app.py

# app.py

from transformers import pipeline
import gradio as gr

# Load the Question Answering pipeline with the selected model
qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")

# Define the function that performs question answering
def answer_question(context, question):
    result = qa_pipeline(question=question, context=context)
    return result['answer']

# Build the Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("""
    # 🔍 AskMe: AI Question Answering App
    Enter a paragraph and ask any related question. The AI will find the most relevant answer.

    ## 📌 Instructions for Users:
    - Provide a **Context** (short paragraph or passage).
    - Enter a **Question** based on that context.
    - The AI will return the most accurate answer found **within the context**.
    
    ⚠️ **Note**:
    - The question **must be closely related** to the context.
    - **Irrelevant or slightly off-topic questions** may return **inaccurate or biased results**.
    """)

    with gr.Row():
        context_input = gr.Textbox(
            label="📘 Context (Paragraph)",
            lines=8,
            placeholder="e.g., The Amazon River flows through Brazil, Peru, and Colombia. It is the second-longest river in the world..."
        )

    question_input = gr.Textbox(
        label="❓ Your Question",
        placeholder="e.g., Which countries does the Amazon River flow through?"
    )
    answer_output = gr.Textbox(label="✅ AI Answer")

    submit_button = gr.Button("Get Answer")
    submit_button.click(fn=answer_question, inputs=[context_input, question_input], outputs=answer_output)

demo.launch()