Delete app.py

app.py

@@ -1,101 +0,0 @@
-
-import gradio as gr
-import sahi
-import torch
-from ultralyticsplus import YOLO, render_model_output
-
-# Images
-sahi.utils.file.download_from_url(
-    "https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg",
-    "highway.jpg",
-)
-sahi.utils.file.download_from_url(
-    "https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg",
-    "small-vehicles1.jpeg",
-)
-sahi.utils.file.download_from_url(
-    "https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/zidane.jpg",
-    "zidane.jpg",
-)
-
-
-model_names = [
-    "yolov8n-seg.pt",
-    "yolov8s-seg.pt",
-    "yolov8m-seg.pt",
-    "yolov8l-seg.pt",
-    "yolov8x-seg.pt",
-]
-
-current_model_name = "yolov8m-seg.pt"
-model = YOLO(current_model_name)
-
-
-def yolov8_inference(
-    image: gr.inputs.Image = None,
-    model_name: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 640,
-    conf_threshold: gr.inputs.Slider = 0.25,
-    iou_threshold: gr.inputs.Slider = 0.45,
-):
-    """
-    YOLOv8 inference function
-    Args:
-        image: Input image
-        model_name: Name of the model
-        image_size: Image size
-        conf_threshold: Confidence threshold
-        iou_threshold: IOU threshold
-    Returns:
-        Rendered image
-    """
-    global model
-    global current_model_name
-    if model_name != current_model_name:
-        model = YOLO(model_name)
-        current_model_name = model_name
-    model.overrides["conf"] = conf_threshold
-    model.overrides["iou"] = iou_threshold
-    results = model.predict(image, imgsz=image_size, return_outputs=True)
-    renders = []
-    for image_results in model.predict(image, imgsz=image_size, return_outputs=True):
-        render = render_model_output(
-            model=model, image=image, model_output=image_results
-        )
-        renders.append(render)
-
-    return renders[0]
-
-
-inputs = [
-    gr.Image(type="filepath", label="Input Image"),
-    gr.Dropdown(
-        model_names,
-        value=current_model_name,
-        label="Model type",
-    ),
-    gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
-    gr.Slider(
-        minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"
-    ),
-    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
-]
-
-outputs = gr.Image(type="filepath", label="Output Image")
-title = "Ultralytics YOLOv8 Segmentation Demo"
-
-examples = [
-    ["zidane.jpg", "yolov8m-seg.pt", 640, 0.6, 0.45],
-    ["highway.jpg", "yolov8m-seg.pt", 640, 0.25, 0.45],
-    ["small-vehicles1.jpeg", "yolov8m-seg.pt", 640, 0.25, 0.45],
-]
-demo_app = gr.Interface(
-    fn=yolov8_inference,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    examples=examples,
-    cache_examples=True,
-    theme="default",
-)
-demo_app.launch(debug=True, enable_queue=True)