Update app.py

app.py

@@ -7,8 +7,6 @@ import requests
 from huggingface_hub import hf_hub_download
 from prometheus_client import Counter, Histogram, Gauge, generate_latest
 
-print("🚀 Starting FastAPI application...")
-
 app = FastAPI(title="Loan Approval API", version="1.0")
 
 # Environment variables
@@ -16,9 +14,6 @@ API_KEY = os.getenv("API_KEY", "test-key-123")
 HF_MODEL_REPO = os.getenv("HF_MODEL_REPO")
 PROM_PUSHGATEWAY = os.getenv("PROM_PUSHGATEWAY")
 
-print(f"API_KEY: {'Set' if API_KEY else 'Not set'}")
-print(f"HF_MODEL_REPO: {HF_MODEL_REPO}")
-
 # Prometheus metrics
 REQS = Counter("pred_requests_total", "Total prediction requests")
 LAT = Histogram("pred_request_latency_seconds", "Request latency")
@@ -33,37 +28,45 @@ categorical_columns = []
 boolean_columns = []
 loaded = False
 
-# Load model on startup
-if not HF_MODEL_REPO:
-    print("⚠️  WARNING: HF_MODEL_REPO not set. Using mock mode.")
-    loaded = False
-else:
-    try:
-        print(f" Downloading model from {HF_MODEL_REPO}...")
-        
-        m = hf_hub_download(repo_id=HF_MODEL_REPO, filename="best_model.joblib")
-        e = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/encoders.joblib")
-        s = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/scaler.joblib")
-        f = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/feature_columns.joblib")
-        c = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/categorical_columns.joblib")
-        b = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/boolean_columns.joblib")
-
-        print(" Loading artifacts...")
-        model = joblib.load(m)
-        encoders = joblib.load(e)
-        scaler = joblib.load(s)
-        feature_columns = joblib.load(f)
-        categorical_columns = joblib.load(c)
-        boolean_columns = joblib.load(b)
-        loaded = True
-        
-        print(" Model loaded successfully!")
-        print(f"   Features: {len(feature_columns)}")
-    except Exception as ex:
-        print(f" Model load error: {ex}")
-        loaded = False
 
-print(" FastAPI app initialized")
+@app.on_event("startup")
+async def load_model():
+    global model, encoders, scaler, feature_columns, categorical_columns, boolean_columns, loaded
+    
+    print("🚀 Starting FastAPI application...")
+    print(f"API_KEY: {'Set' if API_KEY else 'Not set'}")
+    print(f"HF_MODEL_REPO: {HF_MODEL_REPO}")
+    
+    if not HF_MODEL_REPO:
+        print("⚠️  WARNING: HF_MODEL_REPO not set. Using mock mode.")
+        loaded = False
+    else:
+        try:
+            print(f" Downloading model from {HF_MODEL_REPO}...")
+            
+            m = hf_hub_download(repo_id=HF_MODEL_REPO, filename="best_model.joblib")
+            e = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/encoders.joblib")
+            s = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/scaler.joblib")
+            f = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/feature_columns.joblib")
+            c = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/categorical_columns.joblib")
+            b = hf_hub_download(repo_id=HF_MODEL_REPO, filename="models/boolean_columns.joblib")
+
+            print(" Loading artifacts...")
+            model = joblib.load(m)
+            encoders = joblib.load(e)
+            scaler = joblib.load(s)
+            feature_columns = joblib.load(f)
+            categorical_columns = joblib.load(c)
+            boolean_columns = joblib.load(b)
+            loaded = True
+            
+            print(" Model loaded successfully!")
+            print(f"   Features: {len(feature_columns)}")
+        except Exception as ex:
+            print(f" Model load error: {ex}")
+            loaded = False
+    
+    print(" FastAPI app initialized")
 
 
 @app.get("/")