# 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
sales_forecast_api = Flask("Sales Forecast Prediction API ")

# Load the trained churn prediction model
model = joblib.load("sales_forecast_model_v1_0.joblib")

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

# Define an endpoint to predict churn for a single customer
@sales_forecast_api.post('/v1/product')
def predict_sales_forecast():
    # Get JSON data from the request
    product_data = request.get_json()

    # Extract relevant customer features from the input data
    sample = {
        'Product_Weight': product_data['Product_Weight'],
        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
        'Product_Type': product_data['Product_Type'],
        'Product_MRP': product_data['Product_MRP'],
        'Store_Id': product_data['Store_Id'],
        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
        'Store_Size': product_data['Store_Size'],
        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
        'Store_Type': product_data['Store_Type']
    }

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

    # Make prediction (get sales_forecast)
    predicted_price = model.predict(input_data)[0]

    # Map prediction result to a human-readable label
    predicted_price = round(float(predicted_price), 2)

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

# Define an endpoint to predict forecast for a batch of products
@sales_forecast_api.post('/v1/productbatch')
def predict_sales_forecast_batch():
    # 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 products in the DataFrame (get log_prices)
    predicted_sales = model.predict(input_data).tolist()

    # Calculate actual prices
    predicted_sales = [round(float(Product_Store_Sales_Total), 2) for Product_Store_Sales_Total in predicted_sales]

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

    # Return the predictions dictionary as a JSON response
    return output_dict

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