Update app.py

app.py

@@ -40,6 +40,7 @@ custom_title ="<span style='color: #66814a;'>Green Greta</span>"
 
 
 from huggingface_hub import from_pretrained_keras
+from tensorflow.keras.applications import EfficientNetB0
 
 import tensorflow as tf
 from tensorflow import keras
@@ -53,17 +54,15 @@ class_labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
 
 # Function to predict image label and score
 def predict_image(input):
-    # Resize the image to the size expected by the model
-    image = input.resize((244, 224))
-    # Convert the image to a NumPy array
-    image_array = tf.keras.preprocessing.image.img_to_array(image)
+    # Resize the image to the size expected by the model and convert to numpy array
+    image_array = tf.keras.preprocessing.image.img_to_array(input.resize((224, 244)))
     # Normalize the image
-    image_array /= 255.0
+    image_array = tf.keras.applications.efficientnet.preprocess_input(image_array)
     # Expand the dimensions to create a batch
     image_array = tf.expand_dims(image_array, 0)
     # Predict using the model
-    predictions = model1.predict(image_array)
-    
+    predictions = model_tl.predict(image_array)
+    class_labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
     category_scores = {}
     for i, class_label in enumerate(class_labels):
         category_scores[class_label] = predictions[0][i].item()