diff --git a/main.py b/main.py
new file mode 100644
index 0000000..9474657
--- /dev/null
+++ b/main.py
@@ -0,0 +1,94 @@
+import base64
+import io
+import logging
+import os
+import time
+
+import numpy as np
+from PIL import Image
+from fastapi import FastAPI, Request, status
+from fastapi.responses import RedirectResponse
+
+# 导入你的DetectionTool类
+from utils.detection_tool import DetectionTool  # 请替换为实际模块路径
+
+"""
+pip install ultralytics
+pip install fastapi
+"""
+
+# 基础配置
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
+save_dir = os.getenv("SAVE_DIR", "save_dir")
+
+# 初始化检测工具
+detector = DetectionTool()
+
+# 创建FastAPI实例
+app = FastAPI()
+
+
+def image_to_numpy(image_source):
+    """将图片源转换为numpy数组"""
+    if isinstance(image_source, str):
+        # 处理文件路径或URL
+        if image_source.startswith(("http://", "https://")):
+            import requests
+            response = requests.get(image_source)
+            if response.status_code != 200:
+                raise ValueError(f"无法获取图片，状态码: {response.status_code}")
+            image = Image.open(io.BytesIO(response.content))
+        else:
+            # 本地文件路径
+            image = Image.open(image_source)
+    elif isinstance(image_source, bytes):
+        # 二进制数据
+        image = Image.open(io.BytesIO(image_source))
+    else:
+        raise TypeError("不支持的图片源类型")
+
+    # 转换为RGB并转为numpy数组
+    return np.array(image.convert("RGB"))
+
+
+@app.post(f"/v1/predict")
+async def predict(request: Request):
+    """核心预测接口：接收base64编码图片，返回检测结果"""
+    data = await request.json()
+    try:
+        # 解码base64图片为numpy数组
+        image_bytes = base64.b64decode(data["image"])
+        img_np = image_to_numpy(image_bytes)
+
+        # 使用你的检测工具获取结果
+        image_detected, result = detector.detect_image(img_np, image_format="pillow")
+
+        # 处理保存逻辑（如果需要）
+        if save_dir is not None:
+            os.makedirs(save_dir, exist_ok=True)
+            # 可根据需要保存输入图片和结果
+            timestamp = int(time.time())
+            img_save_path = os.path.join(save_dir, f"input_{timestamp}.jpg")
+            image_detected.save(img_save_path)
+
+        return result
+
+    except Exception as e:
+        logger.error(f"预测出错: {str(e)}")
+        return str(e)
+
+
+@app.get("/")
+async def index():
+    """根路径重定向到API文档"""
+    return RedirectResponse(url="/docs", status_code=status.HTTP_302_FOUND)
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8080)