app.py

import torch
import streamlit as st
from transformers import BertTokenizer, BertForQuestionAnswering

# Utilize BertForQuestionAnswering model for direct start/end logits
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")

def answer_query(question, context):
    # Preprocess using tokenizer
    inputs = tokenizer(question, context, return_tensors="pt")

    # Use model for question answering
    with torch.no_grad():
        outputs = model(**inputs)

        # Retrieve logits directly
        start_logits = outputs.start_logits
        end_logits = outputs.end_logits

    # Find answer span
    answer_start = torch.argmax(start_logits)
    answer_end = torch.argmax(end_logits) + 1

    # Extract answer from context
    answer = tokenizer.convert_tokens_to_string(
        tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])  # Access original tokens
    )[answer_start:answer_end]

    return answer


# Streamlit app
st.title("Question Answering App")

# Textbox for user query
user_query = st.text_input("Enter your question:")

# File uploader for context
uploaded_file = st.file_uploader("Upload a context file (txt):")

if uploaded_file is not None:
    # Read the uploaded file content
    context = uploaded_file.read().decode("utf-8")
else:
    # Use default context if no file uploaded
    context = "This is a sample context for demonstration purposes. You can upload your own text file for context."

# Answer the query if a question is provided
if user_query:
    answer = answer_query(user_query, context)
    st.write(f"Answer: {answer}")
else:
    st.write("Please enter a question.")