Create app.py

app.py

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+import gradio as gr
+import cv2
+import requests
+import os 
+
+from ultralyticsplus import YOLO, render_result
+
+image_path = [['test_images/2a998cfb0901db5f8210.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/2ce19ce0191acb44920b.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
+             ['test_images/2daab6ea3310e14eb801.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/4a137deefb14294a7005 (1).jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
+             ['test_images/7e77c596436c9132c87d.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/170f914014bac6e49fab.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
+             ['test_images/3355ec3269c8bb96e2d9.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/546306a88052520c0b43.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
+             ['test_images/33148464019ed3c08a8f.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/a17a992a1cd0ce8e97c1.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
+             ['test_images/b5db5e42d8b80ae653a9 (1).jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/b8ee1f5299a84bf612b9.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
+             ['test_images/b272fec7783daa63f32c.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/bb202b3eaec47c9a25d5.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
+             ['test_images/bf1e22b0a44a76142f5b.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/ea5473c5f53f27617e2e.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
+             ['test_images/ee106392e56837366e79.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/f88d2214a4ee76b02fff.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45]]
+
+# Load YOLO model
+model = YOLO('linhcuem/chamdiem_yolov8_ver10')
+
+###################################################
+def yolov8_img_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,
+):
+    model = YOLO(model_path)
+    model.overrides['conf'] = conf_threshold
+    model.overrides['iou'] = iou_threshold
+    model.overrides['agnostic_nms'] = False
+    model.overrides['max_det'] = 1000
+    results = model.predict(image)
+    render = render_result(model=model, image=image, result=results[0])
+
+    return render
+
+inputs_images = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    gr.inputs.Dropdown(["linhcuem/chamdiem_yolov8_ver10"],
+                      default="linhcuem/chamdiem_yolov8_ver10", 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"),   
+]
+
+output_image = gr.outputs.Image(type="filepath", label="Output Image")
+interface_image = gr.Interface(
+    fn=yolov8_img_inference,
+    inputs=inputs_images,
+    outputs=output_image,
+    title=model_heading,
+    description=description,
+    examples=image_path,
+    cache_examples=False,
+    theme='huggingface'
+)
+
+gr.TabbedInterface(
+    [interface_image],
+    tab_names=['Image inference']
+).queue().launch()