| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.datasets import load_breast_cancer | |
| from sklearn.tree import DecisionTreeClassifier | |
| theme = gr.themes.Monochrome( | |
| primary_hue="indigo", | |
| secondary_hue="blue", | |
| neutral_hue="slate", | |
| ) | |
| description = f""" | |
| ## Description | |
| This demo can be used to evaluate the ability of k-means initializations strategies to make the algorithm convergence robust | |
| """ | |
| with gr.Blocks(theme=theme) as demo: | |
| gr.Markdown(''' | |
| <div> | |
| <h1 style='text-align: center'>Empirical evaluation of the impact of k-means initialization 📊</h1> | |
| </div> | |
| ''') | |
| gr.Markdown(description) | |
| demo.launch() |