| import gradio as gr | |
| from transformers import pipeline | |
| # Load a general text generation model | |
| generator = pipeline("text-generation", model="bigscience/bloom-560m") | |
| def answer_med_question(question): | |
| # Generate answer | |
| result = generator(question, max_length=200, do_sample=True) | |
| return result[0]['generated_text'] | |
| demo = gr.Interface( | |
| fn=answer_med_question, | |
| inputs=gr.Textbox(label="Ask your medical question"), | |
| outputs=gr.Textbox(label="AI Answer") | |
| ) | |
| demo.launch() |