Update app.py

app.py

@@ -6,16 +6,16 @@ from PIL import Image
 # Load model
 model = tf.keras.models.load_model("model.h5")
 
-# Define class names — replace with your actual folder names if different
-class_names = ['batteries', 'cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
+# Correct class names in training order
+class_names = ['batteries', 'clothes', 'e-waste', 'glass', 'light blubs', 'metal', 'organic', 'paper', 'plastic']
 
-# Define image size used in training
+# Match training input size
 IMG_SIZE = (224, 224)
 
 def preprocess(image):
     image = image.resize(IMG_SIZE)
     image_array = tf.keras.preprocessing.image.img_to_array(image)
-    image_array = image_array / 255.0  # Normalize if you did this during training
+    image_array = image_array / 255.0  # Normalize if this was done in training
     return np.expand_dims(image_array, axis=0)
 
 def predict(image):