Update app.py

app.py

@@ -7,13 +7,17 @@ from tensorflow.keras.preprocessing import image
 class_labels = ['bench_press', 'deadlift', 'hip_thrust', 'lat_pulldown', 'pull_up', 'squat', 'tricep_dips']
 
 def preprocess(img):
-    if img is None:
-        raise ValueError("No image uploaded")
-    img = img.resize((150, 150))
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array /= 255.0
-    return img_array
+    try:
+        if img is None:
+            raise ValueError("No image uploaded")
+        img = img.resize((150, 150))
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array /= 255.0
+        return img_array
+    except Exception as e:
+        print(f"Error in preprocessing: {e}")
+        return None
 
 # Laden der Modelle
 try:
@@ -31,20 +35,24 @@ except Exception as e:
 def predict_cnn(img):
     try:
         img_array = preprocess(img)
+        if img_array is None:
+            return {label: 0.0 for label in class_labels}
         prediction = model_cnn.predict(img_array)
         return {class_labels[i]: float(prediction[0][i]) for i in range(len(class_labels))}
     except Exception as e:
         print(f"Error in CNN prediction: {e}")
-        return {label: 0.0 for label in class_labels}  # Standardkonfidenz 0.0 für alle Klassen
+        return {label: 0.0 for label in class_labels}
 
 def predict_resnet50(img):
     try:
         img_array = preprocess(img)
+        if img_array is None:
+            return {label: 0.0 for label in class_labels}
         prediction = model_resnet50.predict(img_array)
         return {class_labels[i]: float(prediction[0][i]) for i in range(len(class_labels))}
     except Exception as e:
         print(f"Error in ResNet50 prediction: {e}")
-        return {label: 0.0 for label in class_labels}  # Standardkonfidenz 0.0 für alle Klassen
+        return {label: 0.0 for label in class_labels}
 
 # Gradio Interface für das CNN Modell
 interface_cnn = gr.Interface(