Update app.py

app.py

@@ -2,28 +2,40 @@ import gradio as gr
 import numpy as np
 from tensorflow.keras.models import load_model
 from PIL import Image
+import requests
+import os
 
-# ✅ 1. Carica il modello (dal repo Hugging Face di liriope)
-model = load_model("https://huggingface.co/liriope/PlantDiseaseDetection/resolve/main/model.h5")
+MODEL_URL = "https://huggingface.co/liriope/PlantDiseaseDetection/resolve/main/model.h5"
+MODEL_PATH = "model.h5"
 
-# ✅ 2. Preprocessing + predizione
+# ✅ 1. Scarica il modello se non esiste ancora localmente
+if not os.path.exists(MODEL_PATH):
+    print("📥 Downloading model from Hugging Face...")
+    response = requests.get(MODEL_URL)
+    with open(MODEL_PATH, "wb") as f:
+        f.write(response.content)
+    print("✅ Model downloaded and saved locally!")
+
+# ✅ 2. Carica il modello
+model = load_model(MODEL_PATH)
+print("✅ Model loaded successfully!")
+
+# ✅ 3. Funzione di predizione
 def predict(image):
     img = image.convert("RGB").resize((224, 224))
     arr = np.expand_dims(np.array(img) / 255.0, axis=0)
     preds = model.predict(arr)[0]
     class_idx = np.argmax(preds)
     confidence = float(preds[class_idx])
-
     return {f"Predicted class: {class_idx}": confidence}
 
-# ✅ 3. Interfaccia Gradio
+# ✅ 4. Interfaccia Gradio
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(label="Upload a leaf photo"),
     outputs=gr.Label(num_top_classes=3),
     title="🌿 Plant Health Checker AI",
-    description="Upload a photo of a leaf and detect potential plant diseases using a deep learning model."
+    description="Upload a photo of a leaf and detect potential plant diseases using a deep learning model.",
 )
 
-# ✅ 4. Avvio Space
 demo.launch()