Update app.py

app.py

@@ -1,15 +1,15 @@
 # app.py
-from fastapi import FastAPI, File, UploadFile
+from fastapi import FastAPI, UploadFile, File
 from fastapi.middleware.cors import CORSMiddleware
-from ultralytics import YOLO
+import requests
 import uvicorn
-import numpy as np
 from PIL import Image
 import io
+import base64
 
 app = FastAPI()
 
-# CORS 활성화 (ConcreteAI 웹과 연결)
+# CORS 허용
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -18,46 +18,47 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# ---- YOLOv8 모델 로드 ----
-print("🔵 Loading YOLOv8 crack model...")
-model = YOLO("keremberke/yolov8n-concrete-crack")
-print("✅ Model loaded!")
+# 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}"
+}
+
 
 @app.post("/predict")
 async def predict(img: UploadFile = File(...)):
     # 이미지 읽기
-    image_bytes = await img.read()
-    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-    np_img = np.array(image)
-
-    # YOLOv8 추론
-    results = model(np_img)[0]
-
-    # 박스 리스트
-    detections = results.boxes
-
-    if detections is None or len(detections) == 0:
-        # 균열 없음
-        return {
-            "data": [
-                {
-                    "label": "normal",
-                    "confidence": 1.0
-                }
-            ]
-        }
-
-    # 가장 높은 confidence 선택
-    max_conf = float(max(d.conf[0].item() for d in detections))
+    bytes_data = await img.read()
+
+    response = requests.post(
+        HF_API_URL,
+        headers=headers,
+        data=bytes_data
+    )
+
+    try:
+        results = response.json()
+    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 {
         "data": [
             {
                 "label": "crack",
-                "confidence": max_conf
+                "confidence": float(max_conf)
             }
         ]
     }
 
+
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)