Update app.py

app.py

@@ -2,17 +2,22 @@ from huggingface_hub import from_pretrained_fastai
 import gradio as gr
 from fastai.vision.all import *
 from icevision.all import *
+from icevision.models.checkpoint import *
 import PIL
 
-repo_id = "paascorb/image-detection-efficientdet"
+checkpoint_path = "efficientdetMapaches.pth"
+checkpoint_and_model = model_from_checkpoint(checkpoint_path)
+model = checkpoint_and_model["model"]
+model_type = checkpoint_and_model["model_type"]
+class_map = checkpoint_and_model["class_map"]
 
-learner = from_pretrained_fastai(repo_id)
+img_size = checkpoint_and_model["img_size"]
+valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
 
 # Definimos una función que se encarga de llevar a cabo las predicciones
 def predict(img):
     img = PIL.Image.open(img)
-    infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
-    pred_dict  = models.ross.efficientdet.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=class_map, detection_threshold=0.5)
+    pred_dict  = model_type(img, valid_tfms, model.to("cpu"), class_map=class_map, detection_threshold=0.5)
     return pred_dict["img"]
     
 # Creamos la interfaz y la lanzamos.