美化UI界面：添加新布局和样式

app.py

@@ -6,7 +6,6 @@ import numpy as np
 from fastapi import FastAPI, File, UploadFile
 from PIL import Image
 import io
-import os
 
 # 初始化 FastAPI
 app = FastAPI()
@@ -16,69 +15,28 @@ model = YOLO("NailongKiller.yolo11n.pt")
 
 def detect_objects(image):
     if image is None:
-        return None, "No image provided"
+        return None
     
     try:
-        # 运行推理
         results = model(image)
         result = results[0]
-        
-        # 在图像上绘制检测框
         annotated_image = result.plot()
-        annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
-        
-        # 获取检测结果统计
-        num_detections = len(result.boxes)
-        detection_info = f"检测到 {num_detections} 个目标"
-        
-        return annotated_image, detection_info
+        return cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
     except Exception as e:
-        return None, f"Error: {str(e)}"
+        return None
 
-# 创建Gradio界面
+# 创建 Gradio 界面
 demo = gr.Interface(
     fn=detect_objects,
-    inputs=gr.Image(type="numpy", label="输入图片 | Input Image"),
-    outputs=[
-        gr.Image(type="numpy", label="检测结果 | Detection Result"),
-        gr.Textbox(label="检测信息 | Detection Info")
-    ],
-    title="🐉 奶龙杀手 (NailongKiller)",
-    description="""
-    这是一个基于 YOLO 的奶龙检测系统。上传图片即可自动检测图中的奶龙。
-    
-    This is a YOLO-based Nailong detection system. Upload an image to detect Nailong automatically.
-    """,
-    theme=gr.themes.Default(),
-    allow_flagging="never",
-    examples=["example1.jpg", "example2.jpg"] if all(os.path.exists(f) for f in ["example1.jpg", "example2.jpg"]) else None
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(),
+    title="奶龙杀手 (Nailong Killer)",
+    description="上传图片来检测奶龙 | Upload an image to detect Nailong"
 )
 
-# API端点
-@app.post("/detect/")
-async def detect_api(file: UploadFile = File(...)):
-    contents = await file.read()
-    image = Image.open(io.BytesIO(contents))
-    image_np = np.array(image)
-    
-    results = model(image_np)
-    result = results[0]
-    
-    detections = []
-    for box in result.boxes:
-        detection = {
-            "bbox": box.xyxy[0].tolist(),
-            "confidence": float(box.conf[0]),
-            "class": int(box.cls[0])
-        }
-        detections.append(detection)
-    
-    return {"detections": detections}
-
-# 将Gradio接口挂载到FastAPI
+# 将 Gradio 接口挂载到 FastAPI
 app = gr.mount_gradio_app(app, demo, path="/")
 
-# 启动应用
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)