Update app.py

app.py

@@ -19,7 +19,7 @@ def predict_image(image):
     image.save(image_bytes, format="JPEG")
 
     # Load the image from the file-like object
-    image = tf.keras.preprocessing.image.load_img(image_bytes, target_size=(256, 256))
+    image = tf.keras.preprocessing.image.load_img(image_bytes, target_size=(256, 256,3))
     image = np.array(image)/255
     image = np.expand_dims(image, axis=0)
 
@@ -29,12 +29,12 @@ def predict_image(image):
     # Get the probability of being 'Clean' or 'Carries'
     probabilities = tf.nn.softmax(prediction, axis=-1)
     predicted_class_index = np.argmax(probabilities)
-    if predicted_class_index == 0:
+    if predicted_class_index == 1:
         predicted_label = "Clean"
-        #predicted_probability = probabilities[0][0] * 100  # Convert to percentage
-    else:
+        predicted_probability = probabilities[0][1] * 100  # Convert to percentage
+    elif predicted_class_index == 0:
         predicted_label = "Carries"
-        #predicted_probability = probabilities[0][1] * 100  # Convert to percentage
+        predicted_probability = probabilities[0][0] * 100  # Convert to percentage
 
     # Return the prediction result as a dictionary
     return {"Predicted Label": predicted_label}