Update app.py

gradio_app.py → app.py

@@ -1,56 +1,50 @@
-import gradio as gr
-from tensorflow.keras.models import load_model  # type: ignore
-from PIL import Image, ImageOps  # Install pillow instead of PIL
-import numpy as np
-from tensorflow.keras.layers import DepthwiseConv2D  # type: ignore
-
-# Définir la couche sans le paramètre 'groups'
-def custom_depthwise_conv2d(*args, **kwargs):
-    if 'groups' in kwargs:
-        del kwargs['groups']  # Retirer 'groups'
-    return DepthwiseConv2D(*args, **kwargs)
-
-# Charger le modèle
-model = load_model("models/keras_model.h5", custom_objects={'DepthwiseConv2D': custom_depthwise_conv2d}, compile=False)
-
-# Charger les étiquettes
-with open("labels.txt", "r") as file:
-    class_names = file.readlines()
-
-# Fonction pour prédire la classe d'une image
-def predict(image):
-    # Redimensionner l'image à 224x224
-    image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)
-    
-    # Convertir l'image en tableau numpy
-    image_array = np.asarray(image)
-    
-    # Normaliser l'image
-    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
-    
-    # Créer le tableau de données pour le modèle
-    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
-    data[0] = normalized_image_array
-
-    # Prédire avec le modèle
-    prediction = model.predict(data)
-    index = np.argmax(prediction)
-    class_name = class_names[index].strip()  # Supprimer les espaces supplémentaires
-    confidence_score = prediction[0][index]
-    
-    return class_name, confidence_score
-
-# Créer l'interface Gradio
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Téléchargez une image"),
-    outputs=[
-        gr.Label(label="Classe Prédite"),
-        gr.Number(label="Score de Confiance")
-    ],
-    title="Medical Assistant",
-    description="Téléchargez une image dentaire et notre application prédira sa classe."
-)
-
-# Lancer l'interface
+import gradio as gr
+from tensorflow.keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
+from tensorflow.keras.layers import DepthwiseConv2D
+
+def custom_depthwise_conv2d(*args, **kwargs):
+    if 'groups' in kwargs:
+        del kwargs['groups']  # Retirer 'groups'
+    return DepthwiseConv2D(*args, **kwargs)
+
+# Load model
+model = load_model("keras_model.h5", custom_objects={'DepthwiseConv2D': custom_depthwise_conv2d}, compile=False)
+
+# Load labels
+with open("labels.txt", "r") as file:
+    class_names = file.readlines()
+
+# Create predict fonction
+def predict(image):
+    image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)
+    
+    image_array = np.asarray(image)
+    
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+    
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    data[0] = normalized_image_array
+
+    # Make prediction
+    prediction = model.predict(data)
+    index = np.argmax(prediction)
+    class_name = class_names[index].strip() 
+    confidence_score = prediction[0][index]
+    
+    return class_name, confidence_score
+
+# Créer l'interface Gradio
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Download image"),
+    outputs=[
+        gr.Label(label="Class predict"),
+        gr.Number(label="Confidence Score")
+    ],
+    title="Medical Assistant",
+    description="Upload a dental image and our app will predict its class."
+)
+
 iface.launch(api_show=False)