Update app.py

app.py

@@ -8,6 +8,14 @@ model_path = 'model__xception.h5'
 # Load the pre-trained model
 model = tf.keras.models.load_model(model_path)
 
+#Defining the labels
+labels = ['Aloevera','Amla','Amruthaballi','Arali','Astma_weed','Badipala','Balloon_Vine','Bamboo','Beans','Betel','Bhrami','Bringaraja','Caricature','Castor','Catharanthus',
+          'Chakte','Chilly','Citron lime (herelikai)','Coffee','Common rue(naagdalli)','Coriender','Curry','Doddpathre','Drumstick','Ekka','Eucalyptus','Ganigale','Ganike',
+          'Gasagase','Ginger','Globe Amarnath','Guava','Henna','Hibiscus','Honge','Insulin','Jackfruit','Jasmine','Kambajala','Kasambruga','Kohlrabi','Lantana','Lemon',
+          'Lemongrass','Malabar_Nut','Malabar_Spinach','Mango','Marigold','Mint','Neem','Nelavembu','Nerale','Nooni','Onion','Padri','Palak(Spinach)','Papaya','Parijatha',
+          'Pea','Pepper','Pomoegranate','Pumpkin','Raddish','Rose','Sampige','Sapota','Seethaashoka','Seethapala','Spinach1','Tamarind','Taro','Tecoma','Thumbe','Tomato',
+          'Tulsi','Turmeric','ashoka','camphor','kamakasturi','kepala']
+
 ## Define the predict function for Gradio
 def predict_gradio(image):
     # Preprocess the input image