app.py

from transformers import pipeline
import gradio as gr

# 1) Load your fine-tuned QA model from the Hub
MODEL_ID = "omarbayoumi2/bert-base-qa-squad-colab"

qa = pipeline(
    "question-answering",
    model=MODEL_ID,
    tokenizer=MODEL_ID,
)

# 2) Inference function
def answer(question, context):
    if not question or not context:
        return "Please provide both a question and a context."
    result = qa(question=question, context=context)
    # result is a dict: {'score': ..., 'start': ..., 'end': ..., 'answer': ...}
    return result["answer"]

# 3) Build Gradio interface
iface = gr.Interface(
    fn=answer,
    inputs=[
        gr.Textbox(label="Question", placeholder="Ask a question about the context..."),
        gr.Textbox(label="Context", lines=8, placeholder="Paste the context paragraph here..."),
    ],
    outputs=gr.Textbox(label="Answer"),
    title="BERT-base SQuAD QA Demo",
    description=(
        "Fine-tuned `bert-base-uncased` on SQuAD v1.1.\n"
        "Model: omarbayoumi2/bert-base-qa-squad-colab"
    ),
)

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