Update app.py

app.py

@@ -2,10 +2,10 @@ from fastai.vision.all import *
 from icevision.all import *
 import gradio as gr
 
+class_map=ClassMap(['kangaroo'])
 	
 # Cargamos el learner
-model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn,
-                                             num_classes=2)
+model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn,num_classes=len(class_map))
 state_dict = torch.load('fasterRCNNkangaroo.pth')
 model.load_state_dict(state_dict)
 	
@@ -15,8 +15,8 @@ model.load_state_dict(state_dict)
 infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(384),tfms.A.Normalize()])
 def predict(img):
 	img = PILImage.create(img)
-	pred_dict  = models.torchvision.faster_rcnn.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=ClassMap(['kangaroo']), detection_threshold=0.5)
+	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)), outputs=gr.outputs.Image(shape=(128, 128)),examples=['00004.jpg','00014.jpg']).launch(share=False)
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Image(),examples=['00004.jpg','00014.jpg']).launch(share=False)