app.py

import streamlit as st
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain.vectorstores import FAISS
import os
import pickle
from pdfreader import read_pdf

os.environ["OPENAI_API_KEY"] = "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"

# Mapping of PDFs
pdf_mapping = {
    'Lupin': 'LUPIN.pdf',
    'INDUSTOWER': 'INDUSTOWER.pdf',
    'M&M': 'M&M_Ltd.pdf',
    'BAJAJFINSERV': 'BAJAJFINSERV.pdf',
    'SBIN': 'SBIN.pdf'
    # Add more mappings as needed
}


# Main Streamlit app
def main():
    st.title("Query your PDF")
    with st.sidebar:
        st.title('StockLoom')
        st.markdown('''
        ## About
        Choose the desired Stock, then perform a query.
        ''')

    custom_names = list(pdf_mapping.keys())
    selected_custom_name = st.sidebar.selectbox('Choose your PDF', ['', *custom_names])
    selected_actual_name = pdf_mapping.get(selected_custom_name)
    # print("1234567")
    # print(selected_actual_name)

    if selected_actual_name:
        pdf_folder = "D:\MecHackStockLoom\Stockloom\statement_pdf"
        file_path = os.path.join(pdf_folder, selected_actual_name)
        # print(file_path)

        try:
            text = read_pdf(file_path)
            st.info("The content of the PDF is hidden. Type your query in the chat window.")
        except FileNotFoundError:
            st.error(f"File not found: {file_path}")
            return
        except Exception as e:
            st.error(f"Error occurred while reading the PDF: {e}")
            return

        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=150,
            length_function=len
        )

        # Process the PDF text and create the documents list
        documents = text_splitter.split_text(text=text)

        # Vectorize the documents and create vectorstore
        embeddings = OpenAIEmbeddings()
        vectorstore = FAISS.from_texts(documents, embedding=embeddings)

        st.session_state.processed_data = {
            "document_chunks": documents,
            "vectorstore": vectorstore,
        }

        # Save vectorstore using pickle
        pickle_folder = "Pickle"
        if not os.path.exists(pickle_folder):
            os.mkdir(pickle_folder)

        pickle_file_path = os.path.join(pickle_folder, f"{selected_custom_name}.pkl")

        if not os.path.exists(pickle_file_path):
            with open(pickle_file_path, "wb") as f:
                pickle.dump(vectorstore, f)



        # # Load the Langchain chatbot
        # llm = ChatOpenAI(temperature=0.0, max_tokens=1000, model_name="gpt-3.5-turbo")
        # qa = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever())
        #
        # # Initialize Streamlit chat UI
        # if "messages" not in st.session_state:
        #     st.session_state.messages = []
        #
        # for message in st.session_state.messages:
        #     with st.chat_message(message["role"]):
        #         st.markdown(message["content"])
        #
        # if prompt := st.chat_input("Ask your questions from PDF "f'{selected_custom_name}'"?"):
        #     st.session_state.messages.append({"role": "user", "content": prompt})
        #     with st.chat_message("user"):
        #         st.markdown(prompt)
        #
        #     result = qa({"question": prompt, "chat_history": [(message["role"], message["content"]) for message in
        #                                                       st.session_state.messages]})
        #     print(prompt)
        #
        #     with st.chat_message("assistant"):
        #         message_placeholder = st.empty()
        #         full_response = result["answer"]
        #         message_placeholder.markdown(full_response + "|")
        #     message_placeholder.markdown(full_response)
        #     print(full_response)
        #     st.session_state.messages.append({"role": "assistant", "content": full_response})


if __name__ == "__main__":
    main()