import os
import pycaret
from pycaret.datasets import get_data
# import pycaret clustering
from pycaret.clustering import *
# import pycaret anomaly
from pycaret.anomaly import *
# import ClusteringExperiment
from pycaret.clustering import ClusteringExperiment
# import AnomalyExperiment
from pycaret.anomaly import AnomalyExperiment

import matplotlib.pyplot as plt
import matplotlib as mpl

import numpy as np
import streamlit as st
import plotly.graph_objs as go

# For measuring the inference time.
import time

def main():
    data = get_data('jewellery')
    s = setup(data, session_id = 123)

    exp_clustering = ClusteringExperiment()
    exp_anomaly = AnomalyExperiment()

    # init setup on exp
    exp_clustering.setup(data, session_id = 123)
    exp_anomaly.setup(data, session_id = 123)

    # train kmeans model
    kmeans = create_model('kmeans')
    iforest = create_model('iforest')

    # kmeans_cluster = assign_model(kmeans)
    # kmeans_cluster

    iforest_anomalies = assign_model(iforest)
    iforest_anomalies

    if st.button("Prediction"):
        # plot pca cluster plot 
        # plot_model(kmeans, plot = 'cluster', display_format = 'streamlit')
        plot_model(iforest, plot = 'tsne', display_format = 'streamlit')
               
if __name__ == '__main__':
    main()