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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() | |