app.py

# Import necessary libraries
import numpy as np
import joblib
import pandas as pd
from flask import Flask, request, jsonify
from flask_cors import CORS

app = Flask(__name__)
CORS(app)

# ── Load model ─────────────────────────────────────────────────────────────────
try:
    model = joblib.load("smartkart_model_v1_0.joblib")
    print("✅ Model loaded successfully.")
except Exception as e:
    model = None
    print(f"❌ Model failed to load: {e}")

# ── Category lists (must match training data exactly) ──────────────────────────
ALL_PRODUCT_TYPES = [
    "Baking Goods", "Breads", "Breakfast", "Canned", "Dairy",
    "Frozen Foods", "Fruits and Vegetables", "Hard Drinks",
    "Health and Hygiene", "Household", "Meat", "Others",
    "Seafood", "Snack Foods", "Soft Drinks", "Starchy Foods"
]

ALL_STORE_TYPES = [
    "Departmental Store", "Food Mart",
    "Supermarket Type1", "Supermarket Type2"
]

@app.get("/")
def home():
    return "Welcome to the SmartKart Total Sales Prediction API!"

@app.post("/v1/predict")
def predict_total_sales():
    data = request.get_json(silent=True)
    print(f"📥 Received payload: {data}")

    # ── Numeric + ordinal fields ───────────────────────────────────────────────
    sample = {
        "Product_Weight":               data["Product_Weight"],
        "Product_Allocated_Area":       data["Product_Allocated_Area"],
        "Product_MRP":                  data["Product_MRP"],
        "Store_Age":                    data["Store_Age"],
        "Product_Sugar_Content_Ord":    data["Product_Sugar_Content_Ord"],
        "Store_Size_Ord":               data["Store_Size_Ord"],
        "Store_Location_City_Type_Ord": data["Store_Location_City_Type_Ord"],
    }

    # ── One-hot encode Product_Type ────────────────────────────────────────────
    for pt in ALL_PRODUCT_TYPES:
        sample[f"Product_Type_{pt}"] = 1 if data["Product_Type"] == pt else 0

    # ── One-hot encode Store_Type ──────────────────────────────────────────────
    for st in ALL_STORE_TYPES:
        sample[f"Store_Type_{st}"] = 1 if data["Store_Type"] == st else 0

    input_df = pd.DataFrame([sample])
    print(f"📊 Input DataFrame columns: {input_df.columns.tolist()}")

    predicted_sales = round(float(model.predict(input_df)[0]), 2)
    print(f"✅ Prediction: {predicted_sales}")

    return jsonify({"Predicted_Total_Sales": predicted_sales})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)