Update app.py

app.py

@@ -33,13 +33,9 @@ def predict_image(image):
     outputs = model(img_array)
     predictions = tf.nn.softmax(outputs.logits, axis=-1)
     predicted_class = np.argmax(predictions)
-    if predicted_class == 0:
-        predict_label = "Clean"
-    else:
-        predict_label = "Carries"
     #confidence = float(np.max(predictions))
-    prediction_dict = {"prediction": predict_label}
-    return json.dumps(prediction_dict, indent=1)
+    return{"prediction": predicted_class}
+    #return json.dumps(prediction_dict, indent=1)
 
 # Create the interface
 input_interface = gr.Image(type = "pil")