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| import gradio as gr | |
| from huggingface_hub import HfApi, hf_hub_download | |
| from transformers import pipeline | |
| def get_model_ids(): | |
| api = HfApi() | |
| models = api.list_models(filter="llama-leaderboard") | |
| model_ids = [x.modelId for x in models] | |
| return model_ids | |
| models = {} | |
| for model_id in get_model_ids(): | |
| models[model_id] = pipeline("image-classification", model=model_id) | |
| def predict(img, model_id): | |
| preds = models[model_id](img) | |
| res = {} | |
| for pred in preds: | |
| res[pred["label"]] = pred["score"] | |
| return res | |
| gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.inputs.Image(type="pil"), | |
| gr.inputs.Dropdown(get_model_ids()), | |
| ], | |
| outputs=gr.outputs.Label(num_top_classes=3), | |
| examples=[["llama.jpg", "osanseviero/llama-or-potato"], | |
| ["potato.jpg", "osanseviero/llama-or-potato"], | |
| ["horse.jpg", "osanseviero/llama-horse-zebra"]] | |
| ).launch() |