Update app.py

app.py

@@ -1,55 +1,70 @@
 from flask import Flask, request, jsonify, make_response
-import joblib, pandas as pd, numpy as np, os, time, sys
+import joblib, pandas as pd, numpy as np, os, sys, time
 
-print("==> [BOOT] Starting app.py", flush=True)
 app = Flask(__name__)
 
-MODEL_PATH = os.getenv("MODEL_PATH", "superkart_sales_forecast.joblib")
+# -----------------------------
+# Config & Model Loading
+# -----------------------------
+MODEL_PATH = os.getenv("MODEL_PATH", "best_model_random_forest.joblib")
 PORT = int(os.getenv("PORT", "5000"))
 
+print(f"==> [BOOT] Starting Flask app on port {PORT}")
 print(f"==> [BOOT] MODEL_PATH={MODEL_PATH}", flush=True)
 
 def load_pipeline(path: str):
-    t0 = time.time()
+    start = time.time()
     if not os.path.exists(path):
         raise FileNotFoundError(f"Model file not found: {path}")
     print(f"==> [LOAD] Loading model from {path} ...", flush=True)
-    bundle = joblib.load(path)
-    if isinstance(bundle, dict) and "pipeline" in bundle:
-        pipe = bundle["pipeline"]
+    model_obj = joblib.load(path)
+    if isinstance(model_obj, dict) and "pipeline" in model_obj:
+        pipe = model_obj["pipeline"]
         print("==> [LOAD] Loaded dict bundle with 'pipeline'", flush=True)
     else:
-        pipe = bundle
+        pipe = model_obj
         print("==> [LOAD] Loaded pipeline object", flush=True)
-    print(f"==> [LOAD] Done in {time.time()-t0:.2f}s", flush=True)
+    print(f"==> [LOAD] Model load complete in {time.time()-start:.2f}s", flush=True)
     return pipe
 
 try:
     pipe = load_pipeline(MODEL_PATH)
-    MODEL_READY = True
-    LOAD_ERROR = None
+    MODEL_READY, LOAD_ERROR = True, None
 except Exception as e:
-    print("==> [ERROR] Model load failed:", e, file=sys.stderr, flush=True)
-    pipe = None
-    MODEL_READY = False
-    LOAD_ERROR = str(e)
+    print(f"==> [ERROR] Model load failed: {e}", file=sys.stderr, flush=True)
+    pipe, MODEL_READY, LOAD_ERROR = None, False, str(e)
 
+# -----------------------------
+# Basic CORS (no external deps)
+# -----------------------------
 @app.after_request
-def cors(resp):
+def add_cors_headers(resp):
     resp.headers["Access-Control-Allow-Origin"] = "*"
     resp.headers["Access-Control-Allow-Headers"] = "Content-Type, Authorization"
     resp.headers["Access-Control-Allow-Methods"] = "GET, POST, OPTIONS"
     return resp
 
+# -----------------------------
+# Health & Root
+# -----------------------------
 @app.get("/")
 def root():
-    return jsonify({"service": "SuperKart API", "health": "/health", "predict": "/predict", "model_path": MODEL_PATH})
+    return jsonify({
+        "service": "SuperKart Sales Forecast API",
+        "health": "/health",
+        "predict": "/predict",
+        "model_path": MODEL_PATH
+    })
 
 @app.get("/health")
 def health():
-    return (jsonify({"status": "ok", "model_path": MODEL_PATH}), 200) if MODEL_READY \
-        else (jsonify({"status": "error", "model_path": MODEL_PATH, "error": LOAD_ERROR}), 500)
+    if MODEL_READY:
+        return jsonify({"status": "ok", "model_path": MODEL_PATH}), 200
+    return jsonify({"status": "error", "error": LOAD_ERROR, "model_path": MODEL_PATH}), 500
 
+# -----------------------------
+# Prediction
+# -----------------------------
 @app.route("/predict", methods=["OPTIONS"])
 def predict_options():
     return make_response(("", 204))
@@ -58,17 +73,26 @@ def predict_options():
 def predict():
     if not MODEL_READY or pipe is None:
         return jsonify({"error": "Model not loaded", "details": LOAD_ERROR}), 503
-    data = request.get_json(force=True)
-    if isinstance(data, dict):
-        df = pd.DataFrame([data])
-    elif isinstance(data, list):
-        df = pd.DataFrame(data)
-    else:
-        return jsonify({"error": "Payload must be an object or list of objects"}), 400
-    preds = pipe.predict(df)
-    preds = [float(x) if isinstance(x, (np.floating, float, int)) else x for x in preds]
-    return jsonify({"predictions": preds})
 
+    try:
+        data = request.get_json(force=True)
+        if isinstance(data, dict):
+            df = pd.DataFrame([data])
+        elif isinstance(data, list):
+            df = pd.DataFrame(data)
+        else:
+            return jsonify({"error": "Invalid input. Must be JSON object or list of objects."}), 400
+
+        preds = pipe.predict(df)
+        preds = [float(p) if isinstance(p, (np.floating, float, int)) else p for p in preds]
+        return jsonify({"predictions": preds})
+
+    except Exception as e:
+        return jsonify({"error": "Prediction failed", "details": str(e)}), 500
+
+# -----------------------------
+# Entry Point
+# -----------------------------
 if __name__ == "__main__":
-    print("==> [RUN] Flask dev server starting on 0.0.0.0:5000", flush=True)
+    print("==> [RUN] Flask dev server starting...", flush=True)
     app.run(host="0.0.0.0", port=PORT)