Update app.py

app.py

@@ -1,22 +1,21 @@
 from huggingface_hub import from_pretrained_fastai
 import gradio as gr
-from fastai.vision.all import *
 from icevision.all import *
 
 # repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
 repo_id = "inigo99/kangaroo-detector"
 
 class_map = ClassMap(['kangaroo'])
-model = models.efficientdet.model(backbone=models.efficientdet.backbones.d1(pretrained=True),
-                                       num_classes=len(class_map), img_size=(384, 384))
-state_dict = torch.load('efficientdetD1kangaroo.pth')
+model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True),
+                                             num_classes=len(class_map))
+state_dict = torch.load("fasterRCNNkangaroo.pth")
 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(384),tfms.A.Normalize()])
-    pred_dict  = models.efficientdet.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=class_map, 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.