app.py

import streamlit as st
import os
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.callbacks import get_openai_callback
from langchain import HuggingFaceHub, LLMChain
from langchain.embeddings import HuggingFaceHubEmbeddings,HuggingFaceInferenceAPIEmbeddings
token = os.environ['HF_TOKEN']="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"
repo_id = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceHubEmbeddings(
    repo_id=repo_id,
    task="feature-extraction",
    huggingfacehub_api_token= token,
)

from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings

embeddings = HuggingFaceInferenceAPIEmbeddings(
    api_key=token, model_name="sentence-transformers/all-MiniLM-l6-v2"
)


def main():
    
    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")
    
    # extract the text
    if pdf is not None:
      pdf_reader = PdfReader(pdf)
      text = ""
      for page in pdf_reader.pages:
        text += page.extract_text()
        
      # split into chunks
      text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
      )
      chunks = text_splitter.split_text(text)
      
      # create embeddings
      # embeddings = OpenAIEmbeddings()
      # embeddings = query(chunks)
      # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
      
      knowledge_base = FAISS.from_texts(chunks, embeddings)
      
      # show user input
      user_question = st.text_input("Ask a question about your PDF:")
      if user_question:
        docs = knowledge_base.similarity_search(user_question)
        
        # llm = OpenAI()

        hub_llm = HuggingFaceHub(
        repo_id='HuggingFaceH4/zephyr-7b-beta',
    model_kwargs={'temperature':0.01,"max_length": 2048,},
    huggingfacehub_api_token=token)
        llm = hub_llm
        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()