Create app.py

app.py

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+import gradio as gr
+from icevision.all import *
+import PIL
+class_map = ClassMap(['raccoon'])
+model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet50_fpn(pretrained=True), num_classes=len(class_map))
+state_dict = torch.load('modelResnet50raccoon.pth')
+model.load_state_dict(state_dict)
+size = 384
+infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
+def predict(img):
+  # img = PIL.Image.open(img)
+  np.int = int
+  img = PIL.Image.fromarray(img)
+
+  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=["image"], outputs=["image"], examples=['raccoon-106.jpg','raccoon-129.jpg']).launch(share=True,debug=True)