| import torch | |
| import gradio as gr | |
| from transformers import pipeline | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| def predict(image): | |
| classifier = pipeline(task="image-classification") | |
| preds = classifier(image) | |
| preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds] | |
| return preds | |
| description = """ | |
| """ | |
| gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.components.Image(label="Image to classify", type="pil"), | |
| ], | |
| outputs="label", | |
| title="Comparateur d'image", | |
| description=description | |
| ).launch() | |