# Import necessary libraries
import os
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
rental_price_predictor_api = Flask("Extraa Learn conversion Predictor")

# Load the trained machine learning model
# Use relative path to load the model inside backend_files
model_path = os.path.join(os.path.dirname(__file__), "conversion_prediction_model_v1_0.joblib")
model = joblib.load(model_path)

print("Model loaded successfully.")
# model = joblib.load(model_path)

# Define a route for the home page (GET request)
@rental_price_predictor_api.get('/')
def home():
    """
    This function handles GET requests to the root URL ('/') of the API.
    It returns a simple welcome message.
    """
    return "HI, Welcome to the Extraa Learn conversion Predictor API!"

# Define an endpoint for single property prediction (POST request)
@rental_price_predictor_api.post('/v1/conversion')
def predict_rental_price():
    property_data = request.get_json()

    sample = {
        'age': property_data['age'],
        'website_visits': property_data['website_visits'],
        'time_spent_on_website': property_data['time_spent_on_website'],
        'page_views_per_visit': property_data['page_views_per_visit'],
        'current_occupation': property_data['current_occupation'],
        'first_interaction': property_data['first_interaction'],
        'profile_completed': property_data['profile_completed'],
        'last_activity': property_data['last_activity'],
        'print_media_type1': property_data['print_media_type1'],
        'print_media_type2': property_data['print_media_type2'],
        'digital_media': property_data['digital_media'],
        'educational_channels': property_data['educational_channels'],
        'referral': property_data['referral']
    }

    input_data = pd.DataFrame([sample])

    # Directly predict class (0 or 1)
    predicted_status = int(model.predict(input_data)[0])

    return jsonify({'Predicted Status': predicted_status})


# Define an endpoint for batch prediction (POST request)
@rental_price_predictor_api.post('/v1/conversionbatch')
def predict_rental_price_batch():
    """
    This function handles POST requests to the '/v1/conversionbatch' 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)
    status_log = model.predict(input_data).tolist()

    # Calculate actual prices
    status = [round(float(np.exp(log_price)), 2) for log_price in status_log]

    # Create a dictionary of predictions with property IDs as keys
    property_ids = input_data['ID'].tolist()  # Assuming 'id' is the property ID column
    output_dict = dict(zip(property_ids, status))  # 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__':
    rental_price_predictor_api.run(debug=True)