app.py

import streamlit as st
import os
from tempfile import NamedTemporaryFile
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain import PromptTemplate, LLMChain
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

# Function to save the uploaded PDF to a temporary file
def save_uploaded_file(uploaded_file):
    with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
        temp_file.write(uploaded_file.read())
        return temp_file.name

# Streamlit UI
st.title("PDF Question Answering App")
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file is not None:
    # Save the uploaded file to a temporary location
    temp_file_path = save_uploaded_file(uploaded_file)

    # Load the PDF document using PyPDFLoader
    loader = PyPDFLoader(temp_file_path)
    pages = loader.load_and_split()

    # Initialize embeddings and Chroma
    embed = HuggingFaceEmbeddings()
    db = Chroma.from_documents(pages, embed)

    # Define a function to get answers
    def get_answer(question):
        doc = db.similarity_search(question, k=4)
        context = doc[0].page_content + doc[1].page_content + doc[2].page_content + doc[3].page_content

        #max_seq_length = 512  # You may define this based on your model

        #context = context[:max_seq_length]

        # Load the model & tokenizer for question-answering
        model_name = "deepset/roberta-base-squad2"
        model = AutoModelForQuestionAnswering.from_pretrained(model_name)
        tokenizer = AutoTokenizer.from_pretrained(model_name)

        # Create a question-answering pipeline
        nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)

        # Prepare the input
        QA_input = {
            "question": question,
            "context": context,
        }

        # Get the answer
        result = nlp(**QA_input)

        return result["answer"]

    question = st.text_input("Enter your question:")
    if st.button("Get Answer"):
        answer = get_answer(question)
        st.write("Answer:")
        st.write(answer)

    # Cleanup: Delete the temporary file
    os.remove(temp_file_path)