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

# Initialize Flask app with a name
sales_predictor_api = Flask("Superkart Sales Predictor")

# Load the trained sales prediction model
model = joblib.load("superkart_price_prediction_model_v1_0.joblib")

# Define a route for the home page
@sales_predictor_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 Price Prediction API!"

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

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

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


    # Calculate actual price
    predicted_price = model.predict(input_data)[0]

    # Convert predicted_price to Python float
    predicted_price = round(float(predicted_price), 2)
    
    # Return the actual price
    return jsonify({'Sales Prediction Price (in dollars)': predicted_price})


# Define an endpoint to predict sales for a batch of multiple products
@sales_predictor_api.post('/v1/salesbatch')
def predict_sales_price_batch():
    """
    This function handles POST requests to the '/v1/salesbatch' endpoint.
    It expects a CSV file containing saleskart details for multiple products
    and returns the predicted sales 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 products in the DataFrame 
    predicted_sales_prices = model.predict(input_data).tolist()

    # Calculate actual prices rounded to 2 DCM
    predicted_prices = [round(float(sales_price), 2) for sales_price in predicted_sales_prices]

    # Create a dictionary of predictions with Product IDs as keys
    product_ids = input_data['id'].tolist()  # Assuming 'id' is the property ID column
    output_dict = dict(zip(product_ids, predicted_prices))  # 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_predictor_api.run(debug=True)