Update app.py

app.py

@@ -6,49 +6,64 @@ from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.image import img_to_array
 from PIL import Image
 
-# Charger le modèle pré-entraîné
+# Load the pre-trained model
 model = load_model('plant_diseases.h5')
 
-# Classes de labels (remplacez par vos propres classes)
-class_labels = ['Piment: Bacterial_spot', 'Piment: healthy', 'Pomme de terre: Early_blight', 'Pomme de terre: Late_blight', 'Pomme de terre: Healthy', 'Tomate: Bacterial Spot', 'Tomate: Early Blight', 'Tomate: Late Blight', 'Tomate: Leaf mold', 'Tomate: Septoria leaf spot', 'Tomate: Siper mites', 'Tomate: Spot', "Tomate: Yellow Leaf Curl", 'Tomate: Virus Mosaïque', 'Tomate: Healthy']
-
+# Class labels (replace with your own classes)
+class_labels = [
+    'Piment: Bacterial_spot', 
+    'Piment: healthy', 
+    'Pomme de terre: Early_blight', 
+    'Pomme de terre: Late_blight', 
+    'Pomme de terre: Healthy', 
+    'Tomate: Bacterial Spot', 
+    'Tomate: Early Blight', 
+    'Tomate: Late Blight', 
+    'Tomate: Leaf mold', 
+    'Tomate: Septoria leaf spot', 
+    'Tomate: Spider mites', 
+    'Tomate: Spot', 
+    'Tomate: Yellow Leaf Curl', 
+    'Tomate: Virus Mosaïque', 
+    'Tomate: Healthy'
+]
 
 def preprocess_image(image, image_size=(224, 224)):
-    # Convertir l'image en niveaux de gris
+    # Convert image to grayscale
     image = np.array(image.convert('L'))
-    # Redimensionner l'image
+    # Resize image
     image = cv2.resize(image, image_size)
- 
-    # Redimensionner pour le modèle
+    # Prepare image for the model
     image = img_to_array(image)
     image /= 255.0
     image = np.expand_dims(image, axis=0)
     return image
 
+# Streamlit app setup
 st.title("Classification des Maladies des Plantes")
 st.write("Téléchargez une image de plante pour la classification")
 
 uploaded_file = st.file_uploader("Choisissez une image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Afficher l'image téléchargée
+    # Display the uploaded image
     image = Image.open(uploaded_file)
     st.image(image, caption='Image téléchargée', use_column_width=True)
-    
+
     st.write("Classification en cours...")
-    
-    # Prétraiter l'image
+
+    # Preprocess the image
     processed_image = preprocess_image(image)
-    
-    # Faire la prédiction
+
+    # Make predictions
     predictions = model.predict(processed_image)
     probabilities = predictions[0]
-    
-    # Afficher les probabilités de chaque classe
+
+    # Display probabilities for each class
     for i, label in enumerate(class_labels):
         if probabilities[i] > 0:
             st.write(f"{label}: {probabilities[i]:.2f}")
 
-    # Afficher le résultat de la classe prédite
+    # Show predicted class
     predicted_class = class_labels[np.argmax(probabilities)]
     st.write(f"Classe prédite: {predicted_class}")