Delete app.py

app.py

@@ -1,31 +0,0 @@
-import gradio as gr
-import torch
-from PIL import Image
-import os 
-# Images
-torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
-torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')
-
-# Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # force_reload=True to update
-
-def yolo(im, size=640):
-    g = (size / max(im.size))  # gain
-    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
-
-    results = model(im)  # inference
-    results.render()  # updates results.imgs with boxes and labels
-    results.save()
-    os.system("ls")
-    return "out.png"
-
-
-inputs = gr.inputs.Image(type='pil', label="Original Image")
-outputs = gr.outputs.Image(type="file", label="Output Image")
-
-title = "YOLOv5"
-description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use."
-article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
-
-examples = [['zidane.jpg'], ['bus.jpg']]
-gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(cache_examples=True,enable_queue=True)