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pdf_crossref.py
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pdf_crossref.py
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import os, tempfile
import streamlit as st
from langchain.llms.openai import OpenAI
from langchain.vectorstores.chroma import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains.summarize import load_summarize_chain
from langchain.prompts import ChatPromptTemplate
from langchain.document_loaders import PyPDFLoader
from langchain.chains import SequentialChain
from langchain.retrievers import WikipediaRetriever
from langchain.chains import SimpleSequentialChain
# define which LLM model to use - this is constructed for OpenAI
import datetime
current_date = datetime.datetime.now().date()
# Define the date after which the model should be set to "gpt-3.5-turbo"
target_date = datetime.date(2024, 6, 12)
# Set the model variable based on the current date
if current_date > target_date:
llm_model = "gpt-3.5-turbo"
else:
llm_model = "gpt-3.5-turbo-0301"
# Streamlit app
st.subheader('PDF Summarize and Cross Reference Tool')
# Get OpenAI API key and source pdf input
with st.sidebar:
openai_api_key = st.text_input("OpenAI API key", value="", type="password")
st.caption("*If you don't have an OpenAI API key, get one [here](https://platform.openai.com/account/api-keys).*")
source_doc = st.file_uploader("Source Document", label_visibility="collapsed", type="pdf")
# If the 'Ask' button is clicked
if st.button("Summarize and Cross Reference"):
# Validate inputs
if not openai_api_key.strip() or not source_doc:
st.error(f"Please provide the missing fields/keys.")
else:
try:
with st.spinner('Thinking...'):
# Save uploaded file temporarily to disk, load and split the file into pages
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(source_doc.read())
loader = PyPDFLoader(tmp_file.name)
pages = loader.load_and_split()
os.remove(tmp_file.name)
# Create embeddings for the pages and insert into Chroma database
embeddings=OpenAIEmbeddings(openai_api_key=openai_api_key)
vectordb = Chroma.from_documents(pages, embeddings)
# Initialize the OpenAI module, load and run the summarize chain
llm=ChatOpenAI(temperature=0, openai_api_key=openai_api_key)
# prompt 1
prompt1 = ChatPromptTemplate.from_template(
"Write a summary within 300 words, emphasize intended \
competitive advantage."
)
# Chain 1
chain1 = load_summarize_chain(llm=llm, chain_type="stuff", output_key="summary")
summary = chain1.run(input_documents=pages, question=prompt1)
# prompt 2
prompt2 = ChatPromptTemplate.from_template(
"Lookup the top 5 competitors against a product that is \
summarized by {summary} for the same demographic market \
and list the top 3 competitive advantages for the first product in \
relation to the competitors. Also list the top 3 competitive \
disadvantages for the first product and what could be developed\
to be more competitive"
)
# chain 2
formatted_prompt2 = prompt2.format(summary=summary)
chain2 = LLMChain(llm=llm, prompt=formatted_prompt2, output_key="analysis")
# master chain
full_output = SequentialChain(
chains=[chain1, chain2],
input_documents=pages,
question=prompt1,
input_variables=["prompt1"],
output_variables=["summary", "analysis"],
verbose=True
)
st.success(full_output)
except Exception as e:
st.exception(f"An error occurred: {e}")