app.py

import gradio as gr

from transformers import pipeline




# Load a pre-trained question answering pipeline

question_answerer = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")




def answer_question(context, question):

    """

    Takes context and a question as input and returns the predicted answer.

    """

    if context and question:

        result = question_answerer(question=question, context=context)

        answer = result['answer']

        confidence = f"{result['score']:.4f}"

        return f"Answer: {answer}", f"Confidence: {confidence}"

    else:

        return "Please provide both context and a question.", ""




# Define the Gradio interface

iface = gr.Interface(

    fn=answer_question,

    inputs=[

        gr.Textbox(lines=7, placeholder="Enter the context here..."),

        gr.Textbox(placeholder="Ask a question about the context...")

    ],

    outputs=[

        gr.Textbox(label="Predicted Answer"),

        gr.Textbox(label="Confidence Score")

    ],

    title="Simple Question Answering",

    description="Enter a block of text (context) and then ask a question about it. The app will try to find the answer within the text.",

)




# Launch the Gradio app

iface.launch()