| import gradio as gr | |
| from transformers import pipeline | |
| # Load a question-answering model instead of a text generator | |
| qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2") | |
| def get_answer(question): | |
| # Define a factual context | |
| context = """ | |
| Washington, D.C. is the capital of the United States of America. | |
| """ | |
| answer = qa_pipeline(question=question, context=context) | |
| return answer["answer"] | |
| # Create Gradio Interface | |
| iface = gr.Interface( | |
| fn=get_answer, | |
| inputs="text", | |
| outputs="text", | |
| title="Ask Any Question", | |
| description="Ask factual questions and get precise answers." | |
| ) | |
| iface.launch() | |