美化UI界面：添加新布局和样式

app.py

@@ -1,16 +1,19 @@
 import gradio as gr
 from ultralytics import YOLO
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
+import numpy as np
+import io
 
-# 加载模型
+# 初始化 FastAPI 和模型
+app = FastAPI()
 model = YOLO('NailongKiller.yolo11n.pt')
 
 def predict(img):
-    # 运行推理
     results = model.predict(img)
-    # 返回第一个结果的绘制图像
     return results[0].plot()
 
-# 创建 Gradio 演示
+# Gradio 界面
 demo = gr.Interface(
     predict,
     inputs=[
@@ -27,6 +30,34 @@ demo = gr.Interface(
     cache_examples=True
 )
 
+# API 端点
+@app.post("/detect/")
+async def detect_api(file: UploadFile = File(...)):
+    # 读取上传的图片
+    contents = await file.read()
+    image = Image.open(io.BytesIO(contents))
+    image_np = np.array(image)
+    
+    # 运行推理
+    results = model.predict(image_np)
+    result = results[0]
+    
+    # 返回检测结果
+    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}
+
+# 挂载 Gradio 到 FastAPI
+app = gr.mount_gradio_app(app, demo, path="/")
+
 # 启动应用
 if __name__ == "__main__":
-    demo.launch()
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -3,3 +3,7 @@ torch>=1.8.0
 torchvision>=0.9.0
 ultralytics>=8.3.0
 Pillow
+fastapi
+python-multipart
+uvicorn
+numpy