app.py

import streamlit as st
from transformers import pipeline

# Define the path to the saved model
model_path = './QAModel'  # Path to your fine-tuned model

# Load the question-answering pipeline
qa_pipeline = pipeline("question-answering", model=model_path, tokenizer=model_path)

# Load the context from a file
context_file = 'context.txt'  # Replace with your context file path

with open(context_file, 'r', encoding='utf-8') as f:
    default_context = f.read()

# Set the title for the Streamlit app
st.title("Movie Trivia Question Answering")

# Text input for the user question
question = st.text_area("Enter your question:")

def generate_answer(question, context):
    # Perform question answering
    result = qa_pipeline(question=question, context=context)
    return result['answer']

if st.button("Get Answer"):
    if question:
        generated_answer = generate_answer(question, default_context)
        # Display the generated answer
        st.subheader("Answer")
        st.write(generated_answer)
    else:
        st.warning("Please enter a question.")

# Optionally, add instructions or information about the app
st.write("""
    Enter a question related to the provided movie-related context above. The model will provide the answer based on the context provided.
    """)