commit

app.py

@@ -1,60 +1,4 @@
-# import gradio as gr
-# import torch
-# import yolov5
 
-# # 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://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg')
-
-# def yolov5_inference(
-#     image: gr.inputs.Image = None,
-#     model_path: gr.inputs.Dropdown = None,
-#     image_size: gr.inputs.Slider = 640,
-#     conf_threshold: gr.inputs.Slider = 0.25,
-#     iou_threshold: gr.inputs.Slider = 0.45,
-# ):
-#     """
-#     YOLOv5 inference function
-#     Args:
-#         image: Input image
-#         model_path: Path to the model
-#         image_size: Image size
-#         conf_threshold: Confidence threshold
-#         iou_threshold: IOU threshold
-#     Returns:
-#         Rendered image
-#     """
-#     model = yolov5.load(model_path, device="cpu")
-#     model.conf = conf_threshold
-#     model.iou = iou_threshold
-#     results = model([image], size=image_size)
-#     return results.render()[0]
-        
-
-# inputs = [
-#     gr.inputs.Image(type="pil", label="Input Image"),
-#     gr.inputs.Dropdown(["yolov5s.pt", "yolov5l.pt", "yolov5x.pt"], label="Model"),
-#     gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-#     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-#     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-# ]
-
-# outputs = gr.outputs.Image(type="filepath", label="Output Image")
-# title = "YOLOv5"
-# description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model."
-
-# examples = [['zidane.jpg', 'yolov5s.pt', 640, 0.25, 0.45], ['image3.jpg', 'yolov5s.pt', 640, 0.25, 0.45]]
-# demo_app = gr.Interface(
-#     fn=yolov5_inference,
-#     inputs=inputs,
-#     outputs=outputs,
-#     title=title,
-#     examples=examples,
-#     cache_examples=True,
-#     live=True,
-#     theme='huggingface',
-# )
-# demo_app.launch(debug=True, enable_queue=True)
 import gradio as gr
 import torch
 import yolov5