import gradio as gr
from transformers import pipeline

def qa_interface(context, question):
    model_checkpoint = "AirrStorm/BERT-SQUAD-QA-Finetuned"
    question_answerer = pipeline("question-answering", model=model_checkpoint)
    answer = question_answerer(question=question, context=context)
    return answer['answer']

# Define inputs
inputs = [
    gr.Textbox(
        lines=10, 
        label="Context", 
        placeholder="Enter the context where the answer can be found...",
    ),
    gr.Textbox(
        label="Question", 
        placeholder="Enter a specific question based on the provided context..."
    )
]

# Define example inputs
examples = [
    [
        "The Eiffel Tower is one of the most famous landmarks in Paris, France. It was constructed in 1889 and stands approximately 330 meters tall.",
        "When was the Eiffel Tower constructed?"
    ],
    [
        "Python is a versatile programming language known for its simplicity and readability. It is widely used for web development, data analysis, artificial intelligence, and more.",
        "What is Python known for?"
    ],
    [
        "The Great Wall of China stretches over 13,000 miles and was built to protect Chinese states from invasions. It is considered one of the greatest architectural achievements in history.",
        "How long is the Great Wall of China?"
    ],
]

# Create the Gradio interface
interface = gr.Interface(
    fn=qa_interface,
    inputs=inputs,
    outputs=gr.Textbox(
        label="Answer",
        placeholder="The model's answer will appear here."
    ),
    title="Question Answering (QA) Tool",
    description=(
        "This tool uses a Question Answering (QA) model to find and return the most relevant answer "
        "to a specific question based on the provided context.\n\n"
        "Provide a context paragraph and a related question to get started!"
    ),
    examples=examples,
    theme="hugging-face",  # Optional theme for a polished look
)

interface.launch()