| from transformers import pipeline | |
| import gradio as gr | |
| classifier = pipeline( | |
| "image-classification", | |
| model="nateraw/food" | |
| ) | |
| def predict(image): | |
| results = classifier(image) | |
| return {r["label"]: float(r["score"]) for r in results} | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Label(num_top_classes=3) | |
| ) | |
| demo.launch() |