Update app.py

app.py

@@ -1,47 +1,19 @@
-from gradio.outputs import Label
 from icevision.all import *
-from icevision.models.checkpoint import *
 import PIL
 import gradio as gr
-import os
 
-# Load model
-checkpoint_path = "fasterRCNNKangaroo.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"]
+class_map = ClassMap(['kangaroo'])
+model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet50_fpn,
+                                             num_classes=len(class_map))
+state_dict = torch.load('fasterRCNNKangaroo.pth')
+model.load_state_dict(state_dict)
 
-# Transforms
-img_size = checkpoint_and_model["img_size"]
-valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
+infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()])
+size = 384
+def predict(img):
+    img = PILImage.create(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']
 
-# Populate examples in Gradio interface
-examples = [
-    ['00004.jpg'],
-    ['00083.jpg'],
-    ['00119.jpg']
-]
-
-def show_preds(input_image):
-    img = PIL.Image.fromarray(input_image, "RGB")
-    pred_dict = model_type.end2end_detect(img, valid_tfms, model, 
-                                          class_map=class_map, 
-                                          detection_threshold=0.5,
-                                          display_label=False, 
-                                          display_bbox=True, 
-                                          return_img=True, 
-                                          font_size=16, 
-                                          label_color="#FF59D6")
-    return pred_dict["img"]
-
-gr_interface = gr.Interface(
-    fn=show_preds,
-    inputs=["image"],
-    outputs=[gr.outputs.Image(type="pil", label="FasterRCNN Inference")],
-    title="Kangaroo Object Detector",
-    description="",
-    examples=examples,
-)
-gr_interface.launch(inline=False, share=False, debug=True)
- 
+# 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=['00004.jpg','00083.jpg', '00119.jpg']).launch(share=False)