Update app.py

app.py

@@ -3,12 +3,8 @@ import tensorflow as tf
 import numpy as np
 from tensorflow.keras.preprocessing import image
 
-# Laden der Modelle
-model_cnn = tf.keras.models.load_model('exercise_classification_model.h5')
-model_resnet50 = tf.keras.models.load_model('exercise_classification_model_resnet50.h5')
-
 # Klassenlabels (ersetzen Sie durch Ihre spezifischen Klassen)
-class_labels = ['bench_press', 'deadlift', 'hip_thrust', 'lat_pulldown', 'pull_up', 'squat', 'tricep_dips']
+class_labels = ['Bench Press', 'deadlift', 'hip_thrust', 'lat_pulldown', 'pull_up', 'squat', 'tricep_dips']
 
 def preprocess(img):
     img = img.resize((150, 150))
@@ -17,6 +13,19 @@ def preprocess(img):
     img_array /= 255.0
     return img_array
 
+# Laden der Modelle
+try:
+    model_cnn = tf.keras.models.load_model('exercise_classification_model.h5')
+    print("CNN model loaded successfully")
+except Exception as e:
+    print(f"Error loading CNN model: {e}")
+
+try:
+    model_resnet50 = tf.keras.models.load_model('exercise_classification_model_resnet50.h5')
+    print("ResNet50 model loaded successfully")
+except Exception as e:
+    print(f"Error loading ResNet50 model: {e}")
+
 def predict_cnn(img):
     img_array = preprocess(img)
     prediction = model_cnn.predict(img_array)