Update app.py

app.py

@@ -19,9 +19,9 @@ infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(384),tfms.A.Normalize()])
 # Definimos una función que se encarga de llevar a cabo las predicciones
 def predict(img):
     img = PIL.Image.open(img)
-    pred_dict  = model_type(img, infer_tfms, model.to("cpu"), class_map=ClassMap(['raccoon']), detection_threshold=0.5)
+    pred_dict = model_type(img, infer_tfms, model.to("cpu"), class_map=ClassMap(['raccoon']), detection_threshold=0.5)
     return pred_dict["img"]
     
 # Creamos la interfaz y la lanzamos. 
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(shape(128,128)),
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=[gr.outputs.Image(type="pil", label="VFNet Inference")],
              examples=['raccoon-161.jpg','raccoon-162.jpg']).launch(share=False)