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  # ✅ Import CORS

# Initialize Flask app
sales_prediction_api = Flask("SuperKart Sales Price Prediction API")
CORS(sales_prediction_api)  # ✅ Enable CORS globally

# Load the trained machine learning model
model = joblib.load("sales_price_prediction_model_v1_0.joblib")

# Home route
@sales_prediction_api.get('/')
def home():
    return "Welcome to SuperKart Sales Price Prediction API!"

# ✅ Single prediction endpoint
@sales_prediction_api.post('/v1/sales')
def predict_sales_price():
    try:
        data = request.get_json()

        # Ensure all required fields are provided
        required_fields = ['Product_Weight', 'Product_Allocated_Area', 'Product_MRP', 'Store_Establishment_Year',
                           'Product_Sugar_Content', 'Store_Size', 'Store_Location_City_Type',
                           'Store_Type', 'Product_Type', 'Store_ID']
        missing_fields = [field for field in required_fields if field not in data]
        if missing_fields:
            return jsonify({"error": f"Missing fields: {', '.join(missing_fields)}"}), 400

         # Extract Store_Id
        store_id = data['Store_ID']

        input_sample = {
            'Product_Weight': data['Product_Weight'],
            'Product_Allocated_Area': data['Product_Allocated_Area'],
            'Product_MRP': data['Product_MRP'],
            'Store_Establishment_Year': data['Store_Establishment_Year'],
            'Product_Sugar_Content': data['Product_Sugar_Content'],
            'Store_Size': data['Store_Size'],
            'Store_Location_City_Type': data['Store_Location_City_Type'],
            'Store_Type': data['Store_Type'],
            'Product_Type': data['Product_Type']
        }

        # One-hot encode Store_Id
        for col in ['Store_OUT001', 'Store_OUT002', 'Store_OUT003', 'Store_OUT004']:
            input_sample[col] = 1 if col == f'Store_{store_id}' else 0

        input_df = pd.DataFrame([input_sample])
        prediction = model.predict(input_df)[0]

        return jsonify({"Predicted Sales": round(float(prediction), 2)})

    except Exception as e:
        return jsonify({"error": str(e)}), 500

# ✅ Batch prediction endpoint
@sales_prediction_api.post('/v1/salesbatch')
def predict_sales_price_batch():
    if 'file' not in request.files:
        return jsonify({"error": "CSV file not found."}), 400

    file = request.files['file']
    df = pd.read_csv(file)

    if 'id' not in df.columns:
        return jsonify({"error": "Missing 'id' column in uploaded CSV"}), 400

    preds = model.predict(df)
    results = [
        {"id": i, "Predicted Sales": round(float(p), 2)}
        for i, p in zip(df['id'], preds)
    ]
    return jsonify(results)

# Debug run (for local testing)
if __name__ == '__main__':
    sales_prediction_api.run(debug=True)