app.py

import gradio as gr
from transformers import pipeline

# Load the question answering model
qa = pipeline("question-answering")

# Define the function to generate the answer
def generate_answer(context, question):
    result = qa(question=question, context=context)
    return result["answer"]

# Define the Gradio interface
inputs = [
    gr.components.Textbox(label="Enter some context"),
    gr.components.Textbox(label="Enter a question")
]
outputs = gr.components.Textbox(label="Answer")

title = "Question Answering with Hugging Face"
description = "Answer questions based on a given context using Hugging Face's question answering model"

iface = gr.Interface(fn=generate_answer, inputs=inputs, outputs=outputs, title=title, description=description)

# Launch the Gradio app
iface.launch()