Delete app.py

app.py

@@ -1,48 +0,0 @@
-import os
-from PIL import Image
-import torch
-from torchvision import transforms
-import gradio as gr
-
-
-# load model
-model = torch.hub.load('datvuthanh/hybridnets', 'hybridnets', pretrained=True)
-
-
-normalize = transforms.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-    )
-
-transform=transforms.Compose([
-            transforms.ToTensor(),
-            # normalize
-        ])
-
-
-def inference(img):
-
-    # print(img.size)
-    img = img.resize((640, 384))
-
-    img = torch.unsqueeze(transform(img), dim=0)
-
-    # img = transform(img)
-
-    features, regression, classification, anchors, segmentation = model(img)
-    
-    features_out = features[0][0, :, :].detach().numpy()
-    regression_out = regression[0][0, :, :].detach().numpy()
-    classification_out = classification[0][0, :, :].detach().numpy()
-    anchors_out = anchors[0][0, :, :].detach().numpy()
-    segmentation_out = segmentation[0][0, :, :].detach().numpy()
-    
-    return features_out, regression_out, classification_out, anchors_out, segmentation_out
-    
-title="HybridNets Demo"
-description="Gradio demo for HybridNets: End2End Perception Network pretrained on BDD100k Dataset. To use it, simply upload your image or click on one of the examples to load them. Read more at the links below"
-
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2203.09035' target='_blank'>ybridNets: End2End Perception Network</a> | <a href='https://github.com/datvuthanh/HybridNets' target='_blank'>Github Repo</a></p>"
-
-examples=[['frame_00_delay-0.13s.jpg']]
-
-gr.Interface(inference,gr.inputs.Image(type="pil"),[gr.outputs.Image(label='Features'),gr.outputs.Image(label='Regression'),gr.outputs.Image(label='Classification'),gr.outputs.Image(label='Anchors'),gr.outputs.Image(label='Segmentation ')],article=article,description=description,title=title,examples=examples).launch()