Delete main.py

main.py

@@ -1,61 +0,0 @@
-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)}"
-        )
-