3qJjDMw1R4Y1fbpeJ05Zo7fUro/tqX1Q852CyAoYQKg93v1sa13WvIdLFi5dk0bvrdI7RsE4lXWDB4umM11eqUeFTj3dVJ2qMtQnTFstX3/4gadKk1kXBQQ93pCGSa++4KyceVHHncpZhc/pYKft083aZu+R9Gf5YZ02Bw0otRLd10o2X+9Hjv8ujD9yjTl+Ll68T9MUb/lgXOXLslGBCjlo9t6XmoGAZtTreHOMgkuHMh4hKTEyMrkYiLczUloXNIEiBA0EdD9Iun+zbKV7aJSbhz76wQCfgDetU1pXcaQuW60seY89t48t6acAA9RNZs2TSv8akZuTEOdK9XWMpdeMNeg8+/vRUeXbQA1KhzI2C2ovnXn5D6tas6FPQpMClT7FdQhy/POdtvVftC1GwCx/w9FRNAatd9RbZsOk7dYZEpMDtjsHmnkTWRqd7Glxlp457Gc+5cqWLy5l/zsoXX30vIx7roj3d3Lxhco062E83fye9u7TQexKuhXMXvy97fzmk5kc/7z8kz73yhnRt20iFypsrPxHULza/s7onCv/eR5ulcoVSrm5LhEwivGtHPt5Vft5/UJ5+fp6mCaPOE1HjZ194TReijV+Am8ed9dwxRxsxYba0b1lPbildTA3zMM56dWquCzAbv9yhi/RYuKFzq3tHDcVdFF/7i5cua90Eej/Za52OnfhDhoydob204AgJkffYyFfk9F9/u962HhPu1es2aXocJjM5s2WRShVKSuXyJTUyhYmPcURDtFriRF/wSNWEeZCbG3djdTtd2jTxBBtWZLG4YGo/ccvB8n/b9z95osZRfBsm6tQQARj3ykKpeEsJqVfrVnl+2hKJjYvTNPY7b79N6/HWffaN7D9wWCME+Pzly7HxogiJ2mGEfxgRJrRCQDocxHChAnnUWh09KV+Y8aZkz5bZkwaMlfDRL76mUc9by94U4Wee9MPHBBqGFlu+2SW1qtyiUZRmd9bQPoGYMCLt0JhKQdQgUoWFCD7nRNADdemK9bL6o006sW7ZqJZGhTHGsBgT97//gxhG/za8W8dNWaj1szmyZdYFm/FTF8nowQ+62sF6wZtrNbUazzE85woWuE4Fi9kQdf/j9JmrDH+SPvIj+xcwnoY+N1OefLSTpmdisW/s0J6eSB2eiRB4eIcwqyiyr3VSjp7iLin0IuS7sKXf9dMv0rb5HTrpNjbYECR4sKaOifGIlm7tGmkz4BYNa3qiCRFymkE/TERJEO1EfSIm1VcK5mdpZNM4wJpeUft+PazNWNG35/bq5aVjq/quT5vDBTGRzs6tG3qK5jFBfPK5WXJv0zoafcLEkSlM/w3fpSvXy+m//tGJ9bhX3pCOrRpI4UJ5ZeOWHWpff/nyZendpaWyg3kNzDEG9Gkv2bJc69qXOUTurp9+lW07fpLaVctJhvTp/o0id/bURhknXEQC0Kh72rzlUiBfLu1l6eYN9x8E8vkLF/Q5BzdbLGyNGdLDs1DF51xgI8QYXdzfoYm8+toKFXeI2k1fsFwG9mkvWf6X3oosBvTGc3tECsY0xQoX0AXU4eNmy8Pd7r4q+o77+rPN2zU9EYumHe6up4s4bs72wELf1HnvytC+nTSijmyEx3u1vYrJkeOnZNE76+TbnXukQe1K2hIGNdvc3EEgpOLONCw/fPTkVTRZc5dyAwzXBSkQaByK1VmkL2HlGw6RRQvlU/GHGhX0O0KtxZ11KrnebtfYg2NV29oiAREp5MC/+MwjmoqIdKexL78u9zSurSkTbt8Qtdu8dac81quNR+xaU4S3bv/JbwoTxiJ6bt1QII+rUsKQeoP6CUSfkL6FSBQ2/D0cNWtUKqOrtjAUKVIoryBKjxc90lxRfO/myQ842WsX8XeImEyYulhXuU+c+sNTA4oshvq1Kurk0u01oKZ9TpP61TxjDuz4nAvsSY404fVfbNPJNrYdP+zXSHzZkkX0GYgFGqQRI5UfpislihVyrSmXcbPFYhUi6nWqV4g35vDORb0sFlgf791Wzp+/oKKmfu3bPCmugV2V6PuUcbMFm7lL1mj9HerZzWZNSa9RuYyWj2zcsl0Xptm7MvrGg7czCpm489bryh1Iw/8sX3/rA4FrIVasL8fGxkvBwdEvX/u5zHx9pfS4r6meDGoGhvfr4jpzFeuVxIvmi692SpmShT1CF1bs6GvUpF5Vef/jLZoO1r5lXV1lLFoovxZBe9vgiIhceKzqum1DO4QhY16VAb3bSYG8uRNMYcJLDM28kV6HhQe3bahFGT5+jkbv6tasoBEpbPboOwyRrOLlulzubj6N+xVCDmma9zapLcdP/SnDnpulKZmo+8Rk0loD+srcdzS97sH7mrltiMU7X2QrfP7lDql2WxlPFNjJcw7jEzVVNxa93lVp6lgoGDFhjhqumB6BiBhfuhyrKZuoY6xVtZymZy5btUFTYAf/330e1piUZ7n2GlcxwwCE6Qq44TlXqXwJzUIACyyUWtNXwXfe0vfVddPtdbPghuccMhCOn/pDF53hBYBUzEnTl2pWglmIxv349nufSu6c2Vmr7ZInfMjEHW2qI2MEIWXTmoKDejJ7g1asRqK2AO6ZtGH/77pijI958XUZ/EhHfRlhlXbS9CVaR/DCqEf0BQ0hjZRDNJJHPQHEirXWMTJGSfCOcv7SNXLmn3O6cv3dzp+1V4+vFKacObLIvCVrNHKFlW+3bkibg/kRFlnuu6eB1pBh4mONvoMNajEwQYKhDa3Yr0Q5X1u2VuYsek8ngo88cI80qV9VoyfWGlBMfOa/uVb7l124eEmKFy6gEVBER90eAcW4Sug5h1Sv2QtXy3e79nocSQ8eOS7PTF7gyQ5x071rGnVXvbW09q6EazUMk+z12hDNyJjB/YwIHjgj7d84WruJmRlnqz7cpItUcGhFBPTU73/pu8J6H0LQHDv+O11w/x0gZgEU4q1KhVJX0lzHz1bHTGu7Dnzucuxl+eCTr2Xpyo8lR7Yscn/7K8ZdfM5F390WMnHnr4l59GGM3DNCfQUeljBVwYZJD1bRrGl0qNfD5BIrjGnTppZZb6yW6pXKuHrCDVamKXy9WhWlZuUrVuz4OwhkCDu8qBE5aH93PTUtgANnntw5JHvWa7XWEX3L5ix+T0VfntzZI3cQJeLIMdbQBD5tmtTaB9NfCpO32p9E7CpqP2ps128qWlAbAJsXM1Jg313zmRrXwB6bLQCuHgLeakARTca9Cidh1NKiX9mI8bM9zn1RO5ACPDF/z7n06dNqemvT+tXU8n/FB58rP4zJ6rfdrC7CSJHF3yOKgDR/N2yIgKL+7rdDx6Vpg2py4tTpq+q1TUS0T5cWkjZNGukzeJKUvLGQjB7UQ81t3L7BcAUben5aN7rg+h8ZWOR7ZvJ8ebJv53jzCnuaK37lxZnLdP4BV1Ju0UUgZOLOCAWkZsDelq49kTFwXpy1TFf9jbU/VrUxScSm0ZZde6Xv8Jekbo1b1d3K7T18YKQycuJcFXeY4KBgHuldEMM//nwgXuuJdZ9tlckzl8nLzz6qK2qILGzaulMqlSsRL18+MkZK0o/SXwoTJofoYYZURKTRYbFo1KR50vKuWlK5Qsmk7zyCfwHP1GdeWKALMljhxkKCaUOBurGcObKyBYCP62uvAcWEBxE7pKkjgoJxZ4yTura5S+tm3e5ICpTennMQdnA5tDqSmigoTDAmDO+tEStEpL79/mcVe+i56rbN1Gujl1u3to08izGfbdkui99dpw3Rf//zjIq/G67PIwcOHZMnHuogJYsXchuqq0QcIpvIGDIprmBpH3N0wY0/TIyIu3TpkrZHMPccFl1GTZqv481E9JA9s3TlJ5rtYe3b6OqBFyUnH1JxR0OVyBslmDi+/f6n2jcKq4f4M2ybxw/vLXn/F11CE1cUNFe85SbZvHWXbP5mp/R7sI1cmymDa90hIdI+2PCVrP3kS+1PhjqTJ56ZrmIYfZCw2aMtaE+BNB287DEBOvn7aW3setf/6oHcNAHylsIEUwu4QMLyf+Tj3XRhCJNytOpAnWLe63Loam61ije7zozAOI22bFRTX8ZPPz9fFwbw90glRHN41FuwBUBgz15vtvSYdL+1+lOPSQEdSa+wtD/nkG0Al0Pco2ayiHE4YsJcjShjwQtZIMhYgAszxuuvB4/K7j0HdNEGdaJu2PDsx7Ns5Yef6/sBizJYjEFadY+OTeW28iXiLTAc+V/kJU3qGBpfiKhz68Rpi+XYyT8Eiy1o2XGll6p3F9zbypXQPoSFC+ZzdW0ZIseIfMIts3SJwto2AfMT3MPWNFfUsyPdHzyPn/xDF6WxQIgFam6RTSBk4o6GKpE5MPAiWrH2c62hSJ06tVy4eFHTvNAXyj7hxhmalB00EG5av7pkcrHIM1fcW70pRPL8N9fImMEPClZwYWlvWlGgrgBpS8dO/O7pnxeZo8fZUdtTmPACggvkw93v1okQhC9aJ/Tq3FxuKVVUftp3UL74+nuv7SbQGylb1sxRa/kMgxUYMfzf/feo6MWqNZpMZ0ifXv+MlJyEJj+ozUCNaN2atzq7YFHyLTiMTn51qdxSuqhGh7EhbXPwmFc9dU+od6QjqfcLjsVb9MNDy4TsWTPrh6xOuPjzmJdeFzhjw7wLveHmLXlfChfMG89xOEqGU4KngWfTmJffEGRw5M2dQ4b166zukMiGAUcsoOa7LkeCv+PGD8DFFYueR46d0jH19IDungbdViOpv/7+56paZDfyMueM+RzmaKg5RmsOLPqhH6N1wRn3Y+tmda7KYHAzt2g495CJOxqqRPbwwKQRk2wj1swkx0y4zdlBtEye+aZGUdAP7u9/zspTj3eTIi50NrQ+UJHKipUwFCyjj+Dol16Xe5vcri9zayuK/HlzajNcmA9gkgSBHBOTKrIHTxKPfsmK9YKJEMaatQejaRzv6+fRGgCrvCgQd2vvsoRaAEBIPzz0Ba2XRQrYHTUqqIHIgUPHXblqiwbw2FKnjrlKnMA8yroIQ0fS+HceFnDRsw2ufHi2QRg/++ICdcItfVNhzfowC1pYeJg8401dxBo/rLdUubUUTRxEdHEGKdZVKpSWxvWq+HxyujWzww7EnwsuahvhkGutRcbnYdICcxu31LX7GkQQwS/MXKZprrhnscj33keb5bkne+pc5cVZb6loNk7LWEj85bejUrtaOddE2ZM4dQmrr4dM3NFQJayuc5IPBi6Hf5w+o0YgJp3Gvspt6i1+/+MvTTeEi5pb3TWxWvbRZ1tl5YdfyN5fDunLBYYN1tVs1DWadE2kQaBxNep9kP/u5rQIq+kK0pdQd4GGrQm1QkChPVbE3dyoNaHJzytz3tFebmigvPPHX2TZqk+0lccnm77VlFdTd5bkB0YE/gAWsNDaBD210LPS3g8Up2R3JMWkG/eqaVERgaedpEPGAuA773+mjsC/HDwqbZvfoWMIzzJrBNSkE8P5FuZSyPRAPZBb3w8GujFVQQsOf47AMJ9C+pxJU0/SRYvwL/tywf1m+0+CecozTzygpQ7Y7G7gEX7qSTp8zM+Mo/fX3/2oC3sD+7ST7Nmy6CKNaQ2DnZiMLETdjf9CknbOLyc7gZCJO5wJUjRoqJLs1zQkO7ROuM0OrCk4SJkw5gRrP/lKrr0mg6CurHObhl7T50JykGH4o2Dy4YavpegN+eWmotfHW83GC8j68kHvOyyKYJWb1sRXLiZaSaRKFaP9A/0xsfbPQ9TAzZuvyY8391G88FGL8djIKQLXvm7tGqs4Bk+I6gG920qxwgVcgxPR33Rp03j6f/pzJEXaOprMr1n/pbRtcYfcd8+drl1YwGTw3bUbpXaVchohsb8brCn9qL1DBNnbIgzEMjIXTLvDTLUAACAASURBVJqnGwYe5kiYXLe/u76mx8FJ2L4hpQ7GSZhowwl3yYqPNTuBDamvkDIR0NurlvOkm+NdClOuxvWqasYMTbm8300wVZn1vzKcUU/crz0WsWFMIoMGzdEzZkyvvfQQ7XNrRkwkPodCJu6Qltl70CTZvmuvVy5YEZg6tp+rHuKROEB8HbOxD3/iofa6yo3tp32/yXOvLNTeRqgdgBvioGena0NXuM65fcOkGxNmNFKG2YBZzUY6jqn5cTsjJ+dvb+h98tRpWbBsrfTo0NSzguvkd6PpO2aiY9xHzbmZv69RuYwgRXHdxm+0xhbRqMNHT0qeXNldZfBjuCTkSFqp/H+Orca8AI6SiLq7vbmyPQKKd8WAp6dqPbFpuePt3sL4w337D/pgdmrmqlQwjCFEO01KnJUP3hujX3xNF0lRT4x3yKGjJ2XKmL7sZ/kvKJMpBvFrbP29mXKhDygY4x3RrmVdNeZya9TdjDFEhRe9u07nbVhwsWdkIZ0T7U5g3AVuMO3q3bmFZoBwC18CIRN34XvKPLJgEEDECf3vkIaDiIq3MD4eEmjYDfcl1EGhiWZsXKyrXtp21ogMoKcRVqfxUIU7FSbTbp8QJmVMWhcV4OiKOikYAqFVR+kbC8v9HRpryqGbN4i2NR9vuSqtCy6k1obeYIT0VvQ/guEDoslu2wJxJF390Watlb2StlRLCuTNpXWzHVrVlzIlirgN2VXnayKgeDd4653qDdCR46f03YAIFbf/CKCOFpE7GGHMeGOlNKlXTVvoPN6rraAdxZxF7+mYQ/NqN28mAlqnegUpUiivCpahj3a6ypQLCwxn/j4roybP16bfJu0QEazPv/pe6+TdlC4Ms5oxL72hZjUN61TSRXrM51BGcjk2Ll7KJv5+2oLlOpeDO7OZ+708521dtDYL/W4eh+Fy7iETd4zchcslTp7jsBbPm3z3d9dslG3f75HBD3fQ1X/Y7qJFQJP61XQV0u0v8U83b9em0yP7d/XZtBZpnegZhRcVeHW4u54KZaZtXhnX9kUFq9DLnTOrWpDPXfyeOtFhAu7WDS/ji5cuxetlZEySYI1tIlGmBhTW9mg6jYbzvlLoopVlQo6kWNh6fvoSnTiil9TCd9bJ/gNHNJ1u7NCegnFHR9L/RgcWsDBxfKhrS59RYCOoMdFGCh23/whgAWb6guWSK2dWGdinvZz6/bRG2MFz+oIVgrq9vj3uUWGM/8aCjFvfDxAq6Oe2+qNNkumajD5NuUz/XkSq2t9dT9+xy1ZvkKb1q7oyiwbvh0NHT8h3O/eqCdKogd1VqHlb/MPC1rbvf5KBD7XX7I6PPt0qW7btUidwM/fj/ZvyBEIm7nydGsLn6JuGyX2xG/KnPAEeQZIJwFJ8zqLVUu7m4roShs1XDRQmijrhXvKejB7UQwvrseGhDGemEsUKucYtEhPpN976UN5ctUHGPdnzqqa1phkpUpth8nD+/AWZOu9d7TPY/M7qrn2BWwes3ZHPWK+b1VhrQ2o0BXb75MfKDi9u9Gcc3q+zZ9IN4TJt/nKNJmOSiD9PnrFU+yDlz5NL60Hdvi1fu1EnkIicYBKN+3TMi6/LzSUKaxQAJkBwJI2NjVWhd2/TOtKgVkVXpreaZ/src9+RHbv3qfj1ZvdvTaHDd0ZNmict76ollSv8l/7q1nGHqB3eATBTwT2J8YdFF4wvLJgOeriDZn7gXkXvvKyZM0m/nm2kTInCrn1HIGsIDrjo1evNlMvMTwY93FFFzKyFqwQO1wMfaict7qzh2nsVIu/oid+1pzHShJ98bqZ0bt3Qs/hnFmHwTIMBC/pWPjR4suS5LoeMeKyL6zNkwukZleziDiePNA2sdD7Ws004seCxBImAvQbqwoVLsuqjL9QJskalMlLshgKy/vNvNLUEqz94UX244StZ//k2V7qBYXIYkyrmKlGLCeTYl1+X0YMf9EyIUMc4b+n70rNTMzl37qJG89zcOgHtN7BBuHmLHmPyA7Ey7sleGoFC7crwx7q4Mt3QfntjMoj0S/BAWwR79MTU4yHqjho83J9Y5cbCnFsaUHt7JCJN7rGRrwjSv+6+q6bs2vOrvLlyva5co3XMy7PfVlGHyCdMpcZNWSilb7pBG1i7ecMiIJpz26NKeKYNHz9bHrm/lbZQwKIDa6P+Gyn2coYpc9+RPfsPybWZMniEnfk03r3oAwqDFtRFmfozN487uymXMX7LmCG9OrvCxAe9LLu1u0sXINBEHl4CMP5x84b7cPPWnfJYrzae573dKAl/xnukWYPq2ieP/grhM2JSRNx5a34aPkh4JEklgHSv5155Qx7r2VYnObjp8QC9vVp5+XjjN3L0+CnJlyenpsnBnW/a/Hd1Ag6DndpVr6Tk4CV14WL8NLKkHlekfX/pyvWCBudIm7NOiPByOnTkpIyYMFud07BIUqNSWVeLPNRQwMa+fct6ngmNXazs2XdQa1aG9+vi6lYT1ong5q27ZN7/2pzgpbz/wGGNqhu7dbshASbhWMnt92BrTQ1284axhcnP9z/u1+gnUqthkuTNeh2fW7dxqzz5aCc59cdfkjNHFleLY+u4sS8EIuLy5HOzpFfn5lor5a02ys3jDuYqw8bNknRp08qwflfMe9ATFP3L/vzrb2lUt4o0qH2boH5s7YavtG7Km/ummxnCIATZY1g0RYTqjbc/1OheQn1UP/78Gzlz5qw0qlfFlfevPRuLDtXhfRdR3IX39Yn4o0PtJersUCtgUjDxcB39IiJSPaRg/txqgAGb9j//OiPVbysjXVo3FBTXDxw1TSeaiMq4cVvw5lo9bTh6+drwgEU0qm6NCtLyrpquTcMBH6tZDf5sFycwsEHfQUyyUTAPYTxz4SqpV/NWqVDmRjcOMc85Y9I47pWF0qZ5HY2emHo8NJI3DodG3D3xcAem1P9LDtHgL7f9IFUqlFTzAVivWx1JURP69KT5+lxrVLeyTJi2WCqXL8W6sn/5WXsJ4l1gn2hba6P8PQfddPNaeyuamuMihfLJnXUqyRdffS/LVm3QlE0YXLRrUVeNz7SPZb7rtE2Am/uAIno8fsoiTaFGr0+MvxHj58jTA7v77aOKcbhn/0HNPip/c3E3DTfPuaKH4Bk42d7XVP8Oi/Zp0qTRP7s5kyNcB0OKiDsUo2NjWma4DovgHReiTM9PWyJnz1/QNJEc2TPrw9UYNsCZD3UCsOHFKhpW0K4vkFsuX4qVNGlSe5wlg3dEkfNLYAPb6yGP3CclihXUA4cgQQ3Grj2/SL2aFaVJ/apqTwwHK+S8w66Ym3gVJ0ghgWhBw2C1rX9ulq5yw6QGqYfcrhDARAaTbKQUQtzhxY2/e2v1p7Jj916NBkAMwrZ+45btUqNyWY2Yut0gyZsjqdWQABOhZybPV4dhtoa5MtYQdfr10DGpemvpBGuj8Aw8cOiYvLdus9aVQcy4qR+et+eTsa1/7ME2nsXT7bv3yStz3ta6WUTzIF6QInzy9z81tRrtOiAG3bpB4KVKJfpcwwIqnmX27BgrGzz7Vn7whfZ7q1+roluxaW0xSmgQCT56/HeNIKMWG3M5XxsW93/c+5u+G4oUzOfq7KLkHjghE3f+3DKb1KsqTw3ozuL85L7aKbQ/rG7jhYxC8IOHT2hvHrgXZs6UUWCAgYgeXj4m9RCpTVgVGjPkQcmRLXMKHXV47BYRzYnTFsuxk39oRPOP02dUoHRr10h2/rBfXlv2gR4oopvIjb948TLNCARmA3Fy8MhxTf81q4ovzlomN1yfV/LnyalpOQN6t5PbypWQv/4+qzVTSEmscmsp1wsV1Nq9tmytpnjhXsWGRRe8zFGflyt7Vhn4zDQpX6a4NrXd+t1PsvLDz6V/zzZS9Ib8ro0e2x1J7f2iNOVw3CzlVLhgXp0sLV+zUdPP72lyu+vT5xKqjUKzeIiWjvc0kJhUqTQKD8fSm4pe79oxB+ExZ/F7Ktr6dGkphQvllQlTF0uD2hXV9RHpmZNnvKlGNujvhrrkT7d8J317tHb9eMNzDdkdH376tTzxUAefi1PWZx9NAK/Mi66kT8+UFnfVlLo1bvUq2kx/S9QbI9qMGkZkcJn+eHj+MeIXunlmyMRd6A6ZvxzJBNZ+8pWKOvTkwYsHLmowuzAWuvZmy5gwob4FUTyYOrh1g5tonIg8M2mBppOYQnmT944VNLx4ECmA2QP6uuW9LoemdFareLPrV8wuXrwkz7ywQLDodPbsBRnRv4sKF7x0nnh2utxRvbzcdUcVgUELeuQN7dvJb5qOm8ah3YAAbTl+/PmARvBMxHPdZ1tl8sxl8vKzj/pdyXUTN7v5AIxYnntloTw9oJukklT639hg3sCGwFePDGttFO7fIWNmyJBHOmraMDa0Pfm/oS9qfR5S0t264R35494DsmHTt/L1dz9q9gbGGOrxjOMyUjNRF4r3LAzO3JyaaR0n4LN2/Vfy3sebZVjfzlc1kbc/+86dv8jF038BIuo+df5ydWVt26LuVbff1u0/ag/GZwf30DnfO+9/Jrt++lVrG8+dOy8jJsyVrm0aMoshRA8uirsQgeXPJkwAK7Fbvtklgx/p6FnBgThZ9eEXMvLxrmo+MHzcbI1axcTEqNPm/3W/29UNvyGOX5q1TB7tca/WQs1fulayZL5GXb/Ay5gR3FKqqLqmffH1917dDWFUgIiqW1LpMDlETyi8rJGWaSY36MEFMx80b8Xfgwcm5aitYI3PlXsYCwhoh9C/Z1udOCK18N6mt3vqFE1vvJuKFtSG3m7tsWV/4mEhASLOCDcY+iCVtWn9ahpNQU1Uq0a1mBLs5VVhr43y1gjd2rYDGR4QMIUL5nO1cMHiFBZccmbLIqvXbZLWzeoo3aUr1mvUDqmaTN1PeG5iPmE3SKKTa+DssCA9atICNZB6uNvdGm3/4/TfgkUuROkRtTPmXYH/Kj8ZKAGKu0BJ8XNBJ4Cbf8xLb8ixE79rXRkic7AkRp1P0UL5VKjAPROpX9iQOocaAggZt04gkYaDh+NHn23VtMxDR0/K8yMfUldSX65f9jqVbFmuDdghLOgXPYx+EFFiCJU2ze/QFCX0OUIKyeXYWMmTK7v22cLiw1ff/iC3lC6qdUFur80zzKzizlsLijC6zGFzKDD06fXE8xpVf2pAN8ECjFufY4FcFGttFOr0kcpq+lfanXCZOhefKN6tz0xeIGVKFpV7m9TW1PPBo18VX2ZIqKHCwkOPDk3ZiPpflPYsImR5JOTkigVTlEps/HK7tG56h9xZ5zbXpR6C29ff/aCL8WfPnZfJM5ZJXFyslkHg/XnwyAntjXf58mVtheV2p+9AnoVOPhN0ceev1s56gGVLFVXre7cXRDu5aNH0HYgV4/6F1VljSfzFVztVzFlXGrHyjRWfgQ91YL3mv2YEiGrCvctbo1aMk/c/3uKpU0H0asUHn0vXNo1k8fJ1amLjrxg6msaZt3OBu+bYl15XcQdrf6Q34aU8d/H7MqJ/VzV7gHlNp9Z3av8jjD/TIw/j1q2RAog5tDpB3Sdq7FAf27Z5XTpA+rlhYIT0/KtLtCa2f682fiPmiIR+tnm7IP0VkWQY/mB8GiGI6EyhAnlcFaFCXdQbb30oo564X1voWJ1w06ZNrWPQ9C0zjeVhlIF0MPwZNUKoTYPxT57c2aP90abnh/NH7SzS4ZD5ckf1Cpr5Yl+gMu0oVnzwhbouFy9cwPWumuAH59G33/tUs4gwthJyckWUD94CaJ6O5+GSFR9LzUplXddr0Ihg9EOFwENGkVlYwKI93NEh+tq1qCfz31wjjetWkZuKFfTa69cVN2qITjLo4i6h48QK3KyFq4XiLiFS7vp3TLSx4oX0ONRCwfwCZhitm15JK8G2+qPNsvmbnWplf+lyLHPfLUPEbkaAf4Lg81an0nvQJOnW9q54JjbuGm3/na0pqB/6SCdP+hxEHtzUkAKLHmaoX8HLHZMkpLPC/AfRUDRahthzS5H98ZN/6Ji6teyN2ugXPStR55MzexZ1TcuYId1Vw+j0mX90kebTzd9JlQqlNZ3TLanAgIGJ83e79qojMFLkEkrDNDVS23ftlcd7t5Xz5y/I1HnvSv3at0nzO6vLgUPHPcY2bhl34Ghqo5au/FhdXHEfThnTV+vvUJv30uy3ZdTA7p6xhb+DsYgxEoHQ2bR1p1QqV8J1aYlY1INbtRG69psUtYuo/xz0UAfNnsF7GCZnJmPGre8Gq0GSt8VTU+8+6OGOauqD5+GAUdOke7tG2r/XzXWNWBjFmIJXAmo/Iebg5o0WV4goWxeWwXn2otXqSNrjvqbSoFZF12fIBOOeSzZxh5SUrn3H6jHPnTxIKpUvGYzj529EKQFE8fASNxbFmFiiXx4emrWqlFXjEEyu4QCGVgDtWtbVGqkM6a+eYEYpogRPC/VkBw4eUxdN40plrVPBpNztG14sqE15efbbavJTqVxJXWDA6qtJG4aw69vjHn3hXLp0WVAo3n/kFGl2Zw01w8BnseGFli1r5qh9qaN9xMiJc6X5nTW0Bccvvx2VUZPmqznNjUWuv2ooGbOairfcpBPF73bu1UgCXvLFChdwzdBDGhLGEiIiCaVh/vDzARn7MnqAPij5rsuhjNBfcN7S96Vnp2YaVbZGqFwD8d8TtTc9j70cq0ZJiBDAHRKb9n57+Q3NaID5FPgh6tKtbSOmHNoGjOmTh8V2k/JqxPQ323+Sr777QW6+qbDrU9ITcnKFgIbzd4nihdQVEu+C3l1autrIDO/WQ0dPaGYW2pig5g6MkCrsrTYbLuAwYEH2H9p1QBRiQ9YD2ni42YnZyXM+5OIO+bcjxs+WVR9tkvvbN2ZvOydXyYXfwQMB1utIR8L/L373YzUQ6dWl+b82vLPUJQ1/h1XcUZPnS5UKpeK9oFyILd4pJ1Sn4nY+1vPHy/mHvQe01g7PLDi5QsAgovzynLcF/w6XL4hkCGQ0/EZ6GP4d9aIwzUDLCozVaF7xRgQEEx2s7iOlHi9hiDdvogWLC/sPHPFwA+/PtmzXOtE2/xo9cAzGJ7B05Xo59ftfV/XdQuQKDqX2CJXb+FmbniOlHBNB1Gnj3jSRTIwx9GR86vGucm2mjJ40sD5dW7qu/imh8eGtXtYsasXEpNI+oIiCIpKMLIXcObMl9JOu+HerkysWYTDmsIAwZvCD/zqSXtRIMRa0WK99ZUjAKwBRu6ce7+ZJjTZ+AHly59AFGrxHx7z4unot3F6tnC7UzFu6RiN/vH8Td2uFVNwh+jJs3GxNr5s+rr9r0pcSdwn4aV8EcGPjAfn9j/ulcvlS6s6HdLmEct+9/Z4bV3/sPXysdSreUug4Eq8QQJQYhfNIo0MPn72/HJJxUxbKc0/2lAzp08vICXOkbs0KcuftlTQ9E9E6rDrmzZ0jaqN2iR0bEIFPPT9X2rWo63HVxG8g8oIVXWRyzF3yvv4s23X8RxdNlQ0TK3O7uUNir0c0fP7y5ViZv3SNTp6xgIIFBXB59oUFUiBfLq1zio2N1TkHeoKiXYxdDELMYKEZbTysaXPRHnX3dv2xKDp07EytQzStdfA5vCdGTpirGR8t7qyhGQsYl0jZtEb3omFMOTkHu5OrvZ+lEcfoSXtvk9vlu10/a9NvtPEw0Sgn+43071jv1Q4t68vFS5d0YQaZWDmyZ5G3Vm2Q6/PnliPHTqm7ZvH/ZYPgXbtl225PCnakM0jO4w+JuMMDtefAiXL46EnNg+cDITkvaXTvK6Hcd4T/7Zs9lcctjTOx2r/qw00yfcFyTXHFy9nUqdgZJWTiEN2j6uqzg1hDiwS8WLDI0Ll1Q2nfsq6s/eRLrf20Gv2Y2gu4gZkeXG7jZT9ftJIYOXGOdG/XWKOZ1u3dNRtl8bvrNNqCSABSccqULKK9yhJKW4x2rnt/PSxPTZyr0WDrs8zaIgZpmZu37qI4/ncwYCEB9bDrNm6Vg4ePawohxBs2pIGZlEM4bA4ZO1Oj8oi21KhcVoUNWgO4Ieru7d5B5kHaNGk86YP4M+pDG9WtootcMAjp0uYuncshesL2MFcoWp1c7f0sIY6t7wjMP16YuUyfg0j5d/OGexV8cmTLoosrMI1C/R3+G5zeePsj2bPvt3j3L9yrYSJ13z0NpHG9qoy+BziAgi7ujFtmIbV77k5XwwAvBD8WGIGEct+9TQ5RMG4v4g1sb9HzKTh/bd+9V7q3b3zVwzEhEwc3T7ix2ohoAIQxzEFQ94k6UESRsXlbOMB3IAgzZcrg6hcRRBwiJUMfvU/T47Ch/qn/U1PULAQp1diQrgPzC6TrICrj9u37H/ar2IATbtc2d0md6uU9LWLAjOLY+whB6vSy1RtU3KFtgj3lENy2fb9HBj/cQWLj4jR1E+ZAiEwx6n6FKZ5d4155Qzq2aqCmKsZxc8mK9fLK6L5a447eZZigowyif6+2cnOJK03l3bhBDGNxqn6tisrLRNibNqiuESmzWSOfiJjCQbJg/tyuXswy7wIs6qEE4vc/z8iQMa9qywQsklrv37Rp08jhYyfl+ry5VFib94kbx1yg5xwycYccbX8b3TIDvUT8nD8C9tx3+2dNwXjhQvmkZcOarp9we2Ppz8Thoa4tXZ1KYuUFEYJVbSxaGbML4zSH1Uf0GkRqyStz31HXPow99DO7texNrryJTeQYbo/ggZodRADgQDq8X2ePIxqi8WitYKKhs95YLdUrlZGyJYu4kps5aaR3YVKzZPnHnhYxqMejOE54WGha8MS52ii+ZuWy4iu6zqj71SxRK4uoXa9OzT33KCLx6dOnldUfbZJvv/9ZBj/SUQ4ePqHPwx4dm7rO7t/XCESK5pCxM9RbwtSA4u8GPjNNPQJg8oOykp0//iL9HmwtWTNn0nvcrRvcMxe9s04+/2qHmr91at1QF0///vucMoMzNWrvzIYoKRa/Bj3cwdXcAhkvQRd3geyUnyGBYBCw5757+02s/kye+aZUq3izhvb//uesRgiKFMoXjEOIit/wZ+Jw4cIlWffZVjlw+Jin7hGF9m7cEKVbvvZzmfn6Svm/+++ROtXKxUv7QlH9vCVr5OmBV8QfxCDcJDH22jSv41qRjBo7pHQh9QbtJVCD8nivtrpqfSUV50OdKKLGBy/uh4dMlpzZs0r2bNdqWqebm9zaW8QgPdOfOEZdGlLpkMoEMwyY/rh1MymHqNNGby28LzBxNGn53qLuEDFg56ttgBtYohE3UtK/2fGT1h2jN1nJ4jdo6ibMpCDonhvaUyNV6P2JKFSZkoXVPbdEsUKudojEghZ6LiISfG/TOhIbFyvT5i3XqBPaxVyp554p5UoXlzP/nNVeenAPhlOzmzdEPBe+86E0qVdNzVbWrP9S2+dYFwG9LcSYtkVuzi7yNW4o7tx8R0XBuVtz3+2nYy90xsscqTgwwXj0gXtcnRJhZeXLxMEYi+TPm0vqVCsv67/YJrlzZJUHOzVzdbohanfwEv9hzwFtwgqHNDRShglLi4Y1PSuNGG/z31yrttjGaTMKbrkknQJe0CiS792lhZQtWVRXbCfPeFMni/nz5lQjhyb1q0ntqreo0+bw8XPkwfuaxUtxStIBRPiXMenxJY57/88N8plJ86VY4fxSuGA+baLcqlFtaVS3squfdRhz46cslAF92nsi7hgG1qh7xvTpZMK0xdqTEJbtN1yfR9uc4Nnnxg3PLkTZ13/xrUbQS910g5w/f1EXaNDPDdkJECW3/JtaHYhZF56Zbqh3h3kbHJZRr33u3HmtJx76aCdNOR8/ZZHA5RURKQgS1HbHxYlMenWJtLyrllSuwBZhuN9efW2FpE+fTjrfe6dnERALNNge6NAk3t+h9QRq4rnFJ0BxxxERtQTshc44Uax8o/AejdDdnA5hvejeTBzMqjZePkaYYHXtmcnzpW3zOzwv9agdPAGcGCLB2EoWL6SW7M++8JoWgpv+geD67OQFMqJ/V32hYzL54qy3tMdPjdvKuLZRKxZXJk5bopNEtFEwdTv2CSLGICbcSAODeHZjE3T7MPQnjq/LnV1rQju2qq+piFicAb++PVrHEzUBDO2o/4i1v1vzhjV00o1UzmF9O+l9+fV3P8qyVZ/E67cV9VD8nOCOH/bJ0hXrVfDC8RFZCjBFGj24h7aDgevhw93v9tTRIvqHqIpxJEV91diX35C+Pe7VZ6EbNnsUGFFP9FMdM6SHpqmbzdqzF66auH8hVtzcs9csLF+XK7suxKM1lr3/p90J1w1jKjHnSHGXGFr8bMQQwCQIffLwMjINlrFyiJc4XOhgpf3Njj3aswsPYW927G5ZacRFtZs41K15qwx6drpOvI1rH3igvxvSvW4rV0Lefu9TyZblWsFn3b6pE9+YGdKm+R06sUZql32VFvx+O3Rca6jwYkKNmekbBSGDFVy3prxiEcE+QUQt3tOT5kn/nm0EL/n3P94sGzZ9J+Oe7OVq4xVf4hj3INLQUb+IFDAIZzzbzAT7r//1A00dE6PmQG7frGYNqPsZP3WRNpmHDTtqofBcw9jDxLJQgTxux6WZCq++tlJ+2POrPPLAPZqC+fbqDfLsoAdkxQef6+Tb9CGzp1pjzCHq4uZeZaifNVkJ1hoycEPGh7Vn74gJc7TNjtudNbE4cPTE71rLvnzNRs3kQC0jNutcDm78yGgY/eJrWucO0zjM59wsjsGI4s71j+3oBIBVspdmvyWN7qjyb0+kKw2U0XwZq4347zdXfiLDH+ss116TUf8eltgPdGyiqSN4GMMVDPb3JvUkOknFPyucN1auIUKee2WhjB78gDarxobJ9ogJs+Wpx7vrnx8e+oI6SWIidE+T2tKkXlVXRqNM3j9ePngxI8KpDZb/+EvGDeults8//PyrXLh4SW4sUkBgZW9PD4Yx0LQFy+WZJx6QLNde44ahFu8c0b8MfaHMBNG8vK0pTJgIoS8XiunhhsjtCgFMprHQM/0ToAAAIABJREFUUu22Mhqhg9U/JjowsIHrnGkeDOECl8jK5UtqHyk0DHbrZo26o+ciUs5RB4qWMTPfWCmIuuf433Nv5ONdZd+vR2Ta/He1Zs/NPRkxznb99Iva1UOoPdTtbn1XQgQP79fFE5GztyvCQhZaUsB0CuMTkSrUVSHjwS0bIpmff7lD71HTY9aIYPAy2TF47qGJ93sfb9Y0WJhxIdrpZkdSjJEPP/1azXzQIgZRT+viDP59wKipWgdfouj18uaqDbJj917XR90p7tzydHHheUKovDT7bdn45Q6to8AKNlYaM12TUfoOf0lfUFgJgmEDJpZohjuwT/t/C3q3yLrPvpGRj3dzZTsPk4J51x2VNRIFVhC7BQtcJ93bN9IicayooXYAEyI0+UakAH2j3LahKTKcvrAwgFXqI8d/V9MBNPBG7RgMQ1Z88IXywUupVpVbJEe2zGpE8FC3lrJs1QZZ+cEXmq7p1p6gmNQgcpk2TWodPt5SmDApxAse9yTSNDEG0bwaUXg3b5gkos8b6hefeKiDpgUjHRNufYiyo7fbLaWKyv3tUasi8v7HW2TH7n3Sr2drV9RAJTQ24BY8e9FqTdXHAh947vhhvy5cwUwEEfm7G9eWm4per7b3OVF3bDFmSej3o/nfIYyRrmnaXuE+RuqheTegPYXpNYgeZXv2H9Q2H+wJemWxFI6uQ/t20nGGDYt8mLOgPzTuY8xdpi9YIRNH9NE2FG7drD17Y2Ji5OSpP2Xs0J5a325SrGHC1atLc0X0/rot+m+4n+2bP5+GaOJLcRdNV5PnchUBvKiRjhQXG+dpg4AX0soPv5DHHmyjK95ff/eDoC8Nan6QzoSX+rBxs+X/ut/t6qbUMAJB9A4pOBC/aGwLJj/tO3hV7QAcN2FpnCXzNeqs1uHuelIwvztqK9C3CFFiREia31lDJ9np0qbRCQxEMlYVkeqFJraI8m3a+r2uzj73ZE8dX3CQHDz6VRXR6P8Gbkgrxot/QO+2UqxwAVfd2aZXFCaDJoXJnraJSRBaAuDljXoe9JlyczQK42rjl9vl+elLBJMfpF6OHfKg/PzLYV1cyJMru6RLl0Ye6NBUEEWAiQ0MMbz1FcR4xmesdUHRPABNyuEvB45o3dj1+f7rP4YFQqTNYUEG/biwyDV38fuafu0WPv6uPSKcw56bpQtbqInFfQkxYurKrBEWOFU/hns20zW6yOpmsQKm9p695rlXt2YFaVinsmJHDR6yQZ7s20nHJcZqTKoY16bvgwl6Va5d/6VGNCHU0KLjzJmz8swLCyTvdTn0PeArJRPPyRmvr5Q/T5+Rfj3beBYTo/H5RnEXjVeV5+SXwNbtP2qOtkmFwMQR1s/58uSUbm3vklkLV7u6PsAKz4hjU6vj7QUEV9JBo1+VLq0bSoniBWXLN7tlzqLVMm5Yb9cUz4MTJjqwb8YqLBopI70Vq4p46UDw3tuktpr4WOtP0M9n8JhXpfO9DSVTpowamerdublcc00Gdau7MilP67o7GlHNa6/JoOduT1+6ePGyRtnRJBiTIDCGex+MHhBRcbMtNiYvsFhHlgJS5mAqhfrPe5vcrpGCSa8u1To8RKEQucMYQ+sE9Nu6s04lTbGGGLSmirlh8IHbF19/rymZcDt8YdQj/4o80VofCLx7m96ubriwt3eD62Og1x01oEjVRDR4/4HDKkRwX9rdqk1PWizaINr3f93voQuuBTIyE3C/mn6fxt37x70HdK4CUYcIM7I8etzXVBrUqujKd4NBBmE3YeoiuaN6Be2zaOoa8RzDYp+3DaZJqGvHOwIum9G8UdxF89XluXklgJcOes0gneuOGhU8trpIC9t34LCm1KE+APU+B4+c0L5S2NzeNwoM1m38RtZ8vMWTruqtbgAPUNTmIUXTrQ28rQMPkaWp895RMxBT5zTpqYelYP7cWnuH+gC4bBoRB5H44sxlMqxfZ4/zpptvZXv6kn0SBDaIELy7ZqPWSCFqeulybFSvygY6Hj7dvF0jeogIQ5BAxGz5ZpemzSEC/8qct6XjPQ0E6XMwxujappEsXr7O8/wLdD/R9jmN5i1YIQ1uv00NuRBJHz5uttzfvjEbdvu42HA4XPXhJmnboq6WMljdqvEVk56J1HOMN2x0rL4CEwsuiNChprPcv+0l8NyDKRxqZ01DdHwWi9FID4ZDM4Q0FrWwudGU6+f9B7VdDmr+0U924vQlglISb+LOpG9WKHujZthE+0ZxF+1XmOfnlcAVa+bXtV6sWsXS0uKumtrDzfoCwoQREQKk4OTJnUP7Rj3QvomrX+6YHF68dMmz6uXtBWStGzDFz6hLg4Axzn1uHZZIxcHLGAsLhh1e0HZHV6uRiFtZmfPGSxl1jBDDp//6RxdmYBVeqfyVnlCY1MDJD/Vk6IkHrv1GvKwpr4jkuSU92Ns4wWo2LOiRCtetXSNPKiEYoZZsyCMdPannaNXRe9AkzV4wfbjcPPbw/J86/115vHc7KVmsoHIsd3MxrxNDPBexMIiJNybibk/ZBAeYH2FRAc82LMggIux2p1t/95M15dI0Q4f5Fvq6IeUfEXbMQ2pUKqOLhEjtr121nCd1HYxhDATDqWiPSlk5YjEZNcdocQVR16pRLa8RTWuKMNLR9/16WHlG65yE4s7Nby+XnzsmhRB3x07+rs5dX237wdOUGmgGPjNNJzmm7geuX6/MeUejA250NLQPF/MCQuG3mQza0zbRIgApTajbw+fhvImXj9vdv8ASqcGo9xz4UHtPmheMRJAijNQcvIDWrv9Klq78WB03ETVA3Z5b0w5NlBhprYisY5IDFtt375PnXn5DXXBNDy2MtS+/2S2TZizV2lqk7bh1gzj+4NOvtVYMaZcThveW1euumAA91quN17FnejW6lZk5b6QcvjBzmYoTRAQgVpDCGn9yeU4mz1imzeXhRLp1+0/SulkdaX5nddfeq+CD/neIop87f1FGTpijde1IpeaWMIE9+w5qFOqpx7tpGiHaxIBdjuxZ5K1VG+T6/Lm1bQfqy24sWlDLIDZs/k76PnCv51lnX4hNeK/R+wnMQ0ZMmKumZahtRyQeC4Bw+IYbOto9RVvrBIq76B3PPLNEEMDqDwwsmt1ZXW9+mK4sfOcjnWSbtAcUN6OHFNLlIFKifeUnEHynz/yjk0Oz+mVPmUOqHAqgBz/cQVNwTDsKruCK1ugZM4KalcqqiQWEMNKWalQuo9Go7bv26oQSG1I1kerqVqFiIp3/172Vpg5i8gKHtC++2iFDH+0kt5a9UdZ/8a3W/VS5tbQuIOz66VeZv/R9Gdm/G/u7WW5oGK+gnYRxZzWLMPiztQ9XIM8At38G6Yeff/W9PD2gm74r8EwcNWmemteYHqFuZoToMVLnWjSsoe9WX4tTMPDCMw+OmkiHbd20TtRGVRIaD1jIQuscROAwF1n07pVUabxnryxyfSR79v2m2TB4r85Z/J7MeG2lNKlfTXvmIXIMbwEYJz33ZC9tQeHmzZoibOrekUaMyOfmrbvUBG3ckz2jyryM4s7NI57nHo8AVhrTpkmjK2XL124UrJ6Zppn44OqPNutDYHi/znLyj7+ifuUnscPD1DKalDmsjg0Z82o822t7rzIURadJHePaFW7UdMJEBU6k3/+wz9N+49eDR2XUpPnxolHoX7Z05SdaZ+GmtBszDtELD/co0pRgEvLjzwfUCRfmIFhswQv8gw1fa9Rk246ftLas4i0ltNAe/ZFSxaSSWW+sluqVykjZkkUSO7yj6vNwBkbvKLROwEQQf/bX+mXDpm8lffp0UqVCqajikNSTgZB74pnpmv5bocyN+nOYfKMVRZ1q5TV1GGM2deoYV5uwYCH02Rdf0wyZRx+496p6WCx0DXh6qmaAoH0M6pMRCX3iofaexVWkeiI1Efd63Zq3ukb4oYQEzsBwa21Sv6r8/ueZeO9Vs+g1rG9nOXzspPa5RCNvLH6lSZNao6duzfbA/Wifhxhjn9GDH1TRi/t1/ptr5fz5C5rWHy0bxV20XEmeR1AJIAUTq9tojYAHAITeE89O15TC28qVUMfDaF/5SSxQvEx+O3xM8ufNpf3ewAjizdoT6psdP+nqLNoAQKCgbgCssdqIgmgIazduiByPe2WhtGleR2ugFry5VvB3VvdHsHtTxV1n7fPmNqHizzAA6cAjxs/WSJSJbGJShNQbpL3CpAAprw8PmazRPlhmu7khtbVvFFLT0Tphypi+Plu/vPraChWBbu3D6OuZhGfX+CmL1PofogMbxt2gZ6fLEw93kCIF82odd/EiBaRp/erausOtzzjcv96MjjAWn5+2RLJny6wLNxAiMFwZ/eJrmkmD5yHS+hGFQn08Mhy0ZvSR+1wj8I4cP6WthrC4jHTqTq0b6rsBG/oK4v417PB3WKyBQIYZixsXAq33K9L2sXDatvkdOq6svgr4HNLWn540X9udNKxTScUeFg2x0G8azkfinITiLhKvGo855ARwg6OBKBquom8U7LGRHoe6i10//SLjpy6SaF/5SQpkrI49Muwladmwpr6E4AT5519wKZ0lTetX9fTxAWcUi784a5ncUCCvNiGlzbgIJtOY7CA1CRsmQJhEIpUOqZkUKlePTqRRY0KDBRiYqCx85z9Lf7zAR06Yq2lLiAzA3v7/2fsK8CiypusiECwQ3AkEt+DBLbgubsElOItbcA8sLLC4BPfg7hCcQJCF4O7uToT3PRV6dhgmAkRm5tb9n//59iU9M31P3+6+VXXqHFDFmtevGKRs9u+sf3P67FHv83Tu0g3O9hu797RKVImCuXSBs6HpvDnNNyzPVevlcWlUhYMQ4DJz4UZ6/vI1U+bQfzfJHUFJDrp07Q7B6w19VOm+mVaH5bmY63eh3QGqkEhaaT2z2oYbVM60qZNzsBw1alSdPx426SoqbSKJheda1bJFKFmSBPwegMfq6H5tdL6Bhp6g5rouwuO80RYChephvVroDM5Be523Yhu34MDHEmrpDx4/Jz8/f6aoow0gjk2s8DidcP1OCe7CFV75cnNHAC9reJGB646XiSanmzNbel0W2zDzgwoWXvqgRCCjptrQMIL4B+hzm3Ye4Wqn97+XqXihXNSmcVW6euMe7T/2Lyupger1lYh7HpF9xOdUHwiOIeSA7HSqFIlp3baDtG2PF1c8saYkUPlxheBePeR1jvtTkDAICAigqaO7se+gMdohqqN4mbeoX4n7pCAiojJ9Kah7DhtKtylLOKmAexObx79nepBt3NjUpXUdJZ9x+lihtwlKmnlyZCIIsGDdYaMY3dpa52GJarLmW3b5+p3vRJRUf9bhXkU1ZXjvVjrrFzz/xs9YSWMGtKO37z/whrtCqQJMyxzaswVXQmUEJgGhqAkWAoYmOoVqfOdWtdjce/GanXT4+DkqVjAn38MqK7kiwYIBejCGfq8xeraRfEYfHtSsMfDeBWMGPd3mVsWT4E6eEILATyAQmswP/JCePH/FmR/46P3Zqpaub+AnfspsD8WLeebijTSid2veAMIoGbQamMSnTZ2MhUQGuM0h51rlmOeOXqncOTKyQA1oJKlTJOHPxIwZXUd1MlswfvHE8ZL2uXyLJs7yoJNnr/A66tOxIVNegwtULKln4Beh448ho43qHBQL0ecJtTlsdjQPKfSHQg0XdOA0qZLx5hGBNLLhMr5HAH5a/d3cuf8JdCUoH4LiVLtqSX7GXb5+lz9giYpzoV0LoAoCB+toUSlzBjudgTwEkfQ9LFFdRqWqq0sdSSR8AxdJGQRyuA/rVi1JT1+8ZqEpUDKrlS/y7W9JuR8PyUKYeRtSWxEgwqcRxxiqmYb2Gprjcag6TZ2/jrq3rUcOWdPxe1bz6UUSEK0k+XNlprpVS5HP5ZscrKA3L0mi+OY43TA/Z/13KZLP67YFmshrInpIVB8/c4kZC76+/pzct7GJaRbsIgnuwny5yBdaMgLBZX4K5MnCFRUo9SHzE/A1gFZt8mSDZcjYQ3JXlYEXdlD0SgRz6FXp1b4Bb3D8/QNo7LTlbKyMyt279x+p/xh3ymifioo65uAsG3pVZAQa2AYXqGiiDoLVfwhAiOXVm3fUsUVNXpNadht9O707NuQNkVicBL1isHHGhjFXtgy078hpGtqrJQvSIBkDLzMkbdDHiL4WKEZmsJeqCkQcQDVEQKx5WKIXr/+YOUzz0pIMcp8GIoD+4iVrdrI5NzbWXVzqsHiIvj0RrGGCGkhA/Hv+Or8v0AKgysCzDOqi+w6fpjpVS3ElT/NIBYMBolP6yQX8Hf3GKph4h7QGsEdBsJsza3reYxi2Qmifx3Gwn5i2YD0lTZyA/Pz8WDE9X87MIf1EpP5dgrtIhV9+3NwRMMz8rN7syZkfLRjBS77PiJnk+mejIMUKzB2Dnz1/ZLhhoAxKFyT/8QLS72EEpjs8vdmTBlVA0DpBi1DVAkAf35ACldD2K2oeVCrQEA37wzR1OVSJYTgNWt2aLQd04gPor3VfvoXKFs+nU0D82TVuScejx7ht7/FUvGAuDlZgoKwJ2CCRhftU8xvEBn1Ij+ZKMRWMXWtUjoEbKpzABptwCEyBBqwlGQw/h40mqi9aMGhJa+hX5hJaew6InUH5tWXDykqLh4CNsHTdbmpcqxy3hCC5ALqmvoorxMyQRIUwEhKsEKkBzVV8LQN9Z/EuQMJP/z0Ke6eFHttplGsbFtfDng79793a1NX1iP7K+g7vz0hwF94Iy/dbLAKGmR9UpEAb0RfBwMMUVB1UpEAPQ88Ajilf0pFSJU9ssdiENLHrt+7T4tW7WMgBm23I+6N53ph9wt5Dp7g/b2DXpvy1KjbS62cRv34lnZS4YaBiiDs2lahMPX72kgMZ9FtolhXYDJl69jGkdfSzf8c9O33Beu5T0dTl8FJH1Qm2J1BW06cdhjZY/tnzMIfjsXbOXrzBVTuNhqnhAbl1ZLL1/SrxfEPv1ICuTZhOjXWmsjqk/jXGRlqjy2miIfp/h0DIyH8WU7VyRdg+QQbR8dOXaPPuI+T6Z5Mg+52M9cAbYqei+A96iNEeApNzrT9Re7f269yYMtin5AAFonG4p6NEIerZvgEVK+CgLF0YljkjJy3m6hyC38AebOI+vBoVi+v8Pw2tTkz1XpXgzlSvjJyX2SGA7OH2fcc5EwZ1JX0/lfi2cXjTiIeqvV0K8ti0j2pXLkmVyxRU9mGKCww6DeT9G9Yow5gZZrfxIEWAjIcs+smQaQS9tUyxvGa3PsL6hI0FKvq/gT6gmYs20v6jZ5gqjKZ6KCKC9gqqHTKU6F/x9Qs0y1VlgNoaM0Z0nQLaqs2e3DuGf0NPFKrEubKlV/q+xFrYdcCb149Gw9RfH0hkoadR3wf0oNc5Xc8KaHagD3dtU4ccsqjtKQjclq7dxQkF0PWNVcuB19hpy6hpnQq6zThsZPDcUznBEBy9H7ii5WHR6h3k5tqWQNsEjkjUoN9M630EE+TQcR+l/EE16jlUHzu1qBVoLTFlCdmlSMqK1KfPXSWwQEb2dWHcwKaBr+ow0KrTplTlVfDDPJEsOHbqAm3YcZjqVy9NyRInYLGkUf1cdNVNrV+7U4ualD2LPavjQvRME2ExFfAkuDOVKyHnYdYI4CUU+BLfTYs8dlDWTGm4Ulf/DyfOAmlmt41rl6PiBXOyRx6Mbru1qcelfhlEV2/e4967EX1a6zCB+ApUNIf0bEEpkiYKrKrUKK1cxSmo9WEYqOgfhxfUQa+zumQDgrpR/yyhfy9coymjuuooiQiYxw5sr+w6hJ/l8vV72RKhZ/v6warJAUOYAqvQ1xOcryDEGRas3M4UTFTn8DyDmTfuTagaLlv3nw2F1ueIirOqHm8hPd+R5MLmGqbyEPXRhDFAW29at+IPpt8hfZ8Kf8cm29VtNjWrW5Ep+1rVHcEJ2DG4V7EBnzhnFfXu0FC5tggEKlv2HGMfPFSlkNgDTROBHqpR2JdA6h8DWKIfFJU+Uav+7+558eotDRk/n9cPKu54Jq7ffojfq2i/QYKro+skThSC7lq3mhOVL5HfJN4PEtyp8BSUOYYrAngAIOvatnE1FhPAwxNUL2S+9fvvkGXEcfDzgYLV+w+BFguGmVw8QNCvgQof/q7KePDoGd158IQK58vOU9a83bQG8UClvgXUqWVNDkw0RUm8xNBXIBvH/1aKvhqk1nNhWOnDBujzZ1/uz0DAokL/neG9hCBl5MRFVLNyCapduUSwL2X0mSG5UL6Uo/LeeFhLSGTt2u9NuXNkoCMnfKh8qQLsafng0XMa9c9iroBqFETQh8dMXUqDujdXNokQ3HMctE0kGfC+OHP+Gpt692hfn5yK5OEgBUJe+tUoVd4Jwc0T1WNNjRTvBvRxN6lTnpOnGKAcou8MFZWg+hxVxBGVTFTZ9eX90Ve2Yceh75Qi9bHRjOZB8URQqNJAz//abQfIuWY5unX3Ie/txg5oR6lSJOHnXIlCOdm3F7TXTTsPU50qJenV2/eUzi5FpO5JJLhTaZXKXMMFAQQZkKv/a/py3vQlS5KQFqzcxma18MNDo3wRRwfe1KBXZfTkJSzeANNbwwHlSHglIVukCT6Ey0mbwZdClABZR7f+bbiaAglxBMdQqrKJFZOWrdvDATRwNuZ7BL9B1SiH2mXFZhE9FcAO/U8YhnjiJQ/Tb6wzlWiZwAIJgbXbDrKaLeYfGhomNkDYUOonbEKijZnBbfZbp4jKMdaaXYokrJqJhAECE9CUIEuPhIFhUgHJr7sPn0b65ue3Jh7GH8Z7ASI+SRLG52oA6HGwhMF7ZeBYdyqUNzDh9ejpCxrRp5XyUvZYd1Bq7dWhAQvQbN3jRUe8fWhw92a6BE1IfY5hfAnN4uuQKMD+A0Gwtv8Ijek57mEITaEKD0El1cb9R894rxErRnSq4FSA36nG3gfABf++ZM0u3qfAciKy/PEkuFNtlcp8ww0B8P3xgkE/kyaYopXx8e+g3KBJF3RMVJ6MZcBwXM+h0+mPCsVYmS5qVKtwO19T/+Kd+70prk0sKuKYg08VL+u5y7ZQu6bVuRcIPnhdW9cJ8mUD5StVKYeQXcemED1RkHnWqk5liuflLGNoXujYSN5/+Mwiq6II/M9fucUJl9CY+moiNC6Nq33nlTdo3Fxq37S6cpSvoJ4doBei1w79nFrvDnqPp8xbx0EJsDaVzY8pPf+ASZeBk1nxEd6L6P/EPdpv9GxqUb+SrhoFcSn4caGqp3IvHq6dvuIvqp72dsl171RDoRX0H3udukgXrt6ignmyWeQz7VfWs6EtjLE1hXeH25QlVK1cUSqYV8R+gLP2PmhWr+J3AkjavyPRZWVlRZ5HzjCbITKqnRLc/codIZ8RBH4CAWwkD584x7Qb3PCg14zp35YNqQ0Hqit/TVvOSpp4eYmx8n8IwbDV1W0OUwjbNK7GJrdBbXCQaVSdcojgdsi4+ZzJRkU4c/rUNLRXCxZ2QE/U0+ev2dgboipYn/NWbKWE8W3ZkxHZcKghYq1qqpI/seQt7lAIraCioi+TrU8LU6EHLzQXFfL1oMchOQUWA55hoLJqSQVT2vyEZj7hfQzeDfAomzRnlY6GqdGjQf9Cb49+NQqy/xBn6dOpEVcERJE08Arh/nz05AX32OFe1Bda+eLryz1m6CVzzJ2FKcTvPnykfp0bKW/ZASrhkHHzqN+fjSl9mhRGl7t+hcomdixCoKxSu4gxUICJ16kL3yVZjAXKSHZFoUD2gj7DISJaICS4C++nt3y/IPANAbzI8VLBgzGoDJm2EYIoAeiZ8ePFJVsx8GYa3cJVO2jPwZPct5Mjy4+UVv2FpjLlUB8HrLnHT1+Q29Sl1Nq5Kled9OlKdimT0PyV2+jwcR/q3q4e94siqMuTPSPTODVKrMo3sebpVu8PJ12WVjOphkFwymSJacrcNdyDli5NCqaKqSwpjt7ZCbNXcYUJFahM6VPT6H4uLDpgKLSibX5UpHoh4YJADRtFjYapf58ZVqO0IObu/Se8qQRNThRJAxGDYAiqwwiU49vaEPrgQbfGfQgFZtyboO+XKJSLAr4GMGUdyQexnfi+Amr4nDesUOGdgEQEdAPw3IssyqEpvo+M2RIhqFu8aidTOqFYffn6HbJPnZyTqrFjxQzXaUhwF67wypcLAqFHAAEJegf0e3rwAl+8ZifL2BcrmJOca5YNFY0s9L9q+keCYggxhni2cYKlYWozCQ3l0PRnHbZniJcMKnTaxkYTqbly4x6r9I12baMTv0Dg0rn/JKaTgMKp+kDACzlsBG5Qv7WKavWdV95pn6s0f8U2NrlFEgJ9oUUdHahmpeJKitRo6wVVO9CiK5UuyEmFkDwZVVxnwSmSwloHATKSBUgGomrXd9QsXY+oKJL+uGKQiNl7OJC66tq5EdPRoQLZvlkNOurtQxeu3CaXxlVpp+cJyp8rC0GNdO6yrVS0gAPlzCqWHfqIGlaifH39uQqPyigYHaAcOtcqS1AAV50ijPcr9AGgmKn1GgNLVJDRGzqibysqkj8HQTF45qINLLxX0alAuD7yJLgLV3jlywWB0CGgWSVgQ62pG8JKAS9zKGvWrVqKoOwHcZZB3QIbdVUYeMGgVwKZ2ApOjiG+RLQXkkY5PH/5FvUbNYsePX3JG/MOzWoo2RCurRVkYpeu202Na5Vjf6PZSzbxCwfrTqOKoJKwZfdRpnCGd3bRXNYwkiwzF2+kbXu9OMlgHS0qTR3djXv2ELT0GTGDbU0gyY5kBOwmUBmICPqNOWAY1ObH2LmD2rlmywHGMnN6u0hVnItMbLVq1I3bDzixgM10h+Y1qHqFonT3wVNRJA3i4uAd4OcfwPcoKsOwf0GFHf3uSPyhX9vn0g2aPLIrPXzynBNZiRLEo+RJE7JVADbhorxMPyRjkHzee+g0+16iYqf1dXdvW095+wQ83456XyCHrPY6ATMkBUf+s5gcsqaR3wEGAAAgAElEQVSjqzfucWsE7Cj+PX+d7j18wr694TkkuAtPdOW7BYFQIgARluETFtKw3q10UuHoI7hy/S6/mLSeHmzG8RKqXqFYKL9ZrcP0KYfwi4J1QvmS+cmpaF7O6EIdEZVRVYLjkK6+IfVLqp7BI4YX9OLVO1kZskIpR4IEO2jTUDscNmEh9/yULpZXpxIJs2Dvs5cpR2Z7tvhQtTfP2OYnKKQhTIX+2tVb9pN1tGg0oGsTnYFwSOvZ0v6OQOXh4+esLgqBGgjSaLL0wSmSwmYHCTHQrlVOMACribNWUfq0KdkSAUEbMMV9G+3/YmV4P1QtV4RKFs7FPbWDx82n5vUr6qxOkGBFP3Ilp4LK3burN++nL75+5FyzDHv2ggIMOiGq8BhacNe3cyOljc+DeuagajxuxgqubOLeRZvN3zM92Opk4rBOrKSO9wOYHzmzpuckfli+HyS4s7S3gczHLBHAC2fjziPkvnQz/dm6DvcKwKMHmUStkodjZi/ZzKX/yFBfMnVgDX3xcL7ot7hx+yEN6t6UhUTQk5Etsz0lS5xAGsOJeBM9df466tamLqvNwfgcm0l94RD96w6/LVQQoOqnmn2C4fpHbxkMkvt0dOZkwdWb98ht8lIa0bc1JYgXh9ymLOPNJJRzoRgJTy70AUliIegnCTZEoLbCGB0DTIVL124zxtbW0Uz9ERQh5xeSIikqVKBsgoKIqkq8uDZKY4eqO2yKENDVqFiMAz2IhxhWonDxkLj5+OkzV1UQvGzadYSePHsZ5PMwQi54JP+IIRsGFEz829qtB7kCiuTzpy++tHqzJwsAwbajbrVSyrWPGF4mbb+GhEztKiU4yYIe+Jt3HnCy6u9Zq+jVm3ecdECQd+f+Y34/4G9ggyC5D1N5UGB/ZUhw9yuoyWcEgXBCAJQkBClRo0alwX/N45tb83BDL1R/t9nUr3NjypLBLpzOwHy/Vp9yiI0gMrMBX79y8AIltYFdm+oomdIYHnid8QK6ePU2ewaC/vXuwyeaOqqrrv9OfzVo8uJ7D5/m6rKqPaAaJlpCZv32g9SldR2KamXFPWYI7rDpQVUAwhc1KhTjjCw2jnhRS2LG+DMG1c/uQ6bxPYq1hsQWelNWbNhDQ3u2/OVNjvk+0YyfeUiKpNgYwgYld/aMLOB11Ps8DenRXGcpY2l4hGY+2FRfv32fe6BKFs7Na8mwEoX3R5+RM9mKAlUVZnlMWsyiUrABQKIG72YkdVKnSKoMdRMJlyVrdlLlMoXYexFDv382vm0cXfsIApWzF27w8VhzuH9VHqh4Dv17AfuA1q9emhL+P6iDvdPO/Sdoz8FTutYHzTILiQisvY07D5NdyqS/pVQtwZ3KK0/mbrIIaNmyB4+fU6cWtVjFcPSUJWSXIim1b179h94zvLygxASTUviaaZlvk51gOJ/YgpXbuacMohYwVkazs23c2NSqYRWd51twjeHo1UOwU7lsoRD7/MJ5KhH29ajKgZqERm9jVK5Dx89xtnZYrxZcCVizZT8dOu5Dfw1sp/R6A90GwfHNOw+5dxHmwBCpwWbo6fNX3KfXvH4lroiiKoCgBfczNt5xYsdiLLFpnL14E2WwT6mkiI3Wn4Ksf5WyhbiqfuDYWTZc7tK6NgfEmlcZnnOguDpkTa/MBtvwIRCUImmsWDFYCVITTMJ9DKo1VEuhViojEIFFq3Zw1aRji5r8fDcUD8ExwBFB4Ju3Hwg014HdmjI9cdQ/i6lPJ2dlqYh4Vk1fsJ6ZMLDJWbFh7w82MXhXYO+CPnfVBxJVx05d4D5iGKCjFxstOJVKF6IShXLq4MFxvn7+dOj4WeoxdDp1bF6DWjas8su2ExLcqb7yZP4miwBu9i17jnFgguZ6NONiY4gXtf7QXkxQVwN3Gw8SNN47FcmjbL8FNtWD/ppH+XJmouoVi9HSNbspQfw4HNyF1BiuVahQMdVXvjLZhRIBJ4astqvbbGpWtyILXWA8fvqShoyfz5seLaMbAadi8j+BTPdf05ZR49rlueqOHjJksj02eXJVFAmYwePmUbRo0bhC1dWlDvfwocI8ul8bSppYDbEk/Qup9ZGBGly3mhMHbaAMYxM+sq8LaV5l8AaFnD0sURCsADuVRX8MFUnBSMD7QixMgn9MYL1BSAqiKxiGSq76OIImh3s6qlUUQm98ujQpeX2q2o8HBhHsEHq2a8D34LAJC6hhjTK69hHgiT0JEs64hxd4bGeMRawmcE1CPM+QlaWtVqwzKJJCxRX93WDJjOrn8kt2WBLcmfxWQU5QEAgegUCj4EDhkDLF83G1AI3hf7aqxVUEVQc2znsOneI+slzZ0lOzehU5+x9SYzj6o5CNhMktqnvb9nhRwbzZCAItqg5jht2w7Tji7cMmy6g+oTJ1+95jSps6GQcrKgs5oF8C92H7ptV1TfLI/t958JhGTVpCfTo2pFzZM3BVZeLsVbwJ6tm+vpJVO+2e0kSlsMHJ65CRjp68wLR0iF2gdxb4QdABlRZszmcu3MgqfWVL5FP1tvxu3kgAagIhpYrkDhYT6Z39Dx7DShSSLzA9RwVZ3wpG38wba1T68YjvyaF/z+ekKe5F/YH37soNe7lfEX3GsIuBcqTqFjEIfPE8+/zZ9wcWlv4a+132lQR38loQBCwAAfQSQJkJPHdt02gTO5aYjOpd29A0hiOYg+JmdOtoLGl89uJ1mrN0M412batTMbWA5fJTU0CmduAYd+rftYmOiqT1f/bu0JAyZ7Bjo9ZVmz2ZcnL5+l3KnSMjUxRV9T9CEgGS61BCg/k5BBwypktNk2avIn2VQ1yIvYdO0brth9jo+3df6D91YU3wYE3JEJU59OBBrAEbaVjCQBxEv9cYFVGtrxZrLk7smJTWLrnSa+7ICR8q4ugQ7HNfYybA4xKy/7myp1dayRW3gT51FYkWJGegqqxVhfWZC455shjtxzPB2ylCTglBHPu5dW2io/5CjKbnsOns0aipayJ5M37GSjaTR8uEykOzOTl97goVzp+DRVW0PlC8Nwvkyfrb8Ehw99sQyhcIApGHAKpMaGgGXenk2Ss0ctIifngiwENGcqenN63avI8Sxrel1s6B2TVVKyohNYZDrlh/nLt0k7oPmUqF8majAV2b/jL3PfJWR9j9MqrDCHixdrTMI/rH0LPifeYy++WNGdCOKYVYd2OnLqOShfPoegqwUT/ifZ7XoCqqh5qMvefRf9kgOXXKJNR35CxWftREkkDtQpUAIg7aJijsrpr5fhMoXb5+fqzIik2h25SlNLx3qx8sEfDMg3gIevWwYXr5+h0N6dlcaMLBXPrA3tkDhMQM7mvcu7DW0ejW5rtqwubMD3qdow07DtHQni04WNGUIa/cuMtVKAxj/Xi/qmoYNmcded+C5/2W3cdoxsINrJAJxUewOHYd8GZWhybvD+rr2GnLOGhGEkvM44mQNHjw+BllSp+aDh/3+QGz37mqEtz9DnryWUEgkhEwzJqhenL3/hPq4lKHbRMgv47sGcZk9zXkXLOsvMS/XTNDOo5h0AuKxMqNezmrvXn3Uea+Z0qXOpKveOT/vL6XIFQzYdSqCWFoZ4d1CPVI9Dse8jpHa7YeoGrlCitNO0SvxYAx7iyzjmDPUMTBWJUTfY2JEtoqW43CeuJK09RlTK3Wp3RpnoxIGGiBCSoIHpv2cSAoAiI/PiuM9c7iKE2s5sLVW1QwTzbun1LVyBvvhWVrd9PqLQdYLArVO9gSIWhBAtBYP96JMxcpZswYnAhUdSAhg4Rf7FgxaOd+b2a99GrfQJcQhD3H/YfPWEEYgmXdBk+hMsXykV2qpGwXkyp5YlWh43ljXWGPhnWG1obfHRLc/S6C8nlBIBIRwIto084jTCNBKR+UEvSpJEuSkBX7Rru20cnao3qyavN+FsBQ3aMMl0y/MdxQxMJwE4QMd9SoVkpvsrVljs320xev+WVszHMLFRRUo2pVLsl+jXOXb+FEQ59ODXW2AJF4y0TqTyP4gNoeqHO4B0+du8LWCaBtGg4Ef+NnruTNdkg9VJE6qQj48UCVwiX08vUbphJCPRNVO/hq6VcH8DxEhQBJB9AOMUDlNBShioBTNsmfMNY7i95Q3K9gdTjmzkKgdkLJFT3HKovVYC0hYJm5aINOGTKofjxUPwP9zEqa5HWP6JPCuxXCIBB2g8gberNhEzN2QDtKmTwR94aWK+nIxt1epy6S1+kL1KtDQ2aHqLo3wfMeWCz8v5IrfGd/1+5KgruIXvXye4JAOCCADfXVm/cpScJ4LGaxZM0uVukDf1urSKH/ZzUHd82YCoCHLagnUAOT8R8CWkXl2s373POj0UoEox8RgIQ9DIKLF8zFAYjm17N933H6a2B7ptZBwKZlw0rkc+kmPXr6kvp2clb2BQ4Eca+iRwy+d9jcBKXIChoxaNYNqpdmmrXqA2vr6fPX9Obte7aMAA0sedKEP2yoYUmBCimeg4kSxKPzl2/S8N4tlffcMtY7q1Gsl67dzXR+KJEGfA1g2iH6Z8Oi98ec1y0CvKPeF8ghqz33YBvrx9OSMCUK5tJVkA3VOM0Zg189d1jE/D3Tg/YdPs3PuZ7tG1COLPZGzePxG1Cxhv0JKleqBni/irWxz0lwF5ZoyncJAiaCADKJCeLHpXrVAn1m8LLBCxtS4xB4wH8fPuHDlSjIjPfv0oSrLKr24+lfthev3lK/UbP4ZRRc9gwVrLuQxrZLoSyFCbhhMz184kJKmyo5By6gbYLOlD5tSgIVB5UU9KoEJ64CLC9eu8NUOlXwBE0TmIDGZGygejzwr7nUs119vm9xD2/ccZi++PpRnaqldDLuJvLIifDTQG/Upp2HdUbAOAEExEPGzaNC+bJT7Sol+HmG3lnNVFnlShTw0e+dxf/GGus/Zg61b1aDjnr70IUrt8mlcVXa6XmC8ufKwkql67Yd5L5uKDGrPgz78bQ15zZlCbc8oPoJujCCGviqdmldh4UyZAQioFGpoXqr32OMf+8zYia9fvue12jSRPHZSxCevTJ+DQEJ7n4NN/mUIGDSCEBo5R/3NRy0QZ0PL2hI+o8d2I4NRzX/KKhWQRYbZsGgcIpfGRGUviDS0KNd/WBfLujJQ4V0UPdmLPUcK+b3/oMmvUDC+OQQeFy5fpdNWDUaHIK8IePm0/A+rYzSDnEKoD2BAjZmylJ+qaPanD5tCt4UqZ69hZDI2GnLueoUhaLwf2Og8hk/XpwwvoLm93Vs/L5kM/17/hr34kGk5uPHzzRtwXquGmuKfKB0QpAFan6ovoBaB/qXVOSJadWgu4KhAAYHNtlQefW5dIMmj+zKvXidB/xDAQEBHODVqVqSqpYtrCx2hv14CD6AYX83d74v377/yHREVNtrVy1pNKF15/5junTtLpUpnlc5mr+heTyeOoYCXejVPu1zjembieLb8jvBxiamclj97hNZgrvfRVA+LwiYIAJ4YPpcvkUTZ3lwb0rpYnnZWwsqVeivQG+A1scDuk7n/pPo4ZPn9DXgK9WoVJz9uVTeQKIXCpseVEga1Sr7wxXmqsrYQOqXlZUVeR45wxRY6bn4DyrQDg2pwYZAwhevx9BpZJcyqU56HPTibXuPcSZcZW9B0IIXr9lJ1coVYQr1HxWKUu3KJcgqqpWo4H5bSJoiKXrvICl+8uxlTl4hMaMNVFvWbTvA6ytKFCvefJcv5ci0Q9UHgpWJs1ZxlR1y7BBR0ewoUFGGIXqSRPGYOgxKLCjYoNjh3lR5ADerKFaMl+bPmCtbBtp35DQN7dWSBZMMB3C9/+gZLfTYzpX4oOjYloyrMbqqvkBXmlRJefo4bu3WgzRv+VaugIL+DyaNsItCvzokuAs9VnKkIGD2CIDXjs0OpP1RadL6y5Ddhqx9gH8AzVy8kT59+sKqVqr6lGlVJfSfGGJgTOUQ2VtUV/BSwsvKLkUS5Y28sdZ2HzxJfTs1YrEBY0Pzy0uaOAE9ffaKhvVuyX1Sx05doAK5syjt+4b+nvZ9J3DgC1ygFukfECAquME8hX0u36QFK7ez3ycSWRALgf1EgxqlWanVmEkw7lmVn3OvXr/joA1VpxoVi3GgB4EffVVI7f6FIBBEWYb1bqU0U0F/CV68epva9h7PfcfBVdVBPUeSBubn4wZ1oEL5sjFtGFTi7fu8mPYaL65a/m9au0jm9HY6GjWwxX06fsYKmubWnQXhsD8BuwhKw78rNGL2m7hQTkCCu1ACJYcJAuaOAOhIeEA2qVOesme25+kYy5rhwbr38Cka2LWpMp5kP3Nt0UOmL42tZRph5A1vH/T7XL5+h+xTJyf0Fqja56P5LG7b50WDujVjDzz9YUjHQTX0/YdPvHls2aCy0ka32HBPmO1Bvr7+1LN9fV1wDCEWUcEN+m7FmoM4yK793pQ7RwZWfixfqgBX1d+/h4qrO7k0rqbr98GaGzRuLjMVtGfizzwLLOVY0KOv377PZtQlC+cmJFtQ4QR1EEGxNlCNRyILtGlQNrEeMVRUJMXz6+zFGzR8wsJgaZgadm/ffWDWTMG82ej46YtMI8a6g/8nnnma/5ulrKnQzANrafm6PdS4dnnd8x5924PHzecWEbCOXBpVpWIFHch96Ra2TQAtWEbICEhwFzJGcoQgYJEIaFkzZMY0iggerJAprlquiE79EIakEA6Bx09QFRiLBMjIpICZPlVJE6DBpggKfSP6tmKp9q9fiSW0M9inoopOBVSB56fmib7QcTNW0GjXtixdbxjsffnix7QnlWTs9TeMED4CDVO/N8wY1VVfBReVKtiiwA5FZSEH9I4ZVtCBCyib+uI++tYAWLwikBR4C6PqPnH2apoyqovO2xNJrf5uc6h/l8ZsDaC6IikSeehPDIqGafgwBKZ7DwXSNtFPjOAYvnAI+No3q/6dwMhPPUgt7GD9PlA8w7buOUbw8/Xz82clzczpU7NJOiiuqPhpvbUWBsNvT0eCu9+GUL5AEDBPBCCkAjlxGP5qD0j9F9DXrwE0ac4aVkN0yJqOhQtKFc1DjWuXU5bGZCiNjSuPfgAYeQOjqzfukb9/ALVyrkL/nr9O9x4+YSqJjO8RYGPqKUspZ7b0uj5Fwyqyqr6M2LQgyZLRPtUP6rUhqeBOX7Be58klyrf/rTlNRRMBsybvD0owKvAQE0HiCoyFqfPXcZUP1ERVVFsNn02aomGVsoVo866j5FQ0D/umLli5jZWWUclDVU91RVIkYpDEC43ZO9Za7+EzOLGgqURq9H6su6Z1K/AaRA+36vctnn0jJy3+jmGENYl+2iKOOWjqvPVskI7eTyRV9cVrVBarMbyPJbiTXZcgoDACCEysraMxAoYyxchqz1m6mSaPDMzeYoM0ZupSqlOlpPhu6a0Z4IIKFIJevKA1f58z56/RxGGdWB4bqpq7D3hTsYI5WYxA9Qoo+snmr9xGbq5tObEQVLA3YZYHje7XRrKz39ZbcCq4qNpNnruWkzUaBfb6rfsE0ZqSRXIrm5DREjBjpi6jdGlSUP0/nFiURj8QfvMWlDl3KlE4NyWMH5fWbDnA96jrn/8Zed+485Bs48S2+HtXv8IJKiFsEV68fkulCufmZ9m5izdCVCSFkBIq7qEJfFR4/eJdCoEk/T52VEKH/b2ARbvQ271s7W4qXigXJxdU7gHFekDQNmXeWmYgFMibVYcHcDxx5jIN6t6U2x20QLBsifxMDVZZrEaCOxWeJDJHQeAXEPDY5EkPHj3jPjG8nEG7Ab8dhtSZM9iRc80ytGLDXkqfJqVQDfXwRQYWkuzYDGreWuhhuXnnAYthHPi/kh8w7NWhAaH5fv6KbSz4AMqmqgP4vP/wUSeYgpf5otU7dMEecAE9B/1lvTs60+s377iXCnLtbRpXU5ZyGJQKboL4tjTqn8VUolBOXY+UsYBZ1fWGeaOPEWJR2/Z6UTzbOOwTOHV0N0qeJMEPfozwIBw0di61dq5CDlnT81pEla9Z3Yo6o2pLxRJ0QdyfQfkvYoMdnCIp+kRd3WZz36y+CTrwf/7yNVdFVatOIWB+9OQFdWpRk2nWxloiUN2DRyOoh2JJRJwkdV+2hfvY8b4s4ujA4kgIfvM6ZNLdfkhqQeV7gcf2H8RqLPUeDc28pHIXGpTkGEFAAQT0ZYqxmXGbvJRcuzRmfyPNVgH//s+ILpzBhkz74ePnpBpFxGpeQ/9ewCqZ9auXpoQJbCmuTSzexECddP6KrewxCD88BDJ4IUHaXgZxIgEZbOBRvGBOHSSoJCPBgGpVHodM1KxuBcqbM5PyWW1jawYU1rnLt9KIvq353tSy30jYDOvVgoNorLste44xDTGojbsK6xG0afQuggKHflhjAkn4N1AP4dEI5eCOrhMpa6Y0XEWOYxNLBZiCnGNIiqQI/tCLBiud5y/ekHOtstSwemlavmEv95hBlVm1yhQqTPBf9Ll0k8YMaEdPnr3k3m23/m10lWD0ziJJiPcE7mG8jyEChHtVY9eovPBAuYTXJ4Rn4FepP4ISq1EZNwnuVL5bZO6CQBAIaFl/0B20DTf+DZsevLT7jZ7NnPe6VUsRXvYwSYciIoIXVQfwgYQ/KF0VnApQlTKFuCcDlBsoaeJvaKZHpQD/Dl9BZCUxypd0pFTJE6sKHRuYW0cLpAdfuXGXq3QI6jB6d2ho8dWS373woLmiqj6iT2veDEIFEtUTrdKEzU//Me6EIBDiNUIPDkTcmECSZo6Ov6NK8ODRc67apU2djKsJsPaAebWqIzhFUg0rVJ9AUUe1DkELqqXHz1yi6W7dlFYl9fXzpy9fvtCwvxfqRMuwjkDtR+Udlc7qFYpy/+ffM1dSrFgx6OPHz9zDjeSXakGx/j2mqWjWrFT8uyQgjjEmVqNyAguYSHCn6hNa5i0IhIAAhFRQjUJwByNl27g2TIcDxeTK9btcAdCU/CD0kDxpQqpeoZjg+g0ByITD3Lx1oypseIsAGBLYCPAgToOXOap8ECvw2LSPXJyrfhfEgCIL0RuV+qUw31WbPKlK2cJMvXFftjnEdQUKmTE/QpUWIjY+blOWMfUL1airN+9xH6N2j0JtDj2grp0bUcDXr2wQnC9nJqWDFC24O+p9gRyy2uuqAfoqrjDw1u/Ne/T0JUWLaqV0Eku7rwwVSeHBaEzQR0sU4u+4v2HFg/tb1UAFtFdYdIBmCK9ZDH3/RSQQeg2fQaP6ufAzEMePnbqMyhTPz7RrjPOXb7EaMzxBkYBIqUhiED3EsEmAIrVj7iwsYgZ/RkOxGpWe/UHNVYI7WQWCgCAQJAKgzKG6tHP/CWrVsApvBpHFhrqXxnvXes6wEapdpSS/wBHUtGxYmSWfVR1asze8eZrXq8TS7FPmruEKJ9Q1YT9Rqkhuhgd/mzZ/PQ3t1UJHyRk3fQUbuGo2FSriCIoXNpHBKY6iMoBeDNh3QNRGVbEaBLkPHj+jsxducP/iiD6tWAhJM4pHBVRlL7fQ3D+GfYow8sZ9OG5wB654yggaAQTFhoI+OFq/nxY0OTAWYJKuWt9dUMjpV9kd82ShCTM9OAGDvsYKpRw5EIaFwq27D5nievi4DycKOzSvwYwHzyOnqXvb+spQrRHsnjp3hV6+fseJrI07Dv8gViP3qVTuZA0IAoLATyAAoYHBf81jFSuYsGJom8d+nRtTOrvkLHG/9/BpoX8RcfUEfU5QHf382ZeG9GzOUvWofqJ3QDM4f/z0JY2dtowGdW/GFQRsKtG3gmMgIqLqQOC2w/MEi9EEl+nHCx8boAUe27gvSlubKuKGIO/xs5dM/8WAKAHoYCGp8IFuh340lRMy6ClevXk/NaxRhtUekYQplDc7wRZARvAI7NzvTdbWUal00by6AxG49Bk587tElrFvQYIQisL2dil01SlV8EYv2b4jZzgxBTGav6YtY1Nv+zTJOZDD/evv708dmtek3DkycB8yWB1De7bgyhUqotv2eLE5erJv97wq2GGehmI1Ks09uLlK5U5WgiAgCIQaAc2b58Hj59SpRS32eBs9ZQnZpUhK7ZtX5ywtKF8QcUCWds2W/XTouA/9NbCdThkx1D9moQdu3Pkt09iuvm6GW/d40RFvHxrcvRn3AWlG8ujB+Pzlyw8N5BYKjdFpvf/wiWLEsP4huENVecueo4S/FyvgQBnSpuIsNnob+3RyVpb2pQ8iEi/jpi9nxdGQKk/oWzlw7Cyr9akc4Gn4IZGFjTSqxjmzplPplguzuSIxc9DrLD/XNAq/4ZdDjApMDzwX0TMKtgIGEmNffP2UErDBs3/irFUcqGmsDjznDhz7l59xsOOAEEuH5tVp1ab95FyjDL37+JGTh6Nd24Z4j4fZhTWhLzIUqwnpOWdCpx6upyLBXbjCK18uCFgeAlo1Ci8ZPFjR7A2aJipT+iIOmDkqUkPGz6eB3ZqKvPO3pQAKJvzbBnZrxi9j+B/1HTWL+nVuxA31+s3hoOJAXKR/lybK0G5Cc8dgE4T+HmwMSxXJQ/sOn6bHT1+wAiKEabAhx9qDomubRtXEJy8EUEF97TNiJr1++56pXkkTxecAWWXhEECGZBV6Y51rlWMDb9gnyAg9AujFjhEjOivdBkXDhOhK7xEz6cWrN7pqFI4FJRu9ZXguqqR6qPWVoXpXpnheriBj4F2LpF/ZEvl0difnLt2k7kOmUqG82WhAV3i/xQj9xbGwI8FOQD+s0H0DL6wEdxa2wGU6gkBkIYCXMQxu9YVW9CtS3G/x+DndffiUldRU7Y1C9fPwCR9WQ7OysmJFOdAOK5UuSE+fv6bB4+ZRl9a1uT8Km8u9h08FWYlCcAOjYENp6MhaAxH1u6DPoc+uT0dnHQUTPT+jJy+l0a5tyC5lEqYzzVu+ldDzmD2TPQvbwHdQxvcIYD0CK02mPqqVFZ32uUYpkyeiRPFt2arCxiamspVQUH5hcq4Zw8v6CT0CsHwZOHYuJU2cgLq61DH6zNd68kA7dF+6mVxPRXkAACAASURBVCqXKUS5c2Tk3u6+nZy5b1S1gWQL6IagCEOAxqVRVe57B/Ucglz6QiwrN+6lwvmy0+bdR1mERUW8VFsfoZmvBHehQUmOEQQEgWARAP1r4Bh36t+1CQduGPpCDvZ2yWnSnDUEBU70CUAtslTRPNxnoKpqmqGPkeEmG7gYCorc+39gDI8u9ASlSZWMK1MfPnyitk3/UApHYAfhgY+fv1CHZjUoYYK4LHyhCdBcu3WfPZFA84LQD/fjrdzGwhgqW04Yu4lRSR4+YeF3lDjgC3o1gmPbuLGZft2zfQOmhklmXF4GP4OA1gOK+9DwWa/5k0GIS6MhalV59CYjqJH1Riwq5Tp6DnVuVYtyZ8/A8BvanaDiHjWqlVLvgZ9Zh6odK8Gdaldc5isIhBMCeLlEt47GL2PDQAWKVugLmDyyC2cW4eszZupSqlOlJOX69rIKp9Mym681tskGfTNHlnSsCnby7BUaOWkRtW5UlaqUKUxPnkOWPaqyFVBUVOCfhV6d+w+fMe0XwRvM4yHqkzNbelZvxUBfHioBLepXkvWmd0dgI42gOHN6O6pdpYRuIw1p9vEzVtA0t+4cMKNCPHryEqa7ZslgZzb3lJyoaSOA5BWea6Coa9UoVOB7DpvOPdowQS9XMj8ncOCvquoA/RI2Os3rV+Rnvtb7Dkq/PlNGVXxk3j8iIMGdrApBQBAIcwT0AxW8lCFMULVsYdq+7zhlzmBHzjXLsOly+jQpOXCRQayAdufBE6bYYCBYRkWlVuUS7F+2arMn2yhACdJYxltlDKHUh6CuiGOO76TX49naMCxQ3YT66F8D20v/nd5CAb11+bo9rM6nYaWZBSNYxsYb1ZNiBR3IfekWprjiPpYqgcp3W9jMXUu46NvqaGbe8HOr6FSQRVVmLt5IqP6B1ilVvEDsX7x6S/1GzeJquiRbwmY9Wtq3SHBnaVdU5iMImAACeCk/ffGaKXDYQLpNXkquXRpTfNs45HP5Fk2c5cH//s+ILjp1NBM4bZM6BU2tD1jCDgH9ZVr2Wl90BfLjc5dtpaIFHJRX9Xv3/iMnEpxrltUZwksg/HPLGvflqH+WcEXAJnZM2rrnGMEE3c/Pn5U0NbsTJBmqlSuqdD/ezyErR+sjwJ6CU5cxk6Nu1ZKspqlv5o3KHQb6ts+cvyoKuHrgPXmGXkZ36tGuvvKiR3JXGUdAgjtZGYKAIBCuCGjGwGVL5KfiBXPyb+Hf7tx/QhnsU0o2Ngj0kdkGFY7phZVL6KTE8WLXF12BJ17n/pMoUYJ4lDxpQlYuLZI/BwutqDhQVbKOFk03f/1AWKN+GeKCnjLVhUM0TFC5GzlpMQs5aKbn6Pk5efYy4R72PnOZJrmv5jV26dodev/hIw3r1ZLSpUmh4nKTOf8GAhCTWrhqBwUEBFCzehVp6Pj5/H+hGoyhJWbqVnNi/zs8++AHivcIKnsqq0PCyBtJmDpVS1GjWmV/4yrIRy0RAQnuLPGqypwEARNDAEIqQ/9ewC/lauWKkG1cG64KGBuPnr6gFev3kve/l6l8KUeqV83pu5c4KDpRopCSQaFhLyOCZM0Tr2ThXHTr7iMaPG4+92aUK5HfxFZBxJ+OMSEC/bMQ4RDj1wQKhlPmraXOLWtRgbxZdSINhiIOWu8Peh57tK8vYg4Rv8Qt5hdRtfM6deG7dWSowIxnP3qNN+08QrsOeJObaxvKYJ9KhwHuZ5UEuoBHwNcApeZsMQs+nCciwV04AyxfLwgIAoEIoDKCFzIknVs1rEL5c2X+ARqYtPYePoOFGxxzZyGvUxdp295jLP8MaiI2k1Dxe/7yNR+j2jAUXTFWlYKaJmTtVcTHcD1g83P/0VP2vzO26RPhkKDvoLsPnrBFAu7ZIT2ac6XEmN0JMIRdx8CuTZXyI1Pt2RPR89VXW86WKS3dvveYYK0A8R/0hx46fo4FlTQxFlT5hoxfQC3qVxTRpIi+WPJ7JoeABHcmd0nkhAQBNRHQ5O13eJ6gBjVKU5M6FVhB7R/3NZTXIRNLZYPKCdVD9P5olgsqoaUvuhKUPHafkTOpfbPqjBk2PKDOoUoKIRuVstohrQsRDgkJoe//js22ofeYprYJqxOYfO/09KZVm/dRwvi21Nq5CmFTLiIYP4ezHB2IwKJVO+gdbF6aVKPDx32Yjlm6WF7yuXSTlXEL5cvGyT8Edxev3qala3fx803fB06wFARURUCCO1WvvMxbEDAxBCDkAEph7w4NCVWDhR7bqUKpAnTu0g2mcubNmYmmL1jPFbxGtcopv2nE5ufVm3fUsUXN7+SxEQD37tiQ/j1/nZvuC+XNTghkINAyok8rSpIovold+cg5ndAKh2DdVa9QLHJO0kR+VfMeg19g/eqlWQDD2joa7Tl0ilZt8qThfVrRmi0H6NzFG9SrQwM+68nua74TtjGRqchpmAkCWHNfvxL344F6Xql0QU7wgb0BH8seQ6ZRrw4N+d/uP3pGnVwnkb+/PwuvFCuQU9meYzO5vHKa4YyABHfhDLB8vSAgCIQOAfTzjJi0iIO7ZEkSsOjKlj3HyPPIGRreuxXduveIps5bR2792yjr7aaPpLb5sY4Wlf9Zv6oJVdL+bnNYXAV9jtgQHT7hQ+NnrKROLWtS+ZKOobsoFnxUaIRDIFyTMH5cevr8Nfd/dmheg+LFDbRXUGnAe2zcjBUsnLJ511HauPMIxYwRnQ3O8W9ffP1oxMRFNNq1jU799uyF67Rq834a2K0pxYhurRJcMtcwRADPrtlLNtObd++pU4ta3H+tT0ePGcOa6cOgojesUZYWrd5BVcoU0okBheGpyFcJAmaDgAR3ZnOp5EQFActGQBMLef32PQs5YPOIgSDG19efldTKFM/LvT/nL9+imYs2kK+fv/LqkBpGqGrGihmDfcng+3bQ6ywN7t5Mp7JpzCTdsldUyLMLrXAIDNNP/F8lMr6tDQfJLRtWVipgWbftIFeH/6hQlEGFkiusEWxsYvK/o88TPbWg0Gk0THgLrubgrhmJXUfIa1GOCBoBWJyAyYF+64QJbOnFyzc0YVgnyp09A129eY/VXUf0aS22OrKIBIFvCEhwJ0tBEBAETAYBVOuWrdvDBudORfMQFCCLOjp8l6n18/PjqlStKiUpc/rUNH/FNkqUMB5vLFXuKUMPHgLiODaxWPjCPyCAlUYxNDsKtlWoUtJkrrcpnEhohUM0DK/cuMc2ALmyp2fDefhzqT5mL9lECeLH1a03/V48eA7CrqPb4ClUplg+NkJH5ThlskS0ZM0usrdLwTL3MgSB0CAAAZ/zl29S26Z/UIB/ALlNWUoOWfFcKxEkVf/5yzfcd6wlDEPzO3KMIGDOCEhwZ85XT85dELBQBECZu37rAaW1S86BievoOdS5VS3O1OJvA8fOpRKFclHNSsWZjrNg5XbuBUqcMJ6FIvJz0zpx5hLNXLSRhWfSpk5GqFCBruTm2paV5mQEjYAx4RAcDXU+BM2gDcNLDwENevEK58+uPJygbUL4qH+XJpQqRWJCpW/bHi8aO7Adb6rRM1WupCMr5EIEY/+xM5QnRybavs9LKi7Kr55fB+Dew6dctQM1GFT+oMaqzZ706dMXZnnIEARUQECCOxWussxREDBjBDw2eRJUIhHcWUWxYo87+LkhwKtbrRTVqFhc5/WD6hXoh3YpkrD8vYpKfaAwYUMN+iA23JAPB+UVfYsQHzA2xBz4P1RQZYL6XoPqpXXrx9DfTTsaVMS9h07R3YdPqGCebKxQqqJ5PNaXz+VbNHGWB508e4VVDft0bEgpkyf+ruqumcjDvLr3iJn04tUbGtqzBTlkTafkvWrGj2WTOPWPn77QqH8Wc0KhUc1yQSauJszyoBxZ0lFFpwImcd5yEoJAeCMgwV14IyzfLwgIAr+FgCYcgqBu9uJNLGyRKV1qQoVl8F/zWHLdMU8WWrxqJ3tyFcqXnS5fv0P2qZNzQBg7lnGz9N86KRP9MILbviNncU9YsQIOfJboU/G5dINc/2wcJIUwJHNgE51uhJ2WMX83BM1IMCCAcSqShzyPnqEkoAc3/UNperD+RQnKRF6rJHdoXpPcl26mymUKsRqiDEHgZxFAggUCK7DfMJa8QpV9+ISFVKdqScqXMzMnukDtvHLjLjWuXZ4TYTIEAUtDQII7S7uiMh9BwIIRwKZwxqINLIGdNYMdjZm6jHLnyEBJEyWgAWPcaUTfVtwPBQltCK5ksE+lXLYWVc6x05azgmGyxAlYcAAKo2lSJfthZWCjE5I5sAUvp1BNDUmEgWPcqX/XJjpvRU38B8ELbCfQ64kqwshJi7jilyt7hlB9t6UfZGjXgfnCexFBMXo/g6ok4zhgvHLjPkqbKhkVccxh6VDJ/MIJAVjAgBYMleBUyRMzXR2WHcN6t2S/PBmCgCUiIMGdJV5VmZMgYMEIQAADdENkX5Ht7+pSh6bMW8vUrqs37pG/fwC1cq7CPm/3Hj6htk3+sGA0jE8NlbgHj58RhASy/D8IDkpIABgGZw6s0eiUA9Bgwsj+R7eOpqMOvnj1lvqNmkU92zdgfDFQYf5r2nIWCymQJys9fPycYsaMTgnixVUWPkO7DgCBKiiom1DRDG59YQ1DcRNCLenTpFAWQ5n47yGAZ+DoyUuoRf1KnBjMaJ+K2jerwZYKSITBZgG+eWCEQIAK/y5DEDB3BCS4M/crKOcvCCiOACom8OBqXLscV1YQ/P0904POnL9GE4d14j4oGT8igIAlJHNgwc04Auj5RHV0tKuLLniDz+CQ8fNoWK9WlCBeHOo/xp3KlcjPlM24cWIr2YtniB4sFAb9NZeFLYK7LxEULvTYQQXzZqOcWdPJMhQEfhkB9GDD4NzX148Ff9APir7YG3ceUu/hM6hRrXKsynzg2Fk6e/E69e3krBSV/5eBlQ+aNAIS3Jn05ZGTEwQEgZAQ0ExuoZSpyWEj63/zzgOyS5lUpOqDADAkc+DQVu0ePX1B9x8+U0pMRKNgonIMk3goto6YuJhl/mHJsefgSVq5YR/3+Jy/cov8/f1pZF8XSpo4fkjL2aL/znYSU5dxz2zdqiWDvDchajN13jqmE4sCrkUviXCdHPwp5y3fSt7/XqYhPVuwcjAGkgcTZnpwVRi+oBDeQvCHCh+8HHHf4h2CPm8VRbnC9aLIl0cIAhLcRQjM8iOCgCAQngiAejP07wWskglLBBjdxrWJJS/mEEAPzhzY8KPY7Bw+cY7mrdjK4gUQssEmfdqC9SxKoG2SwvM6m9J3a72N6Fl89eYdi4L82aoWvX77gfq7zWbLhOyZ7XmTeP/RU1ZvVdmHUbt2UMpcuGoHBQQE0J+ta/+ACQLnoePnU5nieamiU0HuZRwxcSHVrFSCCubNakpLQM7FhBHAfQkhlRxZ7HU0TO10Hz99yRVkUIPTpErK/4zEw/CJi6hGxWKUN2cmGj9jJeXJkVGEfkz4GsupBY2ABHeyOgQBQcAiEMDL+dipC7RmywGq4FSAqpYtbBHziqhJ6JsDGwYhqPLNX7mNDh/3oe7t6nGWG0FdnuwZ2aAaFZZECWyVE8AALm/ff6SoVlYc4GpCK6jkdWxRU4K5X1i8UD7ce+g0De3VknvysC6HjJ/PVU9sykG/blavohhS/wK2Kn0EYkcQQ8qR2f4HSjT88WB+DnsYPLcw4NWIgG7MgHb08MlzqRyrtFgscK4S3FngRZUpCQKCgCAQlghcvn6XRkxcRKNdoboZmOnGxqlz/0lMQ0SFRQQwiJVJ0Ys3ok9rSpE0YbCXAHhBOTJatKgix/4NKUPrBFTkoazZvll1yp09A6HSjECvQqkCyqnghuX9rPp3gZaJQA4KwqAHP33xmgaNncuUTDzLhv29gKqULaxTc0X1eMrcNWynAAVmGYKAqSMgwZ2pXyE5P0FAEBAEIhmB2Us2sb0EAjmtBwUVlS27j9LQXi24d0oEMIjV9+48eEKF82UP9oqhcgCPxifPX5GVlRV7EoLSqZInozGAPDZ5Mobwp0Q1dNm63QShGs1uAptyt8lLads+L/r82Zd7o7q1qcvUOxmCwM8gAH+8JWt20vwV2/i+6+JSh6qWK8z9svqVY3wnnnVQeR3ZtzWLI8kQBEwdAQnuTP0KyfkJAoKAIBDJCEyY5UH2dsnZmwzDsMIiAhihv0Caz1vJwrmpVuUS/MHVmz3p9dv3yvUtGqKmb52AoA4VlAHdmuqsEECdmzJvHY3oE0inO3zCh2Yt3kR/D+movFhN6FegHBkUApqCMPruCucPTNC8fvOeBo51J5fG1bh6LEMQMAcEJLgzh6sk5ygICAKCQCQicOLMJZo6fx1XSRDkbdhxmH3cUFHx9fX/TgADgd+2vV7kHxDAnm8wDpbxHwIHvc5xMKdfBbh28z4tXbuL+nRqRDGiWxNU/lT32wIeUaJYkXPNMlwtNhRaAaLowQNNc2C3pmxIjWoMPB0hdS9DEPhZBLTgrmyJfFS2eD7+uGH1ODTfaczfMTSfk2MEgbBCQIK7sEJSvkcQEAQEAQtFAEIhF6/epmXr9tCN2w/o3YdPNHVUV+6/0xfAwPShdJjBPiXZ26Ugj037qHblklS5TEFRLv22NibPXcPKmTBM1sbWPV7kdfoCDezalC5cvU2T5qxiJb+UyRIHa/RtocvN6LQMqXFYk2u3HqQrN+5ykuH9+0/Ub/Rsqlu1FDnmziLegiotjjCc6807Dzlh4OfnT+nTpqSzF2+wXyo8VI0NJBTOX77JfbOZM9ixiBKeiYeO+3DSAckaGYJARCMgwV1EIy6/JwgIAoKAGSNw6dodgol3RacCBMEL19FzuEcKlKU37z5Q35GzWNEQ/m9Pn7+i8TNXUrc29UIUGDFjSH7q1NG78/T5a13/IjACZi0bVuGgBMExehixWfQ8coaca5VlPFW2UYD5OTbcMD/XqHGgbULOfnCP5rzxBq6bdh2hPDkysa9ZkkTxaEDXpjo1xJ+6SHKw0gggcfDm7Qda4LGdEie0ZaNzY3535y7dDExmpU3JTAUI/nRqUZMmzlmls0JRGkiZfKQhIMFdpEEvPywICAKCgHkjoC+AoQUfx05eoLHTlnHlKX+uzISNOSTt128/RPFt41CZb3Qn8575r589AmIEJdkypeX/D7NzBCztm1f/QczhybNX3O/TvW09PlblAaqbVRQrplziv6cvWE+xYsbgPsW7D55+F+jh7+Omr2AKsXPNsirDJnP/DQT8/QP401GjWv3wLbiP+7vNoSZ1ynMiCwM9oLi30UsrVii/Abx89LcRkODutyGULxAEBAFBQE0EtN6SaFGtaN22g1TE0YErdGcvXKfRk5dwVQVG3qiydB7wDxtXI8CDpDh8CFGhUnFonoznr9yignmyUV6HTPTi1fdVUOCiBXd9OzcKkhamIn7oUfx7lgcN69WSEiaI+12gp1VYFq/eSfAbbNvkD66ovHj1luxSJhF6sIoLJhzmDCr1EW8fGty9me45dv32AzZOhxWKZhkTDj8tXykIhIiABHchQiQHCAKCgCAgCASHAGhMqMxhs9O3UyOmwoGOCbpS9YrF2BAYNDnQm0BJ/Gv6cq7qSVUlEFXgB+EGYAOKK6qgWk+Zz6Ub5PpnY2UDYWPrDth88fXjfiYoaE6eu5YNqWF0jqEpktat5kTFCzowtheu3OYKaLy4NmRtHU1uaEHgtxAwVBBGwgbG6DmzpWdVYfTi7T10iu4+fKJL4IjQz29BLh/+CQQkuPsJsORQQUAQEAQEAeMIwJT78IlzhE0PvNvQMzamf1s2CEZw59a/DSVOGI8/DOomeqSG9W5F1tZReSNkq7B/FJQg4blVuUwhVn3EMOwpM0QdqqRQLM2RJZ2y6pCagua1W/epQ/MaVKZYPgr4GkArN+zlYA7UYPQ0gtqaO3tGevfhIx31Pk9DejSnIo455FYWBH4ZgeXr99CjJy+4xw4MBDzTFq3eQW6ubemLry8NHDuXUiZPTE5F8pDn0TOUJGE8atv0D6V7Z38ZbPngTyMgwd1PQyYfEAQEAUFAEAgKAQR52ERDyt/QJkH7DChzL1+/pS6t6/CmCLL3o13biEHwN4AMe8qMiTlAvXTp2t3Uv0sTZW0TNAXNfn82pmnz19GZ89cYwczpU7OYSjxbG+69A0VOE8VAUBzd2pqevnjFfo2oLmtJB7mrBYHQIvDh4yf2XNx3+DTFt7Vh2i9o6MUKOJD7si28tqDiiio8khAjJy2iBtVLUy7xygstxHLcbyAgwd1vgCcfFQQEAUFAEAgaAUiCT5y9mqaM6kKZ0qXmA1GRghBB/y6NyS5lUs5wg8ZUqkhu/juCwyhRSOneqIdPXrAdQs92DYI054b34K4D3tSnk7OS1QBfP3+av2Ir9ysWyJOVaaxv33+krwFfdTYI/164/kPVGJvySXPW0NmL16lgnqx06txVqveHE1WvUFTpNSfPsV9DAIHb3sOnWKHVtXMjtonpN2oW9WzfgLJksOMvRbLmr2nL2fcTaxUV95gxo1OCeHF/7UflU4JACAhIcCdLRBAQBAQBQSDMEUDmGjYJVcoWos27jpJT0TyULElCWrByG2+ma1YqTjv3n6Bjpy7ypkgTV0FAePLsVc56W0eLyuIsR7zPU2vnKtIrpXeVQGsFxhAMQWCDzeX0heu55zFrxjRhfj3N7QsRxA0dv4CqliuiSxxgDsAN62l475YUO1ZMtu8YMXEhuTSqptuMm9tc5XwjFwHcf37+Afy8gk3M2GnLabSriy54Q0JryPh5NKxXK0oQLw71H+NOGe1TUVHHHHyvxjWgpPv6+smzLnIvqdn/ugR3Zn8JZQKCgCAgCJgeAuhJwUYHQRoELnZ6nqAXr99SqcK5WdYf4iHIcPfq0IAVNTG0gBCiIjmy2NMhr3O0ZusBqlauMFV0Kmh6k4zEM4IZOvrzqpUrQovX7GIbBYiKZEyXimlgEHiwsYmpZFUPl+XT5y905IQPK7jCigND82Fs26QaV/wwsDGH+A96o1BV+fzFl6XvVfYVjMRlbfY/rVEwK5UuyBYJUGwdMXEx2aVKyt6WuE93eHpT7SolWAxo084jTCEunD87z/31m/fUZ+RMau1clQrmzWr2eMgEIgcBCe4iB3f5VUFAEBAELBoBbK5BsUTvnbEBWuGqTZ4sqoLNt6YYCf8oeETB02zu8i00e8lm6tOpIdWoUEwUI78BqdG8cmZNT/uOnGZ10q4udZnqtXbLAZq2YD0lTZyA/Pz8aFD3ZpQvZ2aLXmuhnRyk6tGDB3EfjRIHuwkkGWA3kc4uOSseIkCuVq6o0sFxaDGV435E4MGjZ1y9u33vMb16846Fkv5sVYtev/1A/d1mf2dwDkXN/cf+pYFdm3K1DhYLEKaCGJCWlBCMBYGfRUCCu59FTI4XBAQBQUAQ+G0EfC7f5I02vMpgNm3oEaVV8Vo2rEQ+l27So6cvqW8nZ5a/V31AXRRmyahMIetfpWxhVsyEwMhCj+00yrUN+w2idw8Yd2tTV3y3vlkkDBm/gFwaVeFqMYLkmQs30vOXr9luAv13k9xXU5H8OejStTv0/sNHXp/p0qRQfcnJ/H8SAa0HNOo35WD8bwitoJKnGZzj35C8Qo8x6NVgOCAoxH9DBEgo6T8JuhyuQ0CCO1kMgoAgIAgIAhGOADY2h0/40D/ua3hT8+bde5YVh6ohBrzJ0KuiKc7h31ANvHz9Lve2ZM5gpyR1DnTLZev2sJgKAg9UmTDQYwZxmhoVi+t6zAwphzDzjm4dTekK6KlzV2jM1GWUJ0cmuvvgCQUEBNDIvq1ZQdPVbTY1q1uRKXJaJfn+w2fUo319JddahD8ULPgHr968x4EbDM6ReMG4cechDft7AQ3p2YLSf0sgoN8uilUUoaRb8FqIiKlJcBcRKMtvCAKCgCAgCASJAIK42Us2MV0J8vWo4g0ZN5+G92ml2/ScPHuF5cQL5MlGvn5+vDEf0KWJUlUVeLYNGT+fUiVPzDTMODaxdJiivxFBy6h+LkzTxND6dxA0Z0pvR0PHz6fypRypXIn8Sq9GY0kCCK2cu/i9YTwqoVBC1ChzSoMmk/8tBEDVvPPgCRXOF9hbh6qxoU2H9gOgswsl/bfgVv7DEtwpvwQEAEFAEBAETAsB+OCBeggBAni8gaLZY+g0KpQ3G7VpXI0rT1CH3LbXi/p1bqSMshyqSS9fv6P4tnF+MC6HzxYCPwTIoHTh2PXbD9FBr7NcmTpx5jIrReK/NXU+bDBFOISYvgqaK2i/mmWHtvkGZbhhjTJ02uca+zG+//CJmtatwNRNUGFlCAK/goAxmw7te4SS/iuIymf0EZDgTtaDICAICAKCgEkhADuE3QdPsqw/DKZ3eJ6gQ8fPUc5s6QkCBK0aVmH/t2kL1tGg7s3J1kBK3KQmE4EnA+W9tdsOkHPNcnTr7kPGbeyAdpQkUXwaONadXBpXo9zfTJRR1Rs0bi61b1pdp1YagadqMj+lGcbvPXSa6lcvTXWrluRkwZ5Dp1jwZ7RrG157qzfvp8E9mlGc2LFoyZpdHCC7NK4qwbHJXEnzOpGd+70prk0sKuKY47sT1+jAhpR085qdnG1kIyDBXWRfAfl9QUAQEAQEge8QwIZ7p6c3bdvnRYO6NaNDx8/y32F2jorekjU7aeWGfbwZ16p7AmEgAvcfPeN+vFgxolMFpwKsCqlvS6FV6gxpiMA1ZozoylWjIEc/bkagsA/8GDfuPMI42MaNzf9mEzsWdRs8hYUwerSrT8UK5GQFxFH/LKY+HZ0pWZIEsvQEgTBDwBglPcy+XL5IGQQkuFPmUstEBQFBQBAwTwRO+1wlj42eNKh7UzaexgCVDuIgWn+Z/swQHMJKQWhzxJ53Q8bNY+N4+Lhp2IGGCIXIDGlTcm8exERaNqisO8Y8V8rPn/W6bQe5+vZHhaL8YdAu/fz8dTYIsOzYvPso9Whbn3DsybOXqVr5p/c3fgAAIABJREFUooTqcqBcfQw2Qa9ZqcR3vmQQvtl5wJtKFMpF8eLa/PyJySeURMCQkm4MBFT3sBZBzy5TPJ+SOMmkg0dAgjtZIYKAICAICAImjQCCtcWrdtLRk+fJpVE1ypLBLkgPMogRzFuxlaswbZpUo/Il8v+gDqlSrxnU9yC0Ajn/+n84kVVUK5q+YD0HJS6NqnJPI6p4MxdtpPjx4tDzF2/IuVZZala3gtKqmtoNAXVN0Fs11Vb0QwGrFMkSUauGlenAsbPcD2qXMiklT5pQ14+HXkfDHkeTvsnk5EwCAUNKurGTAmWz84B/KGfWdJQ2dTIqXSwvZbRPxfeyDEEACEhwJ+tAEBAEBAFBwOQRQLb62q37tHbrQfL3D6DubesFa/KLTfj8Fdvo5eu3NLDb9xU/qG6inw/CIyqMV6/f0czFG1mAJp5tHLaSmDq6G0uyY6OIKt7gHs25iodjL169zXYAslkMVBxFv2KtyiV4Ew1MsBa/fqXAtTV2LrVvVp1yZUtPV2/e5wREtXJFWJm0Wb2KylVCVbifwnOOhpR09BbrD/x96rx1nIipUbEYXbhym5MIqMKjP1mGICDBnawBQUAQEAQEAYtBAPYICGCSJUlIxQo48AbIbfJSKlk4N3u/GTMStpjJh2IiCIpB+0LVqaJTAZZjN6ziheJrlDvkybNXNGbqUnr6/DUVyZ+dalQqTimTJTLqxYg1hgTElRt3qWf7BkwNFkVS5ZZMuE04KJVNJLP2/F+E6uPnL1S+pCPbpchQFwGp3Kl77WXmgoAgIAhYDAKafHiJQjkpYQJbWrvlAKVOmYQePXlBnVvWolzZM5AmnjHata3OSNhiAPiFiQCPyXPX0vDerVh9NKiBIHDusq1UtIADU8FUHAjaENw9ef6SsmZMQw8ePWcD6gHdmuq8GIGLfiU0Yfy4NGLiYqZ0asbVKmIncw4bBNA/i4pwmeJ5qaJTQd2XHjt5gcZOW0YtGlQmm9gxWdm1cplCVL1CUam+hw30ZvctEtyZ3SWTExYEBAFBQBAwRADCFys27KURfVpT7FgxuEq3bN0eunbzHlOWMNymLKW9h0/zRjtfzszUyrmK0lQmyLFbW0el0kXzBrugUC2AYmSZYvnILlVSqQwQseddlChW5FyzjG4DrVHmkiSKR41qlaMbdx7ShFkeNLpfG4pnK6Iq8tT6PQTw7Nqx7zgN7dVSR0kHbbjX8Bn09f//b0iP5tz7+fjpS/pr+nIa0LUpIcEgQz0EJLhT75rLjAUBQUAQsDgEQJ3rOWw61axUnKqWK8xm3/3dZrOpd/bM9uxVBrrcsF4t2MdszZb9dPLsFTb11hQ4LQ6UMJjQh4+faOj4BVSupCPlz5WZvE5dJK/TF6hf58bB9jyGwU+b3VcYUubOXrhOKzfu+6aqGZ2goHnA6yxly5RWaHNmd3Uj/4QhFuXr50cxolvrTgaCP7CFad2oKs1esomDO1TtZi3eKFYdkX/JIu0MJLiLNOjlhwUBQUAQEATCEoFHT1/QivV76Yi3D929/4Sa1qvIPnjv339iqf9mdSuyUAgGxDD6u7lT307OZG+XPCxPw6K+C+p9MPjWrxagKnrv4VM2+tb307Ooif/kZIxR5vYf/ZetE+CPp/Xtwbqjq0tdimMT6yd/QQ4XBH5EAIwFz6NnqFf7BvxHn8u36K9py5k+3aN9fQJdHc/Efy9co/IlC3DyC8wGGZaNgAR3ln19ZXaCgCAgCCiHADbay9fvpqpli7DJtKFhNwCBIuT0hRtoVD8XihM7Ft28+5A3QlCMFNW5wCWj9TF2blWLcmfPwP+GwA5G3x4b91Hz+pXoi68v/3fH5jV1gbNyC46IPn3+QkdO+FARRwddRRPr7t37j5Q7R0Y2PXeuWZb99ERgRcUVEj5zRtJgyPj51K1NXbaIwUCF2M8/gJDs6j18BlOEixV0IM8jZ+jw8XOsjJskUdA9tuFzpvKtEYmABHcRibb8liAgCAgCgkCEIgCz8879J/EGp3aVEtwfhc0P+u8ypkvFmex/3NfS2YvXqWCerHTq3FU2/BYxAqJFq3bQqzfvqGOLmrqARPPYypMjA3VpXYcDYQTKy9fvIdc/mwhVU291o98OPngQuejfpQnlyGIfoWtffkwNBM5dukkjJi6kwvmyU6F82SlHZnuKEycWTZjpQalSJOZnn2bhAfPzJIkSEISnZFguAhLcWe61lZkJAoKAICAIUKBX2bgZK7iKl9chE9MJP3/+wv532/cdpyPe52l475bce/fm3QfeKGlm6SoDCIEQ+LnBF08bG3cepms371O5EvlpztLNlD9XFsqfOwstX7ebBRxw7N2HTymdXQq2AVB1oKIyeNw8tp3o2roO23LIEATCCwFUjtF/d+/BU6pWvgiBvQD/SvTG6vt5om/PP8Cfdu0/Sas276OE8W2ptXMV7gMVX8vwujoR/70S3EU85vKLgoAgIAgIAhGMAKp1nkf/pSvX75J9muQcnHzx9aO+I2dxXx6CPgzQDsfPXElORfKwATVoddGto1F0PRGDCD51k/o5UA39AwKoXjUnwkbx8IlzNHryEjbsBu0QfWZT569jTFOnSEJZMqRRKsjD+oFQD2iYwERomCa1fJU5GShmjpy0iMV8kNTSBhI2s5dspnMXb1CvDoF9epPd1/C9q/UjKwOSBU9UgjsLvrgyNUFAEBAEBIGgEbh++wGNm76C3Pq3oQTxAiXDUXHpN2oW9e3ciNKkSsa+UuVLOXIwKIPo8vW7NHnuGt40at5tn7/4clD8+bMvDRzrTiUK52YJ9jVbDhBsAVSia6KCsnHHYcqRJZ3QMOWGiTQEtCDOz8+P2jetrktO4f4dMXERjXZto6voQdV11eb9zGTQV+KMtJOXH/5tBCS4+20I5QsEAUFAEBAEzBGBt+8+0JDxC8ilURW2S8CGaObCjfT85Wv2xjt68gKLscAuIW6c2DxFHKMviGH4v80Rh585ZwRxUOdDtr9K2cKUJ0dGDmJixYxBy9btZhNvmHYDI9Bh+4ycSZ1a1GQTeRmCgCAQcQgg0bB49U5Wy8yexZ4Gdm1KO/efoA8fP3NlXaNhnva5ysbnSNjEjGFNb99/5JOMaxNLqJoRd7nC9JckuAtTOOXLBAFBQBAQBMwJAfSpjJm6jPLkyER3HzyhgIAADuaiW1tzFcqlcTWdUiSClUHj5nImHMGgFhy2qF9RueAFc/c6fZHgg4cg78Gj59zjAyU+KI5ioH9xwBh36ti8Bvf0oA8I9Fgbm5iiGGlON4mcq1kjgAQU7jv0FMMLL0H8uEyr1pJVYC/ADqZy6UI0dtpytu8ADT1z+tTcRwv7DhnmhYAEd+Z1veRsBQFBQBAQBMIYAWS4QVeCGEjmDHYceED98dbdR7oqFH5S31IBflJL1+7iY/U94ML41Mzi67B5nDpvHdnGjU0tG1TWZfthHI9q3og+rWnPwZM0bcF6Spo4AYEqNqh7M8qXM7NZzE9OUhCwFAQuXLlF/7ivYfVWKGlCPXPbHi8a5erCZugQQULyyto6Gu07fJoWrtpBbRpXo6KODkr1zpr79ZbgztyvoJy/ICAICAKCQJgigArTkHHz2BIBoioYsFRAZQp0TVSm7j96Rp1cJ5G/vz/16eRMxQrkVHbz8++F6+Q2eQnFjWNDTeuWp+IFc9GN2w9oxKRF1KFZDUJP3kKP7TTKtQ336QFLVAvgzaWv5BemF1G+TBAQBH5AALRqJKYmzvJg4Z/SxfJSn44NmYo5ZupSGu3aVtdLy5V3tzlUt5oTlSqSW9A0IwQkuDOjiyWnKggIAoKAIBD+CPj6+jFVM12aFFT/DyeyimpF0xes574yl0ZV+QTcl22hj58+U8MaZWnR6h1UpUwhpmqqNhAIQ3SmTPG85Jg7K/sH7trvTSmTJ2JKV16HjDTor3lUo2Jx3QbRUJEUlT9//wARc1Bt8ch8TQaBE2cucS9tr/YNdJX3DTsO05nz18i1cyN+BqIfGdW+WpVLmMx5y4kYR0CCO1kZgoAgIAgIAoKAAQKvXr+jmYs30ra9XhTPNg5TNqeO7sZZ7as379HISYuZbqh65en12/dMuaxUuiD39BgOUFsRKI/q56Lr3TEUWtnheZzNvtGvp6n1IQCEx57KXnlyUwoCEYWAz+WbNGnOan6madX1/m6zqXeHhpy0Ap2z57DpLCz1/MUbKlcyP1flNf9G3K+wlhG1zYi6YsH/jgR3pnEd5CwEAUFAEBAETBABVJSgOAcz6opOBViYANUph6zpqXaVEqImF8I1e/HqLQ0ZP583iQiEsQlcv/0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", + "text/html": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "grp_gen = df.select(['submitted_via','complaint_id']).groupby(['submitted_via']).agg(['count']).to_pandas()\n", "\n", @@ -449,10 +6776,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "4b630738-a5b1-4815-afcc-b06444ffc2e0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Please enter GOOGLE_API_KEY: ·······································\n" + ] + } + ], "source": [ "GOOGLE_API_KEY = getpass.getpass(prompt = 'Please enter GOOGLE_API_KEY: ')\n", "genai.configure(api_key = GOOGLE_API_KEY)" @@ -466,195 +6801,310 @@ "
    \n", "5. Cluster the Complaints\n", "\n", - "

    For our complaint clustering task, we'll be using a sample of the data to cluster the complaints. This approach will allow us to effectively analyze and categorize the complaints without using the entire dataset.

    \n", - "\n", - "

    Here we have used text-embedding-004 model google gemini offerings. please click here to learn more about this embeddings model embeddings

    " + "

    For our complaint clustering task, we'll be using a sample of the data to cluster the complaints. This approach will allow us to effectively analyze and categorize the complaints without using the entire dataset.

    " ] }, { - "cell_type": "code", - "execution_count": null, - "id": "81fc5101-9387-4846-ac0e-23f23495502c", + "cell_type": "markdown", + "id": "4833e1db-387b-49e8-bd92-1ae9a794ee92", "metadata": {}, - "outputs": [], "source": [ - "from google.api_core import retry\n", + "
    \n", "\n", - "def make_embed_text_fn(model):\n", + "

    5.1 Do you want to generate the embeddings?

    \n", + "

    We have already generated embeddings for the comment text and stored them in Vantage table.

    \n", "\n", - " @retry.Retry(timeout=300.0)\n", - " def embed_fn(text: str) -> list[float]:\n", - " # Set the task_type to CLUSTERING.\n", - " embedding = genai.embed_content(model=model,\n", - " content=text,\n", - " task_type=\"clustering\")\n", - " return embedding[\"embedding\"]\n", + "
    \"embeddings_decision\"
    \n", "\n", - " return embed_fn\n", + "
    \n", + "

    Note: If you would like to skip the embedding generation step to save the time and move quickly to next step, please enter \"No\" in the next prompt.

    \n", + "
    \n", "\n", - "def create_embeddings(df):\n", - " model = 'models/text-embedding-004'\n", - " df['Embeddings'] = df['consumer_complaint_narrative'].progress_apply(make_embed_text_fn(model))\n", - " \n", - " # Generate all the embeddings columns from the \"embeddings\" column.\n", - " for i in range(len(df['Embeddings'][0])):\n", - " df[\"embeddings_{}\".format(i)] = df[\"Embeddings\"].apply(lambda x: x[i])\n", - " \n", - " # drop embedding \n", - " df = df.drop(\"Embeddings\", axis=1)\n", - " return df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7be29ef-8877-40a9-9c83-bf7784bc0aa8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "embedded_df = create_embeddings(df.to_pandas())" - ] - }, - { - "cell_type": "markdown", - "id": "75378143-dcba-47ba-a5a7-e434984423d7", - "metadata": {}, - "source": [ - "

    Now the results can be saved back to Vantage.

    " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4aa8b2b9-0ce8-4790-aca0-9747123f2002", - "metadata": {}, - "outputs": [], - "source": [ - "copy_to_sql(embedded_df, table_name ='complaints_embeddings', if_exists = 'replace')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "141d4c41-6cad-4491-84f9-7d37e18444e1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "embedded_df = DataFrame('complaints_embeddings')\n", - "embedded_df" + "

    To save time, you can move to the already generated embeddings section. However, if you would like to see how we generate the embeddings, or if you need to generate the embeddings for a different dataset, then continue to the following section.

    " ] }, { "cell_type": "code", - "execution_count": null, - "id": "e9b99852-9bc1-4ae5-b98c-21b1096fc9c8", + "execution_count": 14, + "id": "6970948e-0a65-4164-afbe-be4585eacd94", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Do you want to generate embeddings? ('yes'/'no'): no\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Loading embeddings from the local file 'embeddings.parquet.gzip'.\n" + ] + }, + { + "data": { + "text/markdown": [ + "

    The code above first reads the data from the files. The files contain information about the complaints embeddings. The code then loads the data into a permanent table in SQL. Once the data is loaded, we will use the Vantage in-database function KMeans to find the clusters from the embeddings. The data contains complaints embeddings, which are lists of numerical values, or vectors.

    \n", + "

    The embeddings file contains 1000 records, each with 1536 numerical features. This means that the file is quite large and it may take some time to load it into SQL.

    \n", + "
    \n", + "

    Note: Please be patient. The code above is loading data from files and copying it to SQL. This process may take 10-15 seconds.

    \n", + "
    " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Embeddings loaded and saved successfully!\n" + ] + } + ], "source": [ - "cols = list(embedded_df.columns[:17]) + ['consumer_complaint_narrative']\n", - "embedded_df = embedded_df.drop(columns=cols)\n", - "copy_to_sql(df = embedded_df, table_name = 'kmeans_features', if_exists = 'replace')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5e1fa67-cd5c-4503-961c-a53d79159c4a", - "metadata": {}, - "outputs": [], - "source": [ - "no_clusters = 5\n", + "# Request user's input\n", + "generate = input(\"Do you want to generate embeddings? ('yes'/'no'): \")\n", + "\n", + "# Check the user's input\n", + "if generate.lower() == 'yes':\n", + " print(\"\\nGreat! We'll start by generating embeddings.\")\n", + "\n", + " print(\"\\nGenerating embeddings, please wait...\")\n", + " \n", + " display(Markdown(\"\"\"
    \n", + "

    Note: The embedding generation step is estimated to take approximately 5 minutes to complete. Please be patient.

    \n", + "
    \"\"\"))\n", + " \n", + " display(Markdown(\"\"\"

    Here we have used text-embedding-004 model google gemini offerings. \n", + "please click here to learn more about this embeddings model embeddings

    \"\"\"))\n", + " \n", + " from google.api_core import retry\n", + " def make_embed_text_fn(model):\n", + "\n", + " @retry.Retry(timeout=300.0)\n", + " def embed_fn(text: str) -> list[float]:\n", + " # Set the task_type to CLUSTERING.\n", + " embedding = genai.embed_content(model=model,\n", + " content=text,\n", + " task_type=\"clustering\")\n", + " return embedding[\"embedding\"]\n", + "\n", + " return embed_fn\n", + "\n", + " def create_embeddings(df):\n", + " model = 'models/text-embedding-004'\n", + " df['Embeddings'] = df['consumer_complaint_narrative'].progress_apply(make_embed_text_fn(model))\n", + "\n", + " # Generate all the embeddings columns from the \"embeddings\" column.\n", + " for i in range(len(df['Embeddings'][0])):\n", + " df[\"embeddings_{}\".format(i)] = df[\"Embeddings\"].apply(lambda x: x[i])\n", + "\n", + " # drop embedding \n", + " df = df.drop(\"Embeddings\", axis=1)\n", + " return df\n", + "\n", + " # API request for embeddings\n", + " embedding_df = create_embeddings(df.to_pandas(num_rows=10))\n", + "\n", + " # Save embeddings to SQL\n", + " print(\"\\nSaving embeddings to the database...\")\n", + " copy_to_sql(\n", + " df = embedding_df,\n", + " table_name = 'complaints_embeddings',\n", + " if_exists = 'replace'\n", + " )\n", "\n", - "# drop existing model\n", - "try:\n", - " db_drop_table(table_name = \"KMeans_Model\")\n", - "except:\n", - " pass\n", + " print(\"\\nEmbeddings generated and saved successfully!\")\n", "\n", - "# Model Table\n", - "query = f'''\n", - " SELECT * from TD_KMeans (\n", - " ON kmeans_features as InputTable\n", - " OUT TABLE ModelTable(KMeans_Model)\n", - " USING\n", - " IdColumn('complaint_id')\n", - " TargetColumns{tuple(embedded_df.columns[19:])}\n", - " NumClusters({no_clusters})\n", - " OutputClusterAssignment('true')\n", - ") AS dt;\n", - "'''\n", + "elif generate.lower() == 'no':\n", + " print(\"\\nLoading embeddings from the local file 'embeddings.parquet.gzip'.\")\n", + " \n", + " display(Markdown(\"\"\"

    The code above first reads the data from the files. The files contain information about the complaints embeddings. The code then loads the data into a permanent table in SQL. Once the data is loaded, we will use the Vantage in-database function KMeans to find the clusters from the embeddings. The data contains complaints embeddings, which are lists of numerical values, or vectors.

    \n", + "

    The embeddings file contains 1000 records, each with 1536 numerical features. This means that the file is quite large and it may take some time to load it into SQL.

    \n", + "
    \n", + "

    Note: Please be patient. The code above is loading data from files and copying it to SQL. This process may take 10-15 seconds.

    \n", + "
    \"\"\"))\n", + " # Save them to SQL\n", + " copy_to_sql(\n", + " df = pd.read_parquet('./data/embeddings.parquet.gzip'),\n", + " table_name = 'complaints_embeddings',\n", + " if_exists = 'replace'\n", + " )\n", + "\n", + " print(\"\\nEmbeddings loaded and saved successfully!\")\n", "\n", - "execute_sql(query)" + "else:\n", + " print(\"\\nInvalid input. Please enter 'yes' or 'no' to proceed.\")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "63a55e88-1632-46eb-b86c-3ee3d16edd8a", + "execution_count": 15, + "id": "7533ed89-6523-407d-9ca0-ce7c47a2e878", "metadata": {}, "outputs": [], "source": [ - "KMeans_Model = DataFrame(\"KMeans_Model\")\n", - "print(\"Data information: \\n\", KMeans_Model.shape)" + "data_cols = DataFrame('complaints_embeddings').iloc[:, 17].columns + DataFrame('complaints_embeddings').iloc[:, 19:].columns\n", + "KMeans_Model = KMeans(\n", + " data = DataFrame('complaints_embeddings')[data_cols],\n", + " id_column = \"complaint_id\",\n", + " target_columns = DataFrame('complaints_embeddings').columns[21:],\n", + " output_cluster_assignment = True,\n", + " num_clusters = 5\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "55d1616f-da1a-4b6e-9824-1262d2eb0d70", + "execution_count": 16, + "id": "0ec9b387-929f-4fe6-a339-787149fda058", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data information: \n", + " (11, 771)\n" + ] + } + ], "source": [ - "no_clusters = 5\n", - "\n", - "# for Cluster info, used later for filtering\n", - "query = f'''\n", - " SELECT * from TD_KMeans (\n", - " ON kmeans_features as InputTable\n", - " USING\n", - " IdColumn('complaint_id')\n", - " TargetColumns{tuple(embedded_df.columns[19:])}\n", - " NumClusters({no_clusters})\n", - " OutputClusterAssignment('true')\n", - ") AS dt;\n", - "'''\n", - "\n", - "kmeans_out_sql = DataFrame.from_query(query)" + "print(\"Data information: \\n\", KMeans_Model.model_data.shape)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "7d5a3743-a51a-46cd-888c-eecd85e3f3ab", + "execution_count": 17, + "id": "de5dc3b2-4131-4b8c-9ecc-2d4d9bc29707", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "
    complaint_idtd_clusterid_kmeans
    63606892
    24680704
    21957913
    54999201
    57870210
    " + ], + "text/plain": [ + " complaint_id td_clusterid_kmeans\n", + "0 6360689 2\n", + "1 2468070 4\n", + "2 2195791 3\n", + "3 5499920 1\n", + "4 5787021 0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "kmeans_out_sql" + "KMeans_Model.result" ] }, { "cell_type": "code", - "execution_count": null, - "id": "7cd95cbc-7434-4248-84f8-20260410bd7e", + "execution_count": 18, + "id": "9f7b8090-3484-4737-9e09-feb5987ff68c", "metadata": {}, "outputs": [], "source": [ - "embeddings_cluster = DataFrame('complaints_embeddings').join(other=kmeans_out_sql, how=\"full\", on=\"complaint_id=complaint_id\", lprefix= \"L_\")" + "embeddings_cluster = DataFrame('complaints_embeddings').join(\n", + " other = KMeans_Model.result,\n", + " how = \"inner\",\n", + " on = \"complaint_id=complaint_id\",\n", + " lprefix = \"L_\"\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "94563f0e-6d80-4b73-ac5a-8aa787ddfc9b", + "execution_count": 19, + "id": "ee2eebe1-91bb-4d93-9e0d-054e21ba2a5a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "\t\n", + "\t\t\n", + "\t\t\n", + "\t\t\n", + "\t\n", + "
    td_clusterid_kmeanscomplaint_idconsumer_complaint_narrative
    16946869In reference to my XXXX XXXX XXXXXXXX XXXX XXXX and Discover Credit Card, n accordance with the fair credit Reporting act XXXX Account # XXXX, has violated my rights. \n", + "15 USC 1681 Section 602 States I have the right to privacy.\n", + "\n", + "15 USC 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions.\n", + "\n", + "15 USC 1666B : A creditor may not treat a payment on a credit card account under an open end consumer credit plan as late for XXXX purpose.
    15499920My name is XXXX XXXX, and I am sending this request myself to address the inaccuracies on my Credit Reports. The XXXX, XXXX, and XXXX Credit Bureaus are reporting inaccurate information on DISCOVER BNK. Per FCRA, reporting must be 100 % accurate or the information must be deleted. Evaluate this reporting by making a revision of the information provided. Modify and perform any corrections or withdraw this from my personal file.
    " + ], + "text/plain": [ + " td_clusterid_kmeans complaint_id consumer_complaint_narrative\n", + "0 1 6946869 In reference to my XXXX XXXX XXXXXXXX XXXX XXXX and Discover Credit Card, n accordance with the fair credit Reporting act XXXX Account # XXXX, has violated my rights. \\n15 USC 1681 Section 602 States I have the right to privacy.\\n\\n15 USC 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions.\\n\\n15 USC 1666B : A creditor may not treat a payment on a credit card account under an open end consumer credit plan as late for XXXX purpose.\n", + "1 1 5499920 My name is XXXX XXXX, and I am sending this request myself to address the inaccuracies on my Credit Reports. The XXXX, XXXX, and XXXX Credit Bureaus are reporting inaccurate information on DISCOVER BNK. Per FCRA, reporting must be 100 % accurate or the information must be deleted. Evaluate this reporting by making a revision of the information provided. Modify and perform any corrections or withdraw this from my personal file." + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# View complaints in cluster 1\n", - "embeddings_cluster[['td_clusterid_kmeans','complaint_id','consumer_complaint_narrative']].loc[embeddings_cluster.td_clusterid_kmeans==1]" + "embeddings_cluster[['td_clusterid_kmeans','complaint_id','consumer_complaint_narrative']] \\\n", + " .loc[embeddings_cluster.td_clusterid_kmeans == 1]" ] }, { @@ -764,13 +7214,39 @@ "6. Cleanup" ] }, + { + "cell_type": "markdown", + "id": "b3416c12-d50d-4f30-9e65-39a18cd9e8c4", + "metadata": {}, + "source": [ + "
    \n", + "

    6.1 Work Tables

    \n", + "

    We need to clean up our work tables to prevent errors next time.

    " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f36eb92-0950-4911-8ca8-6612a0bd8bb1", + "metadata": {}, + "outputs": [], + "source": [ + "for table in db_list_tables()['TableName'].tolist():\n", + " try:\n", + " db_drop_table(table_name=table, schema_name=\"demo_user\")\n", + " except:\n", + " pass" + ] + }, { "cell_type": "markdown", "id": "73a8ead8-0698-4449-bc11-5a07351e3fb6", "metadata": {}, "source": [ - "

    Databases and Tables

    \n", - "

    The following code will clean up tables and databases created above.

    " + "
    \n", + "\n", + "

    6.2 Databases and Tables

    \n", + "

    We will use the following code to clean up tables and databases created for this demonstration.

    " ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb index 4d8b7c31..8be31035 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Sentiment_Analysis.ipynb @@ -122,7 +122,8 @@ "\n", "display.max_rows = 5\n", "pd.set_option('display.max_colwidth', 50)\n", - "pd.set_option('display.max_rows', 5)" + "pd.set_option('display.max_rows', 5)\n", + "from IPython.display import display, Markdown" ] }, { @@ -268,32 +269,34 @@ "source": [ "# Sentiment\n", "for i in tqdm(range(len(pd_df))):\n", - " prompt = f'''Analyze the following customer review:\n", - " \"{pd_df['consumer_complaint_narrative'][i]}\"\n", + " try:\n", + " prompt = f'''Analyze the following customer review:\n", + " \"{pd_df['consumer_complaint_narrative'][i]}\"\n", "\n", - " Classify the sentiment as Positive, Negative or Neutral.\n", + " Classify the sentiment as Positive, Negative or Neutral.\n", "\n", - " Provide a detailed single-sentence explanation for your classification without using any special formatting (no commas or asterisks).\n", + " Provide a detailed single-sentence explanation for your classification without using any special formatting (no commas or asterisks).\n", "\n", - " Your response should follow this format:\n", - " Sentiment: [Your classification]\n", - " Reasoning: [Your explanation]'''\n", - " \n", - " output = model.generate_content([prompt])\n", - " finish_reason = output.candidates[0].finish_reason\n", - " \n", - " if finish_reason == protos.Candidate.FinishReason.STOP:\n", - " print(f'\\n\\n ---- Row op ---- \\n {output} \\n\\n')\n", - " output = output.candidates[0].content.parts[0].text\n", + " Your response should follow this format:\n", + " Sentiment: [Your classification]\n", + " Reasoning: [Your explanation]'''\n", "\n", - " sentiment = re.search('Sentiment:(.*)', output).group(1)\n", - " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", - " else:\n", - " sentiment = 'Neutral'\n", - " reasoning = ''\n", - " \n", - " pd_df['Sentiment'][i] = sentiment\n", - " pd_df['Reasoning with Chain of Thought'][i] = reasoning" + " output = model.generate_content([prompt])\n", + " finish_reason = output.candidates[0].finish_reason\n", + "\n", + " if finish_reason == protos.Candidate.FinishReason.STOP:\n", + " output = output.candidates[0].content.parts[0].text\n", + "\n", + " sentiment = re.search('Sentiment:(.*)', output).group(1)\n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " else:\n", + " sentiment = 'Neutral'\n", + " reasoning = ''\n", + "\n", + " pd_df['Sentiment'][i] = sentiment\n", + " pd_df['Reasoning with Chain of Thought'][i] = reasoning\n", + " except:\n", + " pass" ] }, { @@ -396,6 +399,20 @@ "

    Unlock the power of customer feedback with our intuitive word cloud visualization, which provides a comprehensive snapshot of negative consumer complaints sentiment. This innovative tool highlights the most frequently occurring words and pain points in customer feedback, empowering businesses to:

    1. Identify trends and sentiment patterns
    2. Pinpoint areas for improvement
    3. Make data-driven decisions to enhance customer satisfaction and loyalty

    By leveraging this word cloud, businesses can proactively address customer concerns, refine their products and services, and ultimately drive growth through a deeper understanding of their customers' needs and preferences.

    " ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "afd54125-8159-4484-83c9-cb36f3a3951e", + "metadata": {}, + "outputs": [], + "source": [ + "def display_helper(msg):\n", + " return display(Markdown(\n", + " f\"\"\"
    \n", + "

    Note: \n", + " {msg}

    \"\"\"))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -409,7 +426,7 @@ "# Replace 'X' with blank space\n", "modified_string = neg_text.replace('X', '')\n", "\n", - "if len(modified_string) > 0:\n", + "if len(modified_string):\n", " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", " # Display the word cloud\n", @@ -418,7 +435,8 @@ " plt.axis(\"off\")\n", " plt.show()\n", "else:\n", - " print(\"Sorry, there are no records with Negative Sentiments\")" + " display_helper(\"\"\"We included positive, negative, and neutral categories to cover all bases. \n", + " But in this sample, it's possible that none of the complaints are actually negative.\"\"\")" ] }, { @@ -445,7 +463,7 @@ "# Replace 'X' with blank space\n", "modified_string = neu_text.replace('X', '')\n", "\n", - "if len(modified_string) > 0:\n", + "if len(modified_string):\n", " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", " # Display the word cloud\n", @@ -454,7 +472,8 @@ " plt.axis(\"off\")\n", " plt.show()\n", "else:\n", - " print(\"Sorry, there are no records with Neutral Sentiments\")" + " display_helper(\"\"\"To cover all possible scenarios, we included positive, negative, and neutral categories in our analysis. \n", + " However, given that this dataset consists of complaints, it's expected that the model would rarely, if ever, encounter neutral responses.\"\"\")" ] }, { @@ -481,7 +500,7 @@ "# Replace 'X' with blank space\n", "modified_string = pos_text.replace('X', '')\n", "\n", - "if len(modified_string) > 0:\n", + "if len(modified_string):\n", " wordcloud = WordCloud(width=800, height=400, background_color='white').generate(modified_string)\n", "\n", " # Display the word cloud\n", @@ -490,7 +509,9 @@ " plt.axis(\"off\")\n", " plt.show()\n", "else:\n", - " print(\"Sorry, there are no records with Positive Sentiments\")" + " display_helper(\"\"\"To cover all possible scenarios, we included positive, negative, and neutral categories in our analysis. \n", + " However, given that this dataset consists of complaints, it's expected that the model would rarely, \n", + " if ever, encounter positive responses.\"\"\")" ] }, { @@ -558,9 +579,9 @@ "source": [ "viz_neg = result[result['Sentiment'] == -1]\n", "\n", - "if viz_neg.shape[0] > 0:\n", - " viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", + "viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", "\n", + "if viz_senti.shape[0] > 0:\n", " # Sorting the DataFrame by year for each product\n", " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", "\n", @@ -570,7 +591,8 @@ "\n", " fig.show()\n", "else:\n", - " print(\"Sorry, there are no records with Negative Sentiments\")" + " display_helper(\"\"\"We included positive, negative, and neutral categories to cover all bases. \n", + " But in this sample, it's possible that none of the complaints are actually negative.\"\"\")" ] }, { @@ -593,9 +615,9 @@ "source": [ "viz_neg = result[result['Sentiment'] == 0]\n", "\n", - "if viz_neg.shape[0] > 0:\n", - " viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", + "viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", "\n", + "if viz_senti.shape[0] > 0:\n", " # Sorting the DataFrame by year for each product\n", " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", "\n", @@ -605,7 +627,8 @@ "\n", " fig.show()\n", "else:\n", - " print(\"Sorry, there are no records with Neutral Sentiments\")" + " display_helper(\"\"\"To cover all possible scenarios, we included positive, negative, and neutral categories in our analysis. \n", + " However, given that this dataset consists of complaints, it's expected that the model would rarely, if ever, encounter neutral responses.\"\"\")" ] }, { @@ -628,9 +651,9 @@ "source": [ "viz_neg = result[result['Sentiment'] == 1]\n", "\n", - "if viz_neg.shape[0] > 0:\n", - " viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", + "viz_senti = viz_neg.select(['product','Sentiment', 'year']).groupby(['product', 'year']).agg(['sum']).to_pandas()\n", "\n", + "if viz_senti.shape[0] > 0:\n", " # Sorting the DataFrame by year for each product\n", " pd_df_sorted = viz_senti.sort_values(by=['product', 'year'])\n", "\n", @@ -640,7 +663,8 @@ "\n", " fig.show()\n", "else:\n", - " print(\"Sorry, there are no records with Positive Sentiments\")" + " display_helper(\"\"\"To cover all possible scenarios, we included positive, negative, and neutral categories in our analysis. \n", + " However, given that this dataset consists of complaints, it's expected that the model would rarely, if ever, encounter positive responses.\"\"\")" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb b/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb index 563991e5..03634bb1 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/Topic_Modelling.ipynb @@ -71,6 +71,7 @@ "outputs": [], "source": [ "%%capture\n", + "\n", "!pip install -r requirements.txt --quiet" ] }, @@ -116,7 +117,8 @@ "\n", "display.max_rows = 5\n", "pd.set_option('display.max_colwidth', None)\n", - "pd.set_option('display.max_rows', 5)" + "pd.set_option('display.max_rows', 5)\n", + "from IPython.display import display, Markdown" ] }, { @@ -327,38 +329,41 @@ "outputs": [], "source": [ "for i in tqdm(range(len(pd_df))):\n", - " prompt = f'''\n", - " The following is text from a complaint:\n", - " \n", - " “{pd_df['consumer_complaint_narrative'][i]}”\n", - " \n", - " Give me reasoning as well as topic for this complaint\n", - " Instructions for Reasoning:\n", - " - Give me Reasoning in detail\n", - " - Only one sentence reasoning\n", - " Instructions for Topic:\n", - " - The complaint falls into only one of the following Topics: Mortgage Application, Payment Trouble, Mortgage Closing, Report Inaccuracy, Payment Struggle\n", - " - Only select one Topic\n", - " - Important: Do not add any formating into the output. For example **Mortgage Application** or **Report Inaccuracy** refrain from such formating in the response.\n", + " try:\n", + " prompt = f'''\n", + " The following is text from a complaint:\n", + "\n", + " “{pd_df['consumer_complaint_narrative'][i]}”\n", + "\n", + " Give me reasoning as well as topic for this complaint\n", + " Instructions for Reasoning:\n", + " - Give me Reasoning in detail\n", + " - Only one sentence reasoning\n", + " Instructions for Topic:\n", + " - The complaint falls into only one of the following Topics: Mortgage Application, Payment Trouble, Mortgage Closing, Report Inaccuracy, Payment Struggle\n", + " - Only select one Topic\n", + " - Important: Do not add any formating into the output. For example **Mortgage Application** or **Report Inaccuracy** refrain from such formating in the response.\n", + "\n", + " My output comes in the format:\n", + " Topic: , \n", + " Reasoning: ,\n", + " '''\n", + " output = model.generate_content([prompt])\n", + " finish_reason = output.candidates[0].finish_reason\n", + "\n", + " if finish_reason == protos.Candidate.FinishReason.STOP:\n", + " output = output.candidates[0].content.parts[0].text\n", + " # apply regex\n", + " topic = re.search('Topic:(.*)', output).group(1)\n", + " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", + " else:\n", + " topic = \"Default\"\n", + " reasoning = \"\"\n", "\n", - " My output comes in the format:\n", - " Topic: , \n", - " Reasoning: ,\n", - "'''\n", - " output = model.generate_content([prompt])\n", - " finish_reason = output.candidates[0].finish_reason\n", - " \n", - " if finish_reason == protos.Candidate.FinishReason.STOP:\n", - " output = output.candidates[0].content.parts[0].text\n", - " # apply regex\n", - " topic = re.search('Topic:(.*)', output).group(1)\n", - " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", - " else:\n", - " topic = \"Default\"\n", - " reasoning = \"\"\n", - " \n", - " pd_df['Predicted_Topic'][i] = topic\n", - " pd_df['Reasoning with Chain of Thought'][i] = reasoning" + " pd_df['Predicted_Topic'][i] = topic\n", + " pd_df['Reasoning with Chain of Thought'][i] = reasoning\n", + " except:\n", + " pass" ] }, { diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/data/embeddings.parquet.gzip b/UseCases/Complaints_Analysis_GenAI_Gemini/data/embeddings.parquet.gzip new file mode 100644 index 0000000000000000000000000000000000000000..ce8be44e54c902997f96d113be5aa59eed7a8fe9 GIT binary patch literal 290815 zcmb4~XFS$z`2TGRSyA>Z3MCb?PCF}r@hbc8?s5yat232AzZa>iNGo;$4l1KGj;}ZmP?b}F#;#kv)eNuL5Q@*;~ z0nwB$2h)9`M7rZIgO*n|CT@JiDW6NhPB~xj{0WCR(_{fB8bN$v^$K7_~}|E_-niYZJR>R@5%BbGSX0Pq-e%61MlHE`ZHI 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    Generate
    the embeddings?
    Generate the embeddings for complaints 
    Load the existing embeddings for complaints from Vantage
    Calculate the K-Means
    Yes
    No
     \ No newline at end of file diff --git a/UseCases/Complaints_Analysis_GenAI_Gemini/requirements.txt b/UseCases/Complaints_Analysis_GenAI_Gemini/requirements.txt index 8db07264..0d9cf2a2 100644 --- a/UseCases/Complaints_Analysis_GenAI_Gemini/requirements.txt +++ b/UseCases/Complaints_Analysis_GenAI_Gemini/requirements.txt @@ -1,2 +1,3 @@ google-generativeai -wordcloud \ No newline at end of file +wordcloud +fastparquet \ No newline at end of file From 500a8b637ed38ba5d80aaf208547f191481ae14c Mon Sep 17 00:00:00 2001 From: chetan-hirapara Date: Tue, 20 Aug 2024 11:36:37 +0000 Subject: [PATCH 7/7] fiixed error in catch --- .../Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb b/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb index 19420ea1..21fafc9f 100644 --- a/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb +++ b/UseCases/Complaints_Analysis_GenAI_Bedrock/Topic_Modelling.ipynb @@ -384,7 +384,7 @@ " reasoning = re.search('Reasoning:(.*)', output).group(1)\n", " pd_df['Predicted_Topic'][i] = topic\n", " pd_df['Reasoning with Chain of Thought'][i] = reasoning\n", - " except exception as e:\n", + " except:\n", " pass" ] },