practice.py

from dotenv import load_dotenv
import os
from PyPDF2 import PdfReader
import streamlit as st
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
import tiktoken
from langchain.callbacks import get_openai_callback
import openai
from text2voice import text_to_speech

load_dotenv()

def process_text(text):
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
   
    os.environ["OPENAI_API_KEY"] = os.getenv('MY_API_KEY')
    embeddings = OpenAIEmbeddings()
    knowledgeBase = FAISS.from_texts(chunks, embeddings)

    return knowledgeBase
   
def main():
    st.title("pdf")
    pdf = st.file_uploader('Upload your PDF Document', type='pdf')
    if pdf is not None:
        pdf_reader = PdfReader(pdf)
        text = ""
        for page in pdf_reader.pages:
            text += page.extract_text()
        
        knowledgeBase = process_text(text)
        
        query = st.text_input('Ask a question to the PDF')
        cancel_button = st.button('Cancel')
        
        if cancel_button:
            st.stop()
        
        if query:
            docs = knowledgeBase.similarity_search(query)
            chain = load_qa_chain(llm=OpenAI(), chain_type="stuff")
            response = chain.run(input_documents=docs, question=query)
                        
            with get_openai_callback() as cost:
                response = chain.run(input_documents=docs, question=query)
                print(cost)
                
            st.write(response)
            text_to_speech(response)
            
            

            
if __name__ == "__main__":
    main()