# Import necessary libraries
import numpy as np
import joblib  # For loading the serialized model
import pandas as pd  # For data manipulation
from flask import Flask, request, jsonify  # For creating the Flask API

# Initialize the Flask application
superkart_api = Flask("SuperKart Sales Predictor")

# Load the trained machine learning model
model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")


# Define a route for the home page (GET request)
@superkart_api.get('/')
def home():
    """
    This function handles GET requests to the root URL ('/') of the API.
    It returns a simple welcome message.
    """
    return "Welcome to the SuperKart Sales Prediction API!"


# Define an endpoint for single property prediction (POST request)
@superkart_api.post('/v1/sales')
def predict_sales():
    """
    POST endpoint to predict sales for a single product-store combination.
    Expects JSON input with product and store attributes.
    """
    try:
        # Get the JSON data from the request body
        data = request.get_json()
        print("Raw incoming data:", data)

        # Validate expected fields
        required_fields = [
            "Product_Weight",
            "Product_Allocated_Area",
            "Product_MRP",
            "Store_Age",
            "Product_Sugar_Content",
            "Product_Type",
            "Store_Size",
            "Store_Location_City_Type",
            "Store_Type",
            "Store_Id"
        ]
        missing_fields = [f for f in required_fields if f not in data]
        if missing_fields:
            return jsonify({
                "error": f"Missing required fields: {missing_fields}"
            }), 400

        # Extract relevant features from the JSON data
        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": data["Product_Sugar_Content"],
            "Product_Type": data["Product_Type"],
            "Store_Size": data["Store_Size"],
            "Store_Location_City_Type": data["Store_Location_City_Type"],
            "Store_Type": data["Store_Type"],
            "Store_Id": data["Store_Id"]
        }

        # Prepare data for prediction
        sample = {f: data[f] for f in required_fields}
        # Convert the extracted data into a Pandas DataFrame
        input_df = pd.DataFrame([sample])

        # Make prediction
        prediction = model.predict(input_df)[0]

        # Convert predicted_price to Python float
        predicted_sales = round(float(prediction), 2)

        # Return the predicted sales
        return jsonify({"Predicted_Sales_Total": predicted_sales})

    except Exception as e:
        # Catch all errors and return them as JSON
        return jsonify({"error": str(e)}), 500


# Run the Flask application in debug mode if this script is executed directly
if __name__ == '__main__':
    superkart_api.run(debug=True)