Update app.py

app.py

@@ -6,13 +6,13 @@ from pathlib import Path
 import tempfile
 import os
 
-# 初始化空模型
+# 初始化模型占位符
 model = None
 
 def load_custom_model(model_file):
     global model
     try:
-        # 保存上传文件到临时目录
+        # 保存上传文件
         save_path = f"/tmp/{model_file.name}"
         with open(save_path, "wb") as f:
             f.write(model_file.read())
@@ -23,101 +23,61 @@ def load_custom_model(model_file):
     except Exception as e:
         return f"❌ 加载失败：{str(e)}"
 
-def process_frame(frame, input_type):
-    global model
+def process_frame(frame):
     if model is None:
-        raise gr.Error("请先上传并加载模型")
+        raise gr.Error("请先上传模型文件")
     
     results = model.predict(
         source=frame,
-        verbose=False,
         device="cpu",
-        conf=0.5
+        conf=0.5,
+        verbose=False
     )
     return cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
 
-def video_pipeline(input_video):
-    if model is None:
-        raise gr.Error("请先上传并加载模型")
-    
-    # 创建临时文件
-    temp_dir = tempfile.mkdtemp()
-    output_path = os.path.join(temp_dir, "output.mp4")
-    
-    # 处理视频
-    cap = cv2.VideoCapture(input_video)
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    
-    writer = cv2.VideoWriter(
-        output_path,
-        cv2.VideoWriter_fourcc(*'mp4v'),
-        fps,
-        (width, height)
-    )
-    
-    try:
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break
-            writer.write(process_frame(frame, "video"))
-    finally:
-        cap.release()
-        writer.release()
-    
-    return output_path
-
-with gr.Blocks(title="自定义模型检测系统") as demo:
-    gr.Markdown("# 🛠️ 自定义YOLOv8模型检测系统")
+# Gradio界面（兼容旧版）
+with gr.Blocks(title="YOLO检测系统") as demo:
+    gr.Markdown("# 🔍 自定义YOLO检测系统")
     
     with gr.Tab("🔧 模型管理"):
-        with gr.Row():
-            upload_btn = gr.UploadButton(
-                "上传模型文件 (.pt)",
-                file_types=[".pt"],
-                variant="primary"
-            )
-            model_status = gr.Textbox(label="状态", interactive=False)
-        
-        upload_btn.upload(
-            fn=load_custom_model,
-            inputs=upload_btn,
-            outputs=model_status
+        upload_btn = gr.UploadButton(
+            "上传.pt模型文件",
+            file_types=[".pt"],
+            variant="primary"
         )
+        status = gr.Textbox(label="状态")
+        upload_btn.upload(load_custom_model, upload_btn, status)
     
     with gr.Tab("📷 实时检测"):
-        webcam = gr.Webcam(label="摄像头")
-        cam_output = gr.Image(label="检测结果")
+        webcam = gr.Image(
+            source="webcam",
+            streaming=True,
+            label="摄像头画面"
+        )
+        output = gr.Image(label="检测结果")
         webcam.stream(
-            fn=lambda x: process_frame(x, "camera"),
+            fn=lambda x: process_frame(x),
             inputs=webcam,
-            outputs=cam_output,
-            show_progress="hidden"
+            outputs=output
         )
     
     with gr.Tab("🖼️ 图片检测"):
-        img_input = gr.Image(type="filepath", label="上传图片")
-        img_output = gr.Image(label="检测结果")
-        img_input.upload(
-            fn=lambda x: process_frame(cv2.imread(x), "image"),
+        img_input = gr.Image(type="filepath")
+        img_output = gr.Image()
+        img_input.change(
+            fn=lambda x: process_frame(cv2.imread(x)),
             inputs=img_input,
             outputs=img_output
         )
     
     with gr.Tab("🎥 视频检测"):
-        vid_input = gr.Video(label="上传视频")
-        vid_output = gr.Video(label="处理结果")
-        vid_input.upload(
-            fn=video_pipeline,
+        vid_input = gr.Video()
+        vid_output = gr.Video()
+        vid_input.change(
+            fn=lambda x: video_pipeline(x),
             inputs=vid_input,
             outputs=vid_output
         )
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+    demo.launch(server_name="0.0.0.0")