app.py

import streamlit as st
from transformers import pipeline

# Title and Description
st.title("WhiteRabbitNEO Q&A App")
st.write("Ask any question, and WhiteRabbitNEO will provide an answer.")

# Initialize the model pipeline
@st.cache_resource
def load_model():
    try:
        # Simple model pipeline initialization
        model = pipeline("question-answering", model="WhiteRabbitNEO")
        return model
    except Exception as e:
        st.error(f"Failed to load model: {e}")
        return None

# Load the model
qa_pipeline = load_model()

# Simple input section: Ask question and provide context
if qa_pipeline:
    question = st.text_input("Your question:")
    context = st.text_area("Context (provide background info for the question):")

    # Button to trigger the model for prediction
    if st.button("Get Answer"):
        if question and context:
            try:
                # Get prediction from the model
                result = qa_pipeline(question=question, context=context)
                # Display the answer
                st.write(f"**Answer:** {result['answer']}")
                st.write(f"**Confidence Score:** {result['score']:.2f}")
            except Exception as e:
                st.error(f"Error generating answer: {e}")
        else:
            st.warning("Please fill both the question and context fields.")
else:
    st.error("Model could not be loaded. Please check your configuration.")