Update app.py

app.py

@@ -10,28 +10,22 @@ model = tf.keras.models.load_model('gym_equipment_transferlearning.keras')
 class_names = ['benchPress', 'dumbBell', 'kettleBell', 'treadMill']
 
 def classify_image(image):
-    # Convert the input image to a PIL image
     image = Image.fromarray(image.astype('uint8'), 'RGB')
-    # Resize the image to the input size expected by the model
     img = image.resize((150, 150))
-    # Convert the image to a numpy array and expand dimensions to create a batch
     img_array = tf.keras.preprocessing.image.img_to_array(img)
-    img_array = tf.expand_dims(img_array, 0)
-    # Make predictions
+    img_array = tf.expand_dims(img_array, 0)  # Erstelle einen Batch
     predictions = model.predict(img_array)
-    # Get the predicted class and confidence
     predicted_class = class_names[np.argmax(predictions[0])]
     confidence = np.max(predictions[0])
     return {predicted_class: float(confidence)}
 
-# Define the Gradio interface components
+
 image_input = gr.Image()  # Entferne den `shape` Parameter
 label = gr.Label(num_top_classes=3)
 
-# Create the Gradio interface
 iface = gr.Interface(
-    fn=classify_image,
-    inputs=image_input,
+    fn=classify_image, 
+    inputs=image_input, 
     outputs=label,
     title='Gym Equipment Classifier',
     description='Upload an image of gym equipment and the classifier will tell you which one it is and the confidence level of the prediction.'