Update app.py

app.py

@@ -1,8 +1,6 @@
 from fastapi import FastAPI, UploadFile, File
 from fastapi.middleware.cors import CORSMiddleware
 from ultralytics import YOLO
-from huggingface_hub import hf_hub_download
-
 import numpy as np
 from PIL import Image
 import io
@@ -10,7 +8,7 @@ import uvicorn
 
 app = FastAPI()
 
-# CORS
+# CORS 설정
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -19,16 +17,20 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# ===========================================
-#       DOWNLOAD CRACK MODEL FROM HF
-# ===========================================
-
+# 모델 로드
 print("🔵 Loading local crack model...")
 model = YOLO("best.pt")
 print("✅ Crack Model Loaded Successfully")
 
 # ===========================================
-#               PREDICTION API
+# 1. (추가됨) 기본 경로("/") 처리 - 브라우저 접속 테스트용
+# ===========================================
+@app.get("/")
+def read_root():
+    return {"message": "ConcreteAI Crack Detection API is running!", "status": "OK"}
+
+# ===========================================
+# 2. 예측 API
 # ===========================================
 @app.post("/predict")
 async def predict(img: UploadFile = File(...)):
@@ -36,21 +38,18 @@ async def predict(img: UploadFile = File(...)):
         bytes_data = await img.read()
         image = Image.open(io.BytesIO(bytes_data)).convert("RGB")
         np_img = np.array(image)
-
         results = model(np_img)
         result = results[0]
-
-        # crack detection: check boxes
+        
+        # 균열 감지 로직
         if result.boxes is None or len(result.boxes) == 0:
             return {
                 "data": [
                     {"label": "normal", "confidence": 1.0}
                 ]
             }
-
-        # There are crack boxes
+        
         conf = float(result.boxes.conf.max().item())
-
         return {
             "data": [
                 {
@@ -59,7 +58,6 @@ async def predict(img: UploadFile = File(...)):
                 }
             ]
         }
-
     except Exception as e:
         print("❌ Prediction error:", e)
         return {
@@ -67,6 +65,5 @@ async def predict(img: UploadFile = File(...)):
             "error": str(e)
         }
 
-
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)