Create app.py

app.py

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+# imports
+
+from icevision.all import *
+
+model_2 = models.torchvision.retinanet.model(
+    backbone=models.torchvision.retinanet.backbones.resnext50_32x4d_fpn (pretrained=True),
+    num_classes=len(class_map)
+)
+
+# load from model_repo:
+
+from huggingface_hub import hf_hub_download
+hf_hub_download(repo_id="Alesteba/deep_model_02", filename="retinanet_racoon.pth")
+
+state_dict = torch.load('retinanet_racoon.pth')
+
+model_2.load_state_dict(state_dict)
+
+# use test img:
+
+img = PIL.Image.open('test.jpg')
+
+infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
+
+pred_dict_2  = models.torchvision.retinanet.fastai.end2end_detect(
+    
+    img, 
+    infer_tfms,
+    model_2.to("cpu"), 
+    class_map=class_map, 
+    detection_threshold=0.5
+)
+
+# modelo 2 -> construido:
+
+res_img = pred_dict_2['img']
+