Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+from ultralytics import YOLO
+from pathlib import Path
+import tempfile
+
+# 初始化模型占位符
+model = YOLO('yolov8n.pt')  # 默认加载官方模型
+
+def load_custom_model(model_path):
+    global model
+    model = YOLO(model_path)
+    return "模型加载成功！"
+
+def detect_media(input_type, input_data):
+    if input_type == "camera":
+        # 摄像头实时检测
+        frame = input_data
+        results = model.predict(source=frame, stream=True)
+        annotated_frame = results[0].plot()
+        return annotated_frame[:, :, ::-1]  # BGR转RGB
+    
+    elif input_type == "image":
+        # 图片检测
+        results = model.predict(source=input_data)
+        return results[0].plot()[:, :, ::-1]
+    
+    elif input_type == "video":
+        # 视频检测处理
+        cap = cv2.VideoCapture(input_data)
+        output_frames = []
+        
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret: break
+            
+            results = model.predict(source=frame)
+            annotated_frame = results[0].plot()
+            output_frames.append(annotated_frame)
+        
+        # 生成临时输出视频
+        output_path = str(Path(tempfile.gettempdir()) / "output.mp4")
+        out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), 
+                            30, (annotated_frame.shape[1], annotated_frame.shape[0]))
+        for f in output_frames:
+            out.write(f)
+        out.release()
+        
+        return output_path
+
+# Gradio界面布局
+with gr.Blocks(title="YOLOv8检测系统") as demo:
+    gr.Markdown("# 🚀 YOLOv8多功能检测系统")
+    
+    with gr.Tab("⚙️ 模型管理"):
+        model_upload = gr.File(label="上传模型文件(.pt)", file_types=[".pt"])
+        load_btn = gr.Button("加载模型", variant="primary")
+        model_status = gr.Textbox(label="模型状态")
+    
+    with gr.Tab("📷 实时摄像头"):
+        webcam = gr.Image(source="webcam", streaming=True, label="摄像头画面")
+        cam_output = gr.Image(label="检测结果", interactive=False)
+        webcam.stream(fn=lambda x: detect_media("camera", x), 
+                     inputs=webcam, outputs=cam_output)
+    
+    with gr.Tab("🖼️ 图片检测"):
+        img_input = gr.Image(type="filepath", label="上传图片")
+        img_btn = gr.Button("开始检测", variant="primary")
+        img_output = gr.Image(label="检测结果")
+    
+    with gr.Tab("🎥 视频检测"):
+        vid_input = gr.Video(label="上传视频")
+        vid_btn = gr.Button("处理视频", variant="primary")
+        vid_output = gr.Video(label="处理结果")
+    
+    # 事件绑定
+    load_btn.click(fn=load_custom_model, inputs=model_upload, outputs=model_status)
+    img_btn.click(fn=lambda x: detect_media("image", x), inputs=img_input, outputs=img_output)
+    vid_btn.click(fn=lambda x: detect_media("video", x), inputs=vid_input, outputs=vid_output)
+
+if __name__ == "__main__":
+    demo.queue(concurrency_count=3).launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )