import joblib
import pandas as pd
from flask import Flask, request, jsonify
import numpy as np

# Initialize Flask app
sales_forecast_api = Flask("SuperKart Sales Forecast Predictor")

# Load the trained SuperKart sales model
model = joblib.load("superkart_sales_model_v1_0.joblib")

# Define a route for the home page
@sales_forecast_api.get('/')
def home():
    return "Welcome to the SuperKart Sales Revenue Forecasting API!"

# Define an endpoint to predict sales for a single product-store combination
@sales_forecast_api.post('/v1/sales')
def predict_sales():
    # Get JSON data from the request
    sales_data = request.get_json()

    # Extract relevant features from the input data
    # Note: Store_Age will be calculated from Store_Establishment_Year
    current_year = 2024
    store_age = current_year - sales_data['Store_Establishment_Year']

    sample = {
        'Product_Weight': sales_data['Product_Weight'],
        'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
        'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
        'Product_Type': sales_data['Product_Type'],
        'Product_MRP': sales_data['Product_MRP'],
        'Store_Establishment_Year': sales_data['Store_Establishment_Year'],
        'Store_Size': sales_data['Store_Size'],
        'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
        'Store_Type': sales_data['Store_Type'],
        'Store_Age': store_age
    }

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

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

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

# Define an endpoint to predict sales for a batch of product-store combinations
@sales_forecast_api.post('/v1/salesbatch')
def predict_sales_batch():
    # Get the uploaded CSV file from the request
    file = request.files['file']

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

    # Calculate Store_Age if not present
    if 'Store_Age' not in input_data.columns:
        current_year = 2024
        input_data['Store_Age'] = current_year - input_data['Store_Establishment_Year']

    # Make predictions for the batch data
    predictions = model.predict(input_data).tolist()

    # Add predictions to the DataFrame
    input_data['Predicted_Sales_Total'] = predictions

    # Convert results to dictionary
    result = input_data.to_dict(orient="records")

    return jsonify(result)

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