app.py

import gradio as gr
from transformers import pipeline

# Load pretrained BERT QA model
qa_pipeline = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")

# Define QA function
def answer_question(context, question):
    if not context.strip() or not question.strip():
        return "Please provide both context and question."
    result = qa_pipeline(question=question, context=context)
    return result["answer"]

# Gradio interface
interface = gr.Interface(
    fn=answer_question,
    inputs=[
        gr.Textbox(label="Paragraph (Context)", lines=10, placeholder="Enter a paragraph about Lenin..."),
        gr.Textbox(label="Question", placeholder="Who was Lenin?")
    ],
    outputs=gr.Textbox(label="Answer"),
    title="BERT Question Answering",
    description="Ask a question based on a custom paragraph using a BERT-based QA model.",
)

if __name__ == "__main__":
    interface.launch()