DataChallenges_Presentation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBlCAqWRpcEn"
      },
      "source": [
        "#Clone repository containing test data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0ot1w3VgF-5",
        "outputId": "f9d4baf2-43c2-48ca-ec3d-a02cf34dea24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'DataChallengesSoSe22'...\n",
            "remote: Enumerating objects: 30, done.\u001b[K\n",
            "remote: Counting objects: 100% (30/30), done.\u001b[K\n",
            "remote: Compressing objects: 100% (25/25), done.\u001b[K\n",
            "remote: Total 30 (delta 5), reused 13 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (30/30), done.\n"
          ]
        }
      ],
      "source": [
        "%cd /content/\n",
        "! git clone https://github.com/ToastyDom/DataChallengesSoSe22.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install the latest master of Haystack\n"
      ],
      "metadata": {
        "id": "GG9WTciDlvJf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade pip\n",
        "!pip install git+https://github.com/deepset-ai/haystack.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKYFzCLkl0yG",
        "outputId": "2ae0387a-cd42-4da0-a33c-6840b91a975f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[K     |████████████████████████████████| 2.0 MB 4.7 MB/s \n",
            "\u001b[?25hInstalling collected packages: pip\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/deepset-ai/haystack.git\n",
            "  Cloning https://github.com/deepset-ai/haystack.git to /tmp/pip-req-build-mbnin5c2\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/deepset-ai/haystack.git /tmp/pip-req-build-mbnin5c2\n",
            "  Resolved https://github.com/deepset-ai/haystack.git to commit 4e45062a00da18c7c039c24b1ea70641e79f82a8\n",
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            "Building wheels for collected packages: farm-haystack, sentence-transformers, langdetect, python-docx, seqeval, tika, databricks-cli\n",
            "  Building wheel for farm-haystack (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for farm-haystack: filename=farm_haystack-1.6.1rc0-py3-none-any.whl size=598702 sha256=84dcf7de064ccb09f9b9388bfaad4cfb7f17c9a7a8a4c0b2059b33312824894e\n",
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            "Installing collected packages: tokenizers, sentencepiece, monotonic, mmh3, azure-common, websocket-client, smmap, querystring-parser, pyyaml, python-docx, pyjwt, num2words, langdetect, jarowinkler, isodate, gunicorn, elasticsearch, elastic-apm, backoff, tika, rapidfuzz, quantulum3, posthog, Mako, huggingface-hub, gitdb, docker, databricks-cli, azure-core, transformers, seqeval, prometheus-flask-exporter, msrest, gitpython, alembic, sentence-transformers, mlflow, azure-ai-formrecognizer, farm-haystack\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed Mako-1.2.1 alembic-1.8.1 azure-ai-formrecognizer-3.2.0b2 azure-common-1.1.28 azure-core-1.22.1 backoff-1.11.1 databricks-cli-0.17.0 docker-5.0.3 elastic-apm-6.10.1 elasticsearch-7.10.1 farm-haystack-1.6.1rc0 gitdb-4.0.9 gitpython-3.1.27 gunicorn-20.1.0 huggingface-hub-0.7.0 isodate-0.6.1 jarowinkler-1.2.0 langdetect-1.0.9 mlflow-1.27.0 mmh3-3.0.0 monotonic-1.6 msrest-0.6.21 num2words-0.5.10 posthog-1.4.9 prometheus-flask-exporter-0.20.2 pyjwt-2.4.0 python-docx-0.8.11 pyyaml-6.0 quantulum3-0.7.10 querystring-parser-1.2.4 rapidfuzz-2.3.0 sentence-transformers-2.2.2 sentencepiece-0.1.96 seqeval-1.2.2 smmap-5.0.0 tika-1.24 tokenizers-0.12.1 transformers-4.20.1 websocket-client-1.3.3\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run Elastic Server"
      ],
      "metadata": {
        "id": "yZva6d8BnH2l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Start Elasticsearch from source\n",
        "! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q\n",
        "! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz\n",
        "! chown -R daemon:daemon elasticsearch-7.9.2\n",
        "\n",
        "import os\n",
        "from subprocess import Popen, PIPE, STDOUT\n",
        "\n",
        "es_server = Popen(\n",
        "    [\"elasticsearch-7.9.2/bin/elasticsearch\"], stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1)  # as daemon\n",
        ")\n",
        "# wait until ES has started\n",
        "! sleep 30"
      ],
      "metadata": {
        "id": "UNr6HRaVnM5P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import relevant packages"
      ],
      "metadata": {
        "id": "Yn09j5uWl3x4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "from pprint import pprint\n",
        "import logging\n",
        "\n",
        "from haystack.document_stores import ElasticsearchDocumentStore\n",
        "from haystack.utils import clean_wiki_text, convert_files_to_docs, fetch_archive_from_http, print_answers\n",
        "from haystack.nodes import FARMReader, TransformersReader\n",
        "\n",
        "# Important Imports\n",
        "from haystack.pipelines import ExtractiveQAPipeline\n",
        "from haystack.nodes import BM25Retriever\n",
        "from haystack.pipelines import GenerativeQAPipeline\n",
        "from haystack.nodes import RAGenerator\n",
        "from haystack.nodes import BM25Retriever, EmbeddingRetriever, FARMReader\n",
        "from haystack.pipelines import DocumentSearchPipeline\n",
        "from haystack.utils import print_documents\n",
        "from haystack.nodes import BM25Retriever\n",
        "from haystack import Pipeline\n",
        "from haystack.nodes import EmbeddingRetriever"
      ],
      "metadata": {
        "id": "sRCWC3lol3Lr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Taking the JSON-Data and putting it into the neccessary format"
      ],
      "metadata": {
        "id": "tKMlhIb2lkwu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p-A-kjtop1Fq"
      },
      "outputs": [],
      "source": [
        "file = open('DataChallengesSoSe22/data/db_bahn_tweets_2_years.json', 'r')\n",
        "lines = file.readlines()\n",
        "\n",
        "extracted_tweets = []\n",
        "\n",
        "for line in lines:\n",
        "  tweet = json.loads(line)\n",
        "  extracted_tweets.append([\n",
        "    tweet[\"username\"],\n",
        "    tweet[\"created_at\"],\n",
        "    tweet[\"tweet\"],\n",
        "    tweet[\"id\"],\n",
        "    tweet[\"link\"]\n",
        "  ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KfwVViBmr2fF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8ae2f9e-4ca8-4edf-a200-61a2f7201d82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "71379\n",
            "['db_bahn', '2020-12-31 21:00:37 UTC', 'Ihr Lieben, wir verabschieden uns nun für dieses Jahr von euch! 🎉 Rutscht oder rollt (ich rolle, weil vollgefuttert 🙈 😂) ins neue Jahr 2021! 🥳 Richtet den Blick voller Zuversicht nach vorne, fühlt euch von uns gedrückt und bleibt gesund. 🍀 🥂 /at', 1344750330848931846, 'https://twitter.com/DB_Bahn/status/1344750330848931846']\n",
            "['db_bahn', '2020-12-31 20:46:37 UTC', '@atYildir Schon geschehen. /da', 1344746806383669268, 'https://twitter.com/DB_Bahn/status/1344746806383669268']\n",
            "['db_bahn', '2020-12-31 20:40:59 UTC', '@atYildir Da die Strecke von der HLB betreiben wird, haben wir von den Ticketeinnahmen nichts. Dort verkehren aber auch Züge der Linie RB 41, die von DB Regio betrieben wird. Wann möchtest du von Frankfurt Hbf nach Marburg(Lahn) fahren? /da', 1344745390076260359, 'https://twitter.com/DB_Bahn/status/1344745390076260359']\n",
            "['db_bahn', '2020-12-31 18:53:31 UTC', '@SebastianKueck Danke für die lieben Worte. Wir wünschen dir einen guten Rutsch in ein gesundes &amp; glückliches neues Jahr, Sebastian. 🥂🎇🍀 /be', 1344718345191501824, 'https://twitter.com/DB_Bahn/status/1344718345191501824']\n",
            "['db_bahn', '2020-12-31 16:30:11 UTC', '@zaini_na03 Das geben wir gerne zurück. Einen guten Start in ein gesundes und glückliches neues Jahr, Zain. 🍀🥂🎇 Liebe Grüße /be', 1344682272520339458, 'https://twitter.com/DB_Bahn/status/1344682272520339458']\n",
            "['db_bahn', '2020-12-31 16:20:12 UTC', '@User4818 🤗 Danke schön. Bleib gesund und komm gut ins neue Jahr! 🍀 🎉 🥂 /at', 1344679761298251783, 'https://twitter.com/DB_Bahn/status/1344679761298251783']\n",
            "['db_bahn', '2020-12-31 14:44:30 UTC', '@MrLemey Vielen Dank für den Hinweis. Wünsche dir auch einen guten Rutsch. /mc', 1344655677508169730, 'https://twitter.com/DB_Bahn/status/1344655677508169730']\n",
            "['db_bahn', '2020-12-31 14:34:47 UTC', '@Verti92 Dann frohes Rollen ins neue Jahr, ich werde es nachher auch so handhaben. 😂 Liebe Grüße🎉 🥂  🍀 /at', 1344653231931215874, 'https://twitter.com/DB_Bahn/status/1344653231931215874']\n",
            "['db_bahn', '2020-12-31 14:30:00 UTC', 'Wir rutschen nicht, wir rollen. 😉 Wir wünschen euch allen ein gutes Rein-Rollen ins neue Jahr 2021! Frohes Neues ✨🥂🎊   #FrohesNeues #Silvester #Happy2021  https://t.co/fqSTcLByQW', 1344652029428256771, 'https://twitter.com/DB_Bahn/status/1344652029428256771']\n",
            "['db_bahn', '2020-12-31 13:28:45 UTC', '@StoerungenBahn Vielen lieben Dank, den wünschen wir euch auch! 🤗 Bleibt gesund! /aj', 1344636612446191619, 'https://twitter.com/DB_Bahn/status/1344636612446191619']\n"
          ]
        }
      ],
      "source": [
        "\"\"\"Testing if all the tweets are shown\"\"\"\n",
        "\n",
        "print(len(extracted_tweets))\n",
        "\n",
        "for tweet in extracted_tweets[0:10]:\n",
        "  print(tweet)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6Hke_scsYsT"
      },
      "source": [
        "# Adding data to Haystack Pipeline\n",
        "Reader: Close Anaylsis of documents, perform core task of question answering. Trained from the latest transformer\n",
        "\n",
        "Retriever: Assists reader as filter for documents. Quickly identifies relevant parts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Bs6XCpNHx8v"
      },
      "source": [
        "Haystack looks at documents stored in a class called \"Document Store\"\n",
        "\n",
        "First we need to start an ElasticSearch (like a Bert-Model) server on local machine. Usually using docker but we can execute elastic search from source as well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8AGazcdhIIcS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0990885-9c0f-4c06-d00c-c2400f3ba199"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - haystack.telemetry -  Haystack sends anonymous usage data to understand the actual usage and steer dev efforts towards features that are most meaningful to users. You can opt-out at anytime by calling disable_telemetry() or by manually setting the environment variable HAYSTACK_TELEMETRY_ENABLED as described for different operating systems on the documentation page. More information at https://haystack.deepset.ai/guides/telemetry\n"
          ]
        }
      ],
      "source": [
        "# Connect to Elasticsearch\n",
        "\n",
        "document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\", index=\"document\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUTCHXzRQJ7P"
      },
      "source": [
        "Now we have Elastic Search running and a yet-empty document_store. Now we need to fill our document store with our data!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bm5G0X4yLw_z"
      },
      "outputs": [],
      "source": [
        "# Write function to put json data into document store format\n",
        "\"\"\"\n",
        "dicts = [\n",
        "    {\n",
        "        'content': DOCUMENT_TEXT_HERE,\n",
        "        'meta': {'name': DOCUMENT_NAME, ...}\n",
        "    }, ...\n",
        "]\n",
        "\n",
        "[author_name, tweet_date, tweet, id, url]\n",
        "\"\"\"\n",
        "\n",
        "def to_dstore(json_data):\n",
        "  dicts = []\n",
        "  for element in json_data:\n",
        "    current_dict = {'content': element[2],\n",
        "                    'meta': {'author': element[0], 'date': element[1], 'url': element[4]}}\n",
        "    dicts.append(current_dict)\n",
        "\n",
        "  return dicts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8MsvmmE4MaPD"
      },
      "outputs": [],
      "source": [
        "docs = to_dstore(extracted_tweets)\n",
        "document_store.write_documents(docs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qgu5Ip31uED2"
      },
      "source": [
        "# Extractive Q&A Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
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            "/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py:364: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  \"Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 \"\n",
            "WARNING - haystack.nodes.retriever._embedding_encoder -  You are using a Sentence Transformer with the dot_product function. We recommend using cosine instead. This can be set when initializing the DocumentStore\n",
            "INFO - haystack.document_stores.elasticsearch -  Updating embeddings for all 71351 docs ...\n"
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            "INFO - haystack.modeling.utils -  Using devices: CUDA:0\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n"
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          "text": [
            "INFO - haystack.modeling.model.language_model -   * LOADING MODEL: 'deepset/gelectra-base-germanquad' (Electra)\n"
          ]
        },
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        {
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          "text": [
            "INFO - haystack.modeling.model.language_model -  Auto-detected model language: german\n",
            "INFO - haystack.modeling.model.language_model -  Loaded 'deepset/gelectra-base-germanquad' (Electra model) from model hub.\n"
          ]
        },
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          "text": [
            "INFO - haystack.modeling.utils -  Using devices: CUDA\n",
            "INFO - haystack.modeling.utils -  Number of GPUs: 1\n",
            "INFO - haystack.modeling.infer -  Got ya 2 parallel workers to do inference ...\n",
            "INFO - haystack.modeling.infer -   0     0  \n",
            "INFO - haystack.modeling.infer -  /w\\   /w\\ \n",
            "INFO - haystack.modeling.infer -  /'\\   / \\ \n"
          ]
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      ],
      "source": [
        "retriever = EmbeddingRetriever(\n",
        "    document_store=document_store,\n",
        "   embedding_model=\"clips/mfaq\",\n",
        "   model_format=\"sentence_transformers\"\n",
        ")\n",
        "document_store.update_embeddings(retriever)\n",
        "\n",
        "# Adding reader.\n",
        "reader = FARMReader(model_name_or_path=\"deepset/gelectra-base-germanquad\", use_gpu=True)\n",
        "\n",
        "p_extractive = Pipeline()\n",
        "p_extractive.add_node(component=retriever, name=\"Retriever\", inputs=[\"Query\"])\n",
        "p_extractive.add_node(component=reader, name=\"Reader\", inputs=[\"Retriever\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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          "height": 729,
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 55.24 Batches/s]\n",
            "WARNING - root -  print_answers received details='maximum', which was not understood. \n",
            "WARNING - root -  Valid values are minimum, medium and 'all'. Using 'all'.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Query: Wiviel kostet das 9€ Ticket?\n",
            "Answers:\n",
            "[   <Answer {'answer': '46 Euro', 'type': 'extractive', 'score': 0.8925023674964905, 'context': 'en Verbundshop gegangen? Ich habe es gerade auf iOS getestet und kann dort ein 10er Ticket ab Korschenbroich Geltungsbereich B für 46 Euro buchen. /ne', 'offsets_in_document': [{'start': 193, 'end': 200}], 'offsets_in_context': [{'start': 131, 'end': 138}], 'document_id': 'b0d82054126a22c2bebcd194c9404fd9', 'meta': {'date': '2021-11-12 14:20:35 UTC', 'author': 'db_bahn', 'url': 'https://twitter.com/DB_Bahn/status/1459164221464272901'}}>,\n",
            "    <Answer {'answer': '151,15 Euro', 'type': 'extractive', 'score': 0.7013881802558899, 'context': 'cht, wie du auf 194 Euro kommst, der Flexpreis mit BahnCard 25 kostet 151,15 Euro, Super Sparpreis Europa für morgen gibt es noch für 97,90 Euro. Hast', 'offsets_in_document': [{'start': 105, 'end': 116}], 'offsets_in_context': [{'start': 70, 'end': 81}], 'document_id': '62b648964e38953fcc3a9b812e20a0ea', 'meta': {'date': '2020-06-17 05:57:28 UTC', 'author': 'db_bahn', 'url': 'https://twitter.com/DB_Bahn/status/1273132635217330177'}}>,\n",
            "    <Answer {'answer': '21,30€', 'type': 'extractive', 'score': 0.5292593091726303, 'context': 'gn_dennis Wie meinst du das? Was ist das 2,4fache? Die 7,10€ mal 3 sind 21,30€. So groß ist die Differenz zwischen 55,90€ und 37,90€ nicht, das sind 1', 'offsets_in_document': [{'start': 74, 'end': 80}], 'offsets_in_context': [{'start': 72, 'end': 78}], 'document_id': '48ac88c849cfa9069cca6add7088cbd7', 'meta': {'date': '2022-01-10 11:46:25 UTC', 'author': 'db_bahn', 'url': 'https://twitter.com/DB_Bahn/status/1480506311196434444'}}>]\n"
          ]
        }
      ],
      "source": [
        "# Now we can run it\n",
        "res = p_extractive.run(\n",
        "    query=\"Wiviel kostet das 9€ Ticket?\", params={\"Retriever\": {\"top_k\": 30}, \"Reader\": {\"top_k\": 3}}\n",
        ")\n",
        "print_answers(res, details=\"maximum\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBglr7q7U1Jo"
      },
      "source": [
        "# Frontend Interface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qZM44UDYU8kb"
      },
      "outputs": [],
      "source": [
        "# Disable info logs\n",
        "logger = logging.getLogger()\n",
        "logger.setLevel(logging.CRITICAL)\n",
        "\n",
        "def ask_chatbot(question):\n",
        "  if question == \"\":\n",
        "    return \"\", \"\"\n",
        "  prediction = p_extractive.run(\n",
        "    query=question,\n",
        "    params={\"Retriever\": {\"top_k\": 30}, \"Reader\": {\"top_k\": 1}}\n",
        "  )\n",
        "  if prediction[\"answers\"]:\n",
        "    return prediction[\"answers\"][0].answer, prediction[\"answers\"][0].meta[\"url\"]\n",
        "  else:\n",
        "    return \"\", \"\""
      ]
    },
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        "outputId": "39e09fa7-5970-4a4b-fec9-f3448d2710ad"
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        {
          "output_type": "display_data",
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            "text/plain": [
              "HTML(value='<style>\\n.app { \\n  background-color: #eba8a2;\\n  padding: 50px 200px;\\n  max-width: 1000px;\\n}\\n.…"
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              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
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          "text": [
            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 29.01 Batches/s]\n",
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            "Inferencing Samples: 100%|██████████| 1/1 [00:00<00:00, 55.33 Batches/s]\n"
          ]
        }
      ],
      "source": [
        "from ipywidgets import *\n",
        "\n",
        "\n",
        "style = HTML(value=\"\"\"<style>\n",
        ".app { \n",
        "  background-color: #eba8a2;\n",
        "  padding: 50px 200px;\n",
        "  max-width: 1000px;\n",
        "}\n",
        ".logo {\n",
        "  background: url(https://raw.githubusercontent.com/ToastyDom/DataChallengesSoSe22/main/logo.png);\n",
        "  height: 80px;\n",
        "  background-position: center;\n",
        "  background-repeat: no-repeat;\n",
        "  background-size: contain;\n",
        "}\n",
        ".messages {\n",
        "  height: 300px;\n",
        "  margin: 40px 0;\n",
        "  overflow-y: scroll;\n",
        "  flex-direction: column-reverse;\n",
        "}\n",
        ".message {\n",
        "  padding: 10px;\n",
        "  margin: 5px 0px;\n",
        "  background-color: #f5e1df;\n",
        "  border-radius: 10px;\n",
        "  width: 70%;\n",
        "}\n",
        "a {\n",
        "  color: black;\n",
        "  text-decoration: none;\n",
        "}\n",
        ".message.right {\n",
        "  margin-left: 25%;\n",
        "  border-top-right-radius: 0px;\n",
        "}\n",
        ".message.left {\n",
        "  border-top-left-radius: 0px;\n",
        "}\n",
        ".input {\n",
        "  width: 75%;\n",
        "}\n",
        ".button {\n",
        "  width: 25%;\n",
        "}\n",
        "</style>\"\"\")\n",
        "\n",
        "input = Text(placeholder=\"Frage eingeben\")\n",
        "button = Button(description=\"Fragen\")\n",
        "messages = VBox([\n",
        "  HTML(value='<div class=\"message left\">Hi, wie kann ich dir helfen?</div>'),\n",
        "])\n",
        "messages.add_class('messages')\n",
        "app = VBox([\n",
        "  HTML(value='<div class=\"logo\"></div>'),\n",
        "  messages,\n",
        "  HBox([input, button]),\n",
        "])\n",
        "app.add_class('app')\n",
        "input.add_class('input')\n",
        "button.add_class('button')\n",
        "\n",
        "def on_send_message(b):\n",
        "  question = input.value\n",
        "  answer, url = ask_chatbot(question)\n",
        "  messages.children = (\n",
        "    HTML(value=f'<a href=\"{url}\" target=\"_blank\"><div class=\"message left\">{answer}</div></a>'),\n",
        "    HTML(value=f'<div class=\"message right\">{question}</div>'),\n",
        "    *messages.children,\n",
        "  )\n",
        "\n",
        "button.on_click(on_send_message)\n",
        "input.on_submit(on_send_message)\n",
        "\n",
        "\n",
        "display(style, app)"
      ]
    }
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