Create app.py

app.py

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+import gradio as gr 
+import pandas as pd 
+from skimage import data
+from ultralytics.yolo.data import utils 
+import ultralytics 
+from pathlib import Path
+from torchkeras import plots
+ 
+model = ultralytics.YOLO('yolov8n.pt')
+ 
+#load class_names
+yaml_path = str(Path(ultralytics.__file__).parent/'datasets/coco128.yaml') 
+class_names = utils.yaml_load(yaml_path)['names']
+
+def detect(img):
+    if isinstance(img,str):
+        img = get_url_img(img) if img.startswith('http') else Image.open(img).convert('RGB')
+    result = model.predict(source=img)
+    if len(result[0].boxes.boxes)>0:
+        vis = plots.plot_detection(img,boxes=result[0].boxes.boxes,
+                     class_names=class_names, min_score=0.2)
+    else:
+        vis = img
+    return vis
+    
+with gr.Blocks() as demo:
+    gr.Markdown("# yolov8目标检测演示")
+ 
+    with gr.Tab("捕捉摄像头喔"):
+        in_img = gr.Image(source='webcam',type='pil')
+        button = gr.Button("执行检测",variant="primary")
+ 
+        gr.Markdown("## 预测输出")
+        out_img = gr.Image(type='pil')
+ 
+        button.click(detect,
+                     inputs=in_img, 
+                     outputs=out_img)
+        
+    
+gr.close_all() 
+demo.queue(concurrency_count=5)
+demo.launch()