app.py

import time 
import math

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import gradio as gr

from datasets import load_dataset
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

LOGS_DATASET_URI = 'pgurazada1/machine-failure-mlops-demo-logs'


def get_data():
    """
    Connect to the HuggingFace dataset where the logs are stored.
    Pull the data into a dataframe
    """
    data = load_dataset(LOGS_DATASET_URI)
    sample_df = data['train'].to_pandas().sample(100)

    return sample_df

def load_training_data():
    dataset = fetch_openml(data_id=42890, as_frame=True, parser="auto")
    data_df = dataset.data
    
    target = 'Machine failure'
    numeric_features = [
        'Air temperature [K]',
        'Process temperature [K]',
        'Rotational speed [rpm]',
        'Torque [Nm]',
        'Tool wear [min]'
    ]
    
    categorical_features = ['Type']

    X = data_df[numeric_features + categorical_features]
    y = data_df[target]

    Xtrain, Xtest, ytrain, ytest = train_test_split(
        X, y,
        test_size=0.2,
        random_state=42
    )

    return Xtrain, ytrain

def check_model_drift():
    sample_df = get_data()
    p_pos_label_training_data = 0.03475
    training_data_size = 8000
    
    p_0 = sample_df.prediction.value_counts()[0]
    p_1 = sample_df.prediction.value_counts()[1]

    p_pos_label_sample_logs = p_1/(p_0+p_1)
    
    variance = (p_pos_label_training_data * (1-p_pos_label_training_data))/training_data_size
    p_diff = abs(p_pos_label_training_data - p_pos_label_sample_logs)
    
    if p_diff > 2 * math.sqrt(variance):
        return "Model Drift Detected! Check logs!"
    else:
        return "No Model Drift!"

    
with gr.Blocks() as demo:
    gr.Markdown("# Real-time Monitoring Dashboard")

    gr.Markdown("Model drift detection (every 5 seconds)")
    
    with gr.Row():
        with gr.Column():
            gr.Textbox(check_model_drift, every=5, label="Model Drift Status")

demo.queue().launch()