app.py

import os

import pandas as pd
from sklearn.ensemble import IsolationForest

import numpy as np
from sklearn.model_selection import train_test_split
import gradio as gr
import matplotlib.pyplot as plt
from skops import hub_utils
import pickle
import time



#Data preparation
n_samples, n_outliers = 120, 40
rng = np.random.RandomState(0)
covariance = np.array([[0.5, -0.1], [0.7, 0.4]])
cluster_1 = 0.4 * rng.randn(n_samples, 2) @ covariance + np.array([2, 2])  # general deformed cluster
cluster_2 = 0.3 * rng.randn(n_samples, 2) + np.array([-2, -2])  # spherical cluster
outliers = rng.uniform(low=-4, high=4, size=(n_outliers, 2))

X = np.concatenate([cluster_1, cluster_2, outliers]) #120+120+40 = 280 with 2D
y = np.concatenate(
    [np.ones((2 * n_samples), dtype=int), -np.ones((n_outliers), dtype=int)]
)

def load_hf_model_hub():
    '''
    Load the directory containing pretrained model  
    and files from the model repository
    '''
    repo_id="sklearn-docs/anomaly-detection"
    download_repo = "downloaded-model"
    hub_utils.download(repo_id=repo_id, dst=download_repo)
    time.sleep(2)
    loaded_model = pickle.load(open('./downloaded-model/isolation_forest.pkl', 'rb'))
    return loaded_model

#Visualize the data as a scatter plot

def visualize_input_data():
    fig = plt.figure(1, facecolor="w", figsize=(5, 5))
    scatter = plt.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
    handles, labels = scatter.legend_elements()
    plt.axis("square")
    plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
    plt.title("Gaussian inliers with \nuniformly distributed outliers")
    # plt.show()
    # plt.clear()
    return fig






title = " An example using IsolationForest for anomaly detection."
description1 = "The isolation forest is an Ensemble of Isolation trees and it isolates the datapoints using recursive random partitioning."
description2 = "In case of outliers the number of splits required is greater than those required for inliers."
description3 = "We will use the toy dataset as given in the scikit-learn page for Isolation Forest."

with gr.Blocks(title=title) as demo:
    
    gr.Markdown(f"# {title}")
    gr.Markdown(f"# {description1}")
    gr.Markdown(f"# {description2}")
    gr.Markdown(f"# {description3}")

    gr.Markdown(" **https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py**")
    
    loaded_model = load_hf_model_hub()

    with gr.Tab("Visualize Input dataset"):
        btn = gr.Button(value="Visualize input dataset")
        btn.click(visualize_input_data, outputs= gr.Plot(label='Visualizing input dataset') )

    with gr.Tab("Plot Decision Boundary"):
        image_decision = gr.Image('./downloaded-model/decision_boundary.png')
    
    with gr.Tab("Plot Path"):
        image_path = gr.Image('./downloaded-model/plot_path.png')

    
    gr.Markdown( f"## Success")
demo.launch()