main.py

import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv
from langchain.chains.question_answering import load_qa_chain
import time



load_dotenv()
# genai.configure(api_key = os.getenv("GOOGLE_API_KEY"))
os.environ['OPENAI_API_KEY'] = os.getenv("OPENAI_API_KEY")
os.environ["LANGCHAIN_TRACING_V2"]=os.getenv("LANGCHAIN_TRACING_V2")
os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")

def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000 , chunk_overlap =1000)
    chunks = text_splitter.split_text(text)
    return chunks

def get_vector_store(text_chunks):
    # embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
    embeddings = OpenAIEmbeddings()
    #model = "gpt-3.5-turbo-0125"
    vector_store = FAISS.from_texts(text_chunks , embedding = embeddings)
    vector_store.save_local("faiss_index_openai")
    return vector_store

def get_conversational_chain():
     prompt_template = """
        Answer the following question in a detailed manner based only on the provided context. 
        Think step by step before providing an answer,make sure to provide all the details
        I will tip you $1000 if the user finds the answer helpful.
        if the answer is not in provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n 
        <context>
        {context}
        </context>
        Question: {question}"""
     llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
    #  model = ChatGoogleGenerativeAI(model="gemini-1.5-flash" , temeperature = 0.3)
     prompt = PromptTemplate(template = prompt_template , input_variables={"context","question"})
    #  chain = load_qa_chain(model , chain_type="stuff" , prompt=prompt)
     chain = load_qa_chain(llm , chain_type="stuff" , prompt=prompt)
     return chain

def user_input(user_question):
    start_time = time.time()
    # embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
    # embeddings = OpenAIEmbeddings()
    
    new_db = st.session_state.get("vector_store")
    docs = new_db.similarity_search(user_question)

    chain = get_conversational_chain()

    placeholder = st.empty()

    response = ""
    for r in chain.stream(
        {"input_documents": docs, "question": user_question},
        return_only_outputs=True):
        response += r["output_text"]
        time.sleep(1)
        placeholder.write(response)
    
    # print(response)
    # st.write("Reply: ", response["output_text"])
    st.write("Time taken: ", time.time() - start_time)

def main():
    st.set_page_config("Chat with multiple PDFs")
    st.header("Chat with multiple PDFs using GPT💁")

    user_question = st.text_input("Ask a Question from the PDF Files")

    if user_question:
        user_input(user_question)

    with st.sidebar:
        st.title("Menu:")
        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
        if st.button("Submit & Process"):
            with st.spinner("Processing..."):
                start_time = time.time()
                raw_text = get_pdf_text(pdf_docs)
                text_chunks = get_text_chunks(raw_text)
                vector_store = get_vector_store(text_chunks)
                st.session_state.vector_store = vector_store
                st.success(f"Done \n Time Taken: {time.time() - start_time}" )
               



if __name__ == "__main__":
    main()