app.py

import streamlit as st
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch

# Load the tokenizer and model
model_name = 'roberta-base'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained('./results')  # Path to your fine-tuned model

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

# Text inputs for the user
context = st.text_area("Enter the context (movie-related text):")
question = st.text_area("Enter your question:")

def get_answer(context, question):
    inputs = tokenizer.encode_plus(question, context, return_tensors='pt', truncation=True, padding=True)
    input_ids = inputs['input_ids'].tolist()[0]

    # Get the model's answer
    outputs = model(**inputs)
    answer_start_scores = outputs.start_logits
    answer_end_scores = outputs.end_logits

    # Get the most likely beginning and end of the answer span
    answer_start = torch.argmax(answer_start_scores)
    answer_end = torch.argmax(answer_end_scores) + 1

    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
    return answer

if st.button("Get Answer"):
    if context and question:
        answer = get_answer(context, question)
        st.subheader("Answer")
        st.write(answer)
    else:
        st.warning("Please enter both context and question.")

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