Create app.py

app.py

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+# ================================
+# 0. PATCH pour huggingface_hub (contourne l'absence de HfFolder)
+# ================================
+import huggingface_hub
+if not hasattr(huggingface_hub, 'HfFolder'):
+    class HfFolder:
+        _token = None
+        @staticmethod
+        def get_token():
+            return HfFolder._token
+        @staticmethod
+        def save_token(token):
+            HfFolder._token = token
+    huggingface_hub.HfFolder = HfFolder
+
+# ================================
+# 1. PATCH pour contourner le bug de Gradio 4.44.0
+#    (TypeError: argument of type 'bool' is not iterable)
+# ================================
+import gradio_client.utils
+
+original_get_type = gradio_client.utils.get_type
+
+def patched_get_type(schema):
+    if isinstance(schema, bool):
+        return "boolean"
+    return original_get_type(schema)
+
+gradio_client.utils.get_type = patched_get_type
+
+# ================================
+# 2. IMPORTS STANDARDS
+# ================================
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# ================================
+# 3. CHARGEMENT DU MODÈLE (72 classes)
+# ================================
+MODEL_PATH = "final_model.keras"
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# ================================
+# 4. NOMS DES CLASSES (issus de l'entraînement)
+# ================================
+class_names = [
+    "Apple___alternaria_leaf_spot",
+    "Apple___black_rot",
+    "Apple___brown_spot",
+    "Apple___gray_spot",
+    "Apple___healthy",
+    "Apple___rust",
+    "Apple___scab",
+    "Bell_pepper___bacterial_spot",
+    "Bell_pepper___healthy",
+    "Blueberry___healthy",
+    "Cassava___bacterial_blight",
+    "Cassava___brown_streak_disease",
+    "Cassava___green_mottle",
+    "Cassava___healthy",
+    "Cassava___mosaic_disease",
+    "Cherry___healthy",
+    "Cherry___powdery_mildew",
+    "Coffee___healthy",
+    "Coffee___red_spider_mite",
+    "Coffee___rust",
+    "Corn___common_rust",
+    "Corn___gray_leaf_spot",
+    "Corn___healthy",
+    "Corn___northern_leaf_blight",
+    "Grape___Leaf_blight",
+    "Grape___black_measles",
+    "Grape___black_rot",
+    "Grape___healthy",
+    "Orange___citrus_greening",
+    "Peach___bacterial_spot",
+    "Peach___healthy",
+    "Potato___bacterial_wilt",
+    "Potato___early_blight",
+    "Potato___healthy",
+    "Potato___late_blight",
+    "Potato___leafroll_virus",
+    "Potato___mosaic_virus",
+    "Potato___nematode",
+    "Potato___pests",
+    "Potato___phytophthora",
+    "Raspberry___healthy",
+    "Rice___bacterial_blight",
+    "Rice___blast",
+    "Rice___brown_spot",
+    "Rice___tungro",
+    "Rose___healthy",
+    "Rose___rust",
+    "Rose___slug_sawfly",
+    "Soybean___healthy",
+    "Squash___powdery_mildew",
+    "Strawberry___healthy",
+    "Strawberry___leaf_scorch",
+    "Sugercane___healthy",
+    "Sugercane___mosaic",
+    "Sugercane___red_rot",
+    "Sugercane___rust",
+    "Sugercane___yellow_leaf",
+    "Tomato___bacterial_spot",
+    "Tomato___early_blight",
+    "Tomato___healthy",
+    "Tomato___late_blight",
+    "Tomato___leaf_curl",
+    "Tomato___leaf_mold",
+    "Tomato___mosaic_virus",
+    "Tomato___septoria_leaf_spot",
+    "Tomato___spider_mites",
+    "Tomato___target_spot",
+    "Watermelon___anthracnose",
+    "Watermelon___downy_mildew",
+    "Watermelon___healthy",
+    "Watermelon___mosa"
+]
+
+# ================================
+# 5. FONCTION DE PRÉDICTION
+# ================================
+def preprocess_image(img):
+    """Redimensionne et normalise l'image pour le modèle EfficientNet."""
+    img = img.resize((224, 224))
+    img_array = np.array(img)
+    img_array = tf.keras.applications.efficientnet.preprocess_input(img_array)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+def predict(img):
+    """
+    img : image PIL fournie par gr.Image(type="pil")
+    Retourne un dictionnaire {classe: probabilité} pour le composant gr.Label
+    """
+    processed = preprocess_image(img)
+    preds = model.predict(processed, verbose=0)[0]
+    # Créer le dictionnaire des probabilités
+    results = {class_names[i]: float(preds[i]) for i in range(len(class_names))}
+    return results
+
+# ================================
+# 6. INTERFACE GRADIO
+# ================================
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Chargez une image de feuille"),
+    outputs=gr.Label(num_top_classes=3, label="Maladie prédite (top 3)"),
+    title="Classification des maladies des plantes (72 classes)",
+    description="Chargez une photo de feuille et le modèle prédira la maladie parmi 72 classes. Modèle basé sur EfficientNetB0 avec fine-tuning.",
+    examples=None,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)