Create app.py

app.py

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+import gradio as gr
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import torch
+from PIL import Image
+
+# Carica feature extractor e modello dal tuo Hub
+MODEL_ID = "jaqen79/retail_images_classification_v1"
+extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
+model = AutoModelForImageClassification.from_pretrained(MODEL_ID)
+
+def predict(image: Image.Image):
+    # Preprocess
+    inputs = extractor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    probs = outputs.logits.softmax(dim=-1).tolist()[0]
+    # Assumi che model.config.id2label esista
+    labels = [model.config.id2label[i] for i in range(len(probs))]
+    # Ritorna dizionario label→probabilità
+    return {labels[i]: float(probs[i]) for i in range(len(probs))}
+
+# Interfaccia Gradio
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.components.Image(type="pil"), # Changed to gr.components.Image
+    outputs=gr.components.Label(num_top_classes=5), # Changed to gr.components.Label
+    title="Vision Transformer Demo",
+    description="Carica un'immagine e il modello ritorna le classi con probabilità."
+)
+
+if __name__ == "__main__":
+    demo.launch()