Update app.py

app.py

@@ -1,160 +1,30 @@
-import os
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-import pycaret
+# app.py
 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
-
-    # 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 uploaded_file is not None:
-        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')
-        
-        s = setup(insurance_claims, session_id = 123, log_experiment='mlflow', experiment_name='fraud_detection')
-
-        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
-
-        # 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)
+import mlflow
+import pandas as pd
 
-    # Read and display the CSV file
-    if uploaded_file is not None:
-        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')
-        
-        s = setup(insurance_claims, session_id = 123)
+# Set the MLflow tracking URI (e.g., local file system or a remote server)
+mlflow.set_tracking_uri("http://localhost:5000")
 
-        exp_anomaly = AnomalyExperiment()
+def main():
+    st.title("Streamlit and MLflow Integration")
 
-        # init setup on exp
-        exp_anomaly.setup(insurance_claims, session_id = 123)
+    # Retrieve the list of experiments
+    experiments = mlflow.list_experiments()
+    experiment_names = [exp.name for exp in experiments]
+    experiment_id_map = {exp.name: exp.experiment_id for exp in experiments}
 
-        if st.button("Prediction"):
-            with st.spinner("Analyzing..."):
-                # train model
-                anomaly_model = create_model(selected_model)
+    # Create a dropdown menu to select an experiment
+    selected_experiment = st.selectbox("Select an experiment:", experiment_names)
 
-                anomaly_model_2 = assign_model(anomaly_model)
-                anomaly_model_2
+    # Get the runs for the selected experiment
+    if selected_experiment:
+        experiment_id = experiment_id_map[selected_experiment]
+        runs = mlflow.search_runs(experiment_ids=[experiment_id])
 
-                anomaly_results = pull()
-                anomaly_results
+        # Display the runs in a table
+        st.write("Runs for the selected experiment:")
+        st.write(runs)
 
-                # plot
-                plot_model(anomaly_model, plot = 'tsne', display_format = 'streamlit')
-                plot_model(anomaly_model, plot = 'umap', display_format = 'streamlit')
+if __name__ == "__main__":
+    main()