Update app.py

app.py

@@ -3,13 +3,11 @@ from fastapi import FastAPI, UploadFile, File
 from fastapi.middleware.cors import CORSMiddleware
 import requests
 import uvicorn
-from PIL import Image
-import io
-import base64
+import os
 
 app = FastAPI()
 
-# CORS 허용
+# CORS 설정
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -20,16 +18,13 @@ app.add_middleware(
 
 # HuggingFace 모델 엔드포인트
 HF_API_URL = "https://api-inference.huggingface.co/models/keremberke/yolov8n-concrete-crack"
-HF_TOKEN = "YOUR_HF_TOKEN"  # 반드시 입력 필요
-
-headers = {
-    "Authorization": f"Bearer {HF_TOKEN}"
-}
 
+# ❗ 토큰은 절대로 문자열로 넣으면 안 됨. 환경변수로만!
+HF_TOKEN = os.getenv("HF_TOKEN")
+headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
 
 @app.post("/predict")
 async def predict(img: UploadFile = File(...)):
-    # 이미지 읽기
     bytes_data = await img.read()
 
     response = requests.post(
@@ -43,11 +38,9 @@ async def predict(img: UploadFile = File(...)):
     except:
         return {"data": [{"label": "normal", "confidence": 1.0}]}
 
-    # 결과가 bounding box 리스트
     if not isinstance(results, list) or len(results) == 0:
         return {"data": [{"label": "normal", "confidence": 1.0}]}
 
-    # confidence 최고값 찾기
     max_conf = max(item.get("score", 0) for item in results)
 
     return {
@@ -59,6 +52,5 @@ async def predict(img: UploadFile = File(...)):
         ]
     }
 
-
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)