Update app.py

app.py

@@ -11,9 +11,10 @@ labels = learn.dls.vocab
 
 # Definimos una función que se encarga de llevar a cabo las predicciones
 def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    img = PIL.Image.open('kangarooc.jpg')
+    infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
+    pred_dict  = models.torchvision.faster_rcnn.end2end_detect(img, infer_tfms, learn.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)), outputs=gr.outputs.Label(num_top_classes=3),examples=['1002_5866_6582.jpg','1038_31199_2068.jpg']).launch(share=False)
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['kangarooc.jpg']).launch(share=False)