app.py

import gradio as gr
from transformers import pipeline

# Try loading the model with a fallback for any loading errors
try:
    print("Loading the model...")
    qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
    print("Model loaded successfully.")
except Exception as e:
    # Print error message for debugging purposes
    print(f"Error loading model: {e}")
    qa_pipeline = None

# Define the function that takes inputs and returns the answer
def answer_question(context, question):
    if qa_pipeline is None:
        return "Error: Model not loaded."
    result = qa_pipeline(question=question, context=context)
    return result['answer']

# Create the Gradio interface
interface = gr.Interface(
    fn=answer_question, 
    inputs=[gr.Textbox(lines=7, label="Context (Enter the passage)"), gr.Textbox(lines=2, label="Question")],
    outputs="text",
    title="Question Answering Model",
    description="Ask a question based on the given context.",
)

# Print a message before launching the app to confirm it's starting
print("Launching the Gradio interface...")
# Launch the interface
interface.launch()