# 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
from pathlib import Path # For using a robust, absolute path

# Define the base directory of the script
BASE_DIR = Path(__file__).resolve().parent

# Define the full path to your model file
MODEL_PATH = BASE_DIR / "xgb_tuned.joblib"

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

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

# 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 Predictor API !"

# Define an endpoint to predict for a single observation
@superkart_api.post('/v1/predict')
def predict_sales():
    """
    This function handles POST requests to the '/v1/predict' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted rental price as a JSON response.
    """
    # Get JSON data from the request
    data = request.get_json()

    # Extract relevant customer features from the input data. The order of the column names matters.
    sample = {
        'Product_Weight': data['Product_Weight'],
        'Product_MRP': data['Product_MRP'],
        'Product_Allocated_Area': data['Product_Allocated_Area'],
        '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'],
        'Store_Age_Years': data['Store_Age_Years'],
        'Product_Id_prefix': data['Product_Id_prefix'],
        'Product_FD_perishable': data['Product_FD_perishable'],
    }

    # Convert the extracted data into a DataFrame
    input_data = pd.DataFrame([sample])

    # Make a store sales prediction using the trained model
    prediction = model.predict(input_data).tolist()[0]

    # Return the prediction as a JSON response
    return jsonify({'Sales': prediction})

# Define an endpoint for batch prediction (POST request)
@superkart_api.post('/v1/batch')
def predict_sales_batch():
    """
    This function handles POST requests to the '/v1/batch' endpoint.
    It expects a CSV file containing property details for multiple properties
    and returns the predicted rental prices as a dictionary in the JSON response.
    """
    # Get the uploaded CSV file from the request
    file = request.files['file']

    # Read the CSV file into a Pandas DataFrame
    input_data = pd.read_csv(file)

    # Make predictions for all properties in the DataFrame
    predicted_sales = model.predict(input_data).tolist()

    # Create a dictionary of predictions with property IDs as keys
    product_ids = input_data['Product_Id'].tolist()
    output_dict = dict(zip(product_ids, predicted_sales))

    # Return the predictions dictionary as a JSON response
    return output_dict


# Run the Flask app in debug mode
if __name__ == '__main__':
    superkart_api.run(debug=True)