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import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import pycaret
import streamlit as st
from streamlit_option_menu import option_menu
import PIL
from PIL import Image
from PIL import ImageColor
from PIL import ImageDraw
from PIL import ImageFont
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
with st.sidebar:
image = Image.open('./itaca_logo.png')
st.image(image,use_column_width=True)
page = option_menu(menu_title='Menu',
menu_icon="robot",
options=["Clustering Analysis",
"Anomaly Detection"],
icons=["chat-dots",
"key"],
default_index=0
)
st.title('ITACA Insurance Core AI Module')
if page == "Clustering Analysis":
st.header('Clustering Analysis')
st.write(
"""
"""
)
# import pycaret unsupervised models
from pycaret.clustering import *
# import ClusteringExperiment
from pycaret.clustering import ClusteringExperiment
# Display the list of CSV files
directory = "./"
all_files = os.listdir(directory)
# Filter files to only include CSV files
csv_files = [file for file in all_files if file.endswith(".csv")]
# Select a CSV file from the list
selected_csv = st.selectbox("Select a CSV file from the list", ["None"] + csv_files)
# Upload the CSV file
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
# Define the unsupervised model
clusteringmodel = ['kmeans', 'ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics', 'birch']
selected_model = st.selectbox("Choose a clustering model", clusteringmodel)
# Define the options for the dropdown list
numclusters = [2, 3, 4, 5, 6]
# selected_clusters = st.selectbox("Choose a number of clusters", numclusters)
selected_clusters = st.slider("Choose a number of clusters", min_value=2, max_value=10, value=4)
# Read and display the CSV file
if selected_csv != "None" or uploaded_file is not None:
if uploaded_file:
try:
delimiter = ','
insurance_claims = pd.read_csv (uploaded_file, sep=delimiter)
except ValueError:
delimiter = '|'
insurance_claims = pd.read_csv (uploaded_file, sep=delimiter, encoding='latin-1')
else:
insurance_claims = pd.read_csv(selected_csv)
s = setup(insurance_claims, session_id = 123)
exp_clustering = ClusteringExperiment()
# init setup on exp
exp_clustering.setup(insurance_claims, session_id = 123)
if st.button("Prediction"):
with st.spinner("Analyzing..."):
# train kmeans model
cluster_model = create_model(selected_model, num_clusters = selected_clusters)
cluster_model_2 = assign_model(cluster_model)
cluster_model_2
all_metrics = get_metrics()
all_metrics
cluster_results = pull()
cluster_results
# plot pca cluster plot
plot_model(cluster_model, plot = 'cluster', display_format = 'streamlit')
if selected_model != 'ap':
plot_model(cluster_model, plot = 'tsne', display_format = 'streamlit')
if selected_model not in ('ap', 'meanshift', 'dbscan', 'optics'):
plot_model(cluster_model, plot = 'elbow', display_format = 'streamlit')
if selected_model not in ('ap', 'meanshift', 'sc', 'hclust', 'dbscan', 'optics'):
plot_model(cluster_model, plot = 'silhouette', display_format = 'streamlit')
if selected_model not in ('ap', 'sc', 'hclust', 'dbscan', 'optics', 'birch'):
plot_model(cluster_model, plot = 'distance', display_format = 'streamlit')
if selected_model != 'ap':
plot_model(cluster_model, plot = 'distribution', display_format = 'streamlit')
elif page == "Anomaly Detection":
st.header('Anomaly Detection')
st.write(
"""
"""
)
# import pycaret anomaly
from pycaret.anomaly import *
# import AnomalyExperiment
from pycaret.anomaly import AnomalyExperiment
# Display the list of CSV files
directory = "./"
all_files = os.listdir(directory)
# Filter files to only include CSV files
csv_files = [file for file in all_files if file.endswith(".csv")]
# Select a CSV file from the list
selected_csv = st.selectbox("Select a CSV file from the list", ["None"] + csv_files)
# Upload the CSV file
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
# Define the unsupervised model
anomalymodel = ['abod', 'cluster', 'cof', 'iforest', 'histogram', 'knn', 'lof', 'svm', 'pca', 'mcd', 'sod', 'sos']
selected_model = st.selectbox("Choose an anomaly model", anomalymodel)
# Read and display the CSV file
if selected_csv != "None" or uploaded_file is not None:
if uploaded_file:
try:
delimiter = ','
insurance_claims = pd.read_csv (uploaded_file, sep=delimiter)
except ValueError:
delimiter = '|'
insurance_claims = pd.read_csv (uploaded_file, sep=delimiter, encoding='latin-1')
else:
insurance_claims = pd.read_csv(selected_csv)
s = setup(insurance_claims, session_id = 123)
exp_anomaly = AnomalyExperiment()
# init setup on exp
exp_anomaly.setup(insurance_claims, session_id = 123)
if st.button("Prediction"):
with st.spinner("Analyzing..."):
# train model
anomaly_model = create_model(selected_model)
anomaly_model_2 = assign_model(anomaly_model)
anomaly_model_2
anomaly_results = pull()
anomaly_results
# plot
plot_model(anomaly_model, plot = 'tsne', display_format = 'streamlit')
plot_model(anomaly_model, plot = 'umap', display_format = 'streamlit')
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