Update app.py

app.py

@@ -1,29 +1,60 @@
-from fastapi import FastAPI
-import joblib
-import pandas as pd
-
-app = FastAPI()
-
-# Load models once at startup
-future_spend_model = joblib.load("future_spend_7d.pkl")
-spike_model = joblib.load("spike_probability.pkl")
-acc_model = joblib.load("acceleration.pkl")
-FEATURES = joblib.load("model_features.pkl")
-
-@app.get("/")
-def root():
-    return {"status": "ML backend running"}
-
-@app.post("/predict")
-def predict(payload: dict):
-    X = pd.DataFrame([payload], columns=FEATURES)
-
-    future_spend = future_spend_model.predict(X)[0]
-    spike_prob = spike_model.predict_proba(X)[0][1]
-    acceleration = acc_model.predict(X)[0]
-
-    return {
-        "future_7d_spend": round(float(future_spend), 2),
-        "spike_probability": round(float(spike_prob), 3),
-        "acceleration": round(float(acceleration), 2)
-    }
+from fastapi import FastAPI, HTTPException
+import joblib
+import pandas as pd
+from typing import Dict
+
+app = FastAPI(
+    title="Spending Risk ML Backend",
+    description="Predicts future spend, spike risk, and spending acceleration",
+    version="1.0.0"
+)
+
+# -----------------------------
+# Load models ONCE at startup
+# -----------------------------
+try:
+    future_spend_model = joblib.load("future_spend_7d.pkl")
+    spike_model = joblib.load("spike_probability.pkl")
+    acceleration_model = joblib.load("acceleration.pkl")
+    FEATURES = joblib.load("model_features.pkl")
+except Exception as e:
+    raise RuntimeError(f"❌ Model loading failed: {e}")
+
+# -----------------------------
+# Health check (HF requirement)
+# -----------------------------
+@app.get("/")
+def health_check():
+    return {
+        "status": "running",
+        "service": "spending-risk-backend"
+    }
+
+# -----------------------------
+# Prediction endpoint
+# -----------------------------
+@app.post("/predict")
+def predict(payload: Dict):
+    try:
+        # 1. Build feature vector safely
+        #    Missing features -> default 0
+        input_row = {feat: payload.get(feat, 0) for feat in FEATURES}
+        X = pd.DataFrame([input_row])
+
+        # 2. Predictions
+        future_spend = float(future_spend_model.predict(X)[0])
+        spike_prob = float(spike_model.predict_proba(X)[0][1])
+        acceleration = float(acceleration_model.predict(X)[0])
+
+        # 3. Response (frontend-friendly)
+        return {
+            "future_7d_spend": round(future_spend, 2),
+            "spike_probability": round(spike_prob, 3),
+            "acceleration": round(acceleration, 2)
+        }
+
+    except Exception as e:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Prediction failed: {str(e)}"
+        )