Update app.py

app.py

@@ -13,10 +13,10 @@ model.load_state_dict(state_dict)
 # Definimos una función que se encarga de llevar a cabo las predicciones
 def predict(img):
     #img = PILImage.create(img)
-    infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
+    infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(384),tfms.A.Normalize()])
     pred_dict  = models.torchvision.faster_rcnn.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=class_map, 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)), examples=['00001.jpg','00002.jpg']).launch(share=False)
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(256, 256)), examples=['00001.jpg','00002.jpg']).launch(share=False)