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app.py

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+import os
+import time
+import json
+import joblib
+import logging
+import traceback
+from typing import List, Any
+
+import numpy as np
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# ----------------------------
+# Config
+# ----------------------------
+MODEL_PATH = os.environ.get(
+    "MODEL_PATH",
+    "/content/superkart_best_tuned_RandomForest.joblib"
+)
+ALLOWED_EXTENSIONS = {"csv"}
+EXPECTED_COLUMNS: List[str] = None
+ID_COLUMNS = ["Product_Id", "Store_Id"]
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+log = logging.getLogger("superkart_api")
+
+# ----------------------------
+# Load model on startup
+# ----------------------------
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError(
+        f"MODEL_PATH not found: {MODEL_PATH}\n"
+        "Make sure your Drive is mounted and the path is correct."
+    )
+
+t0 = time.time()
+pipe = joblib.load(MODEL_PATH)
+load_secs = time.time() - t0
+log.info(f"Loaded pipeline from {MODEL_PATH} in {load_secs:.2f}s")
+
+assert hasattr(pipe, "predict"), "Loaded object does not have .predict(...)"
+assert "pre" in pipe.named_steps and "model" in pipe.named_steps, \
+    "Pipeline must contain 'pre' and 'model' steps."
+
+try:
+    raw_feature_names = getattr(pipe.named_steps["pre"], "feature_names_in_", None)
+except Exception:
+    raw_feature_names = None
+
+app = Flask(__name__)
+
+# ----------------------------
+# Helpers
+# ----------------------------
+def _allowed_file(filename: str) -> bool:
+    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def _align_columns(df: pd.DataFrame) -> pd.DataFrame:
+    global raw_feature_names
+    if raw_feature_names is not None:
+        for col in raw_feature_names:
+            if col not in df.columns:
+                df[col] = np.nan
+        df = df[list(raw_feature_names)]
+    if EXPECTED_COLUMNS:
+        for col in EXPECTED_COLUMNS:
+            if col not in df.columns:
+                df[col] = np.nan
+        df = df[EXPECTED_COLUMNS]
+    return df
+
+def _to_dataframe_from_json(payload: Any) -> pd.DataFrame:
+    if isinstance(payload, dict):
+        df = pd.DataFrame([payload])
+    elif isinstance(payload, list):
+        if not payload or not all(isinstance(x, dict) for x in payload):
+            raise ValueError("Payload must be a dict or list of dicts.")
+        df = pd.DataFrame(payload)
+    else:
+        raise ValueError("JSON payload must be object or list of objects.")
+    return _align_columns(df)
+
+def _predict_df(df: pd.DataFrame) -> np.ndarray:
+    preds = pipe.predict(df)
+    return np.asarray(preds).reshape(-1).astype(float)
+
+# ----------------------------
+# Routes
+# ----------------------------
+@app.get("/")
+def index():
+    return jsonify({
+        "service": "SuperKart Forecast API",
+        "status": "ok",
+        "endpoints": ["/health", "/model-info", "/predict", "/predict-csv"]
+    }), 200
+
+@app.get("/health")
+def health():
+    return jsonify({
+        "status": "ok",
+        "model_path": MODEL_PATH,
+        "loaded_in_seconds": round(load_secs, 3),
+        "training_features": list(raw_feature_names) if raw_feature_names is not None else None
+    })
+
+@app.get("/model-info")
+def model_info():
+    mdl = pipe.named_steps["model"]
+    try:
+        params = mdl.get_params()
+    except Exception:
+        params = str(mdl)
+    return jsonify({
+        "type": mdl.__class__.__name__,
+        "params": params
+    })
+
+@app.post("/predict")
+def predict():
+    try:
+        payload = request.get_json(silent=True)
+        if payload is None:
+            return jsonify({"error": "Invalid or empty JSON body."}), 400
+        df = _to_dataframe_from_json(payload)
+        preds = _predict_df(df)
+        echo_ids = {idc: df[idc].tolist() for idc in ID_COLUMNS if idc in df.columns}
+        return jsonify({"n": int(len(preds)), **({"ids": echo_ids} if echo_ids else {}), "predictions": preds.tolist()})
+    except Exception as e:
+        log.error("Predict error: %s\n%s", e, traceback.format_exc())
+        return jsonify({"error": str(e)}), 500
+
+@app.post("/predict-csv")
+def predict_csv():
+    try:
+        if "file" not in request.files:
+            return jsonify({"error": "No file part named 'file'."}), 400
+        file = request.files["file"]
+        if file.filename == "":
+            return jsonify({"error": "Empty filename."}), 400
+        if not _allowed_file(file.filename):
+            return jsonify({"error": "Only .csv files are allowed."}), 400
+        df = pd.read_csv(file)
+        df = _align_columns(df)
+        preds = _predict_df(df)
+        ids = {idc: df[idc].astype(str).tolist() for idc in ID_COLUMNS if idc in df.columns}
+        return jsonify({"n": int(len(preds)), **({"ids": ids} if ids else {}), "predictions": preds.tolist()})
+    except Exception as e:
+        log.error("Predict-CSV error: %s\n%s", e, traceback.format_exc())
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", "8000"))
+    app.run(host="0.0.0.0", port=port, debug=False)