Update app.py

app.py

@@ -2,20 +2,19 @@ from huggingface_hub import from_pretrained_fastai
 import gradio as gr
 from fastai.vision.all import *
 
-
-
-# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
-repo_id = "luisvarona/deteccion-canguros"
-
-learner = from_pretrained_fastai(repo_id)
-labels = learner.dls.vocab
+model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet50_fpn,
+                                             num_classes=len(class_map))
+state_dict = torch.load('fasterRCNNCanguros/fasterRCNNCanguros.pth')
+model.load_state_dict(state_dict)
+class_map = ClassMap(['kangaroo'])
 
 # Definimos una función que se encarga de llevar a cabo las predicciones
 def predict(img):
-    #img = PILImage.create(img)
-    pred,pred_idx,probs = learner.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    img = PILImage.create(img)
+    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, 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=['buildings.jpg','forest.jpg']).launch(share=False)
+gr.Interface(fn=predict, inputs=image, outputs=image ,examples=['00011_(1).jpg','00014_(1).jpg']).launch(share=False)