# 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 flask app with a name
sales_prediction_api = Flask("Superkart Sales Predictor")

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

# Define a route for the home page
@sales_prediction_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 Superkart Sales Prediction API!"

# Define an endpoint for single property prediction (POST request)
@sales_prediction_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 the JSON data from the request body
    business_data = request.get_json()

    # Extract relevant features from the JSON data
    sample = {
      'Product_Weight': business_data['Product_Weight'] ,
      'Product_Sugar_Content': business_data['Product_Sugar_Content'],
      'Product_Allocated_Area': business_data['Product_Allocated_Area'],
      'Product_Type': business_data['Product_Type'],
      'Product_MRP': business_data['Product_MRP'],
      'Store_Establishment_Year': business_data['Store_Establishment_Year'],
      'Store_Size': business_data['Store_Size'],
      'Store_Age': business_data['Store_Age'],
      'Store_Location_City_Type': business_data['Store_Location_City_Type'],
      'Store_Type': business_data['Store_Type']
  }

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

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

    # Convert predicted_price to Python float
    predicted_sales = float(predicted_sales)

    # Return the actual sales
    return jsonify({'Predicted sales': predicted_sales})

if __name__ == "__main__":
    sales_prediction_api.run(debug=True)

# Define an endpoint for batch prediction (POST request)
@sales_prediction_api.post("/v1/predict/batch")
def predict_sales_batch():
  """
  This function handles POST requests to the '/v1/predict/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 (get log_prices)
  predicted_log_sales = model.predict(input_data).tolist()

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

  # Return the predictions dictionary as a JSON response
  return output_dict

  if __name__ == '__main__':
      sales_prediction_api.run(debug=False, host='0.0.0.0', port=7860)