app.py

import streamlit as st
from transformers import AutoModelForQuestionAnswering, AutoTokenizer

# Load the pre-trained model and tokenizer
model_name = "facebook/bart-base-squad2"
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

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

  # Use the model to get the answer
  with torch.no_grad():
    outputs = model(**inputs)
    start_scores, end_scores = outputs.start_logits, outputs.end_scores

  # Find the most likely answer span
  answer_start = torch.argmax(start_scores)
  answer_end = torch.argmax(end_scores) + 1

  # Extract the answer from the context
  answer = tokenizer.convert_tokens_to_string(context)[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.")