main.py

import os
from dotenv import load_dotenv
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback


def main():
    load_dotenv()
    # print(os.getenv('OPENAI_API_KEY'))
    st.set_page_config(page_title="Ask your PDF")
    st.header("Ask your PDF 💬")
    
    # upload file
    pdf = st.file_uploader("Upload your PDF", type="pdf")
    
    if pdf is not None:
        # read pdf
        pdf_reader = PdfReader(pdf)

        
        
        # extract text
        text=''
        for page in pdf_reader.pages:
            text += page.extract_text()
            
        # display text
        # st.write(text)
        
        # split into chunks
        text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
        )
        chunks = text_splitter.split_text(text)
        # st.write(chunks)
        
        
        # create embedding
        embeddings = OpenAIEmbeddings()
        knowledge_base = FAISS.from_texts(chunks, embeddings)
        
        
        user_question = st.text_input("Ask a question about your PDF:")
        if user_question:
            docs = knowledge_base.similarity_search(user_question)
            # st.write(docs)
            
            llm = OpenAI()
            chain = load_qa_chain(llm, chain_type="stuff")
            with get_openai_callback() as cb:
                response = chain.run(input_documents=docs, question=user_question)
                print(cb)
           
            st.write(response)

if __name__ == '__main__':
    main()
    
    
'''
{
    "builds": [{
        "src": "llmModelAi/llmModelAi/wsgi.py",
        "use": "@vercel/python",
        "config": { "maxLambdaSize": "15mb", "runtime": "python3.9" }
    }],
    "routes": [
        {
            "src": "/(.*)",
            "dest": "llmModelAi/llmModelAi/wsgi.py"
        }
    ]
}
'''