Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest

# Page title
st.title("Anomaly Detection App")

# Data generation
st.header("Generate Data")
num_samples = st.sidebar.slider("Number of samples:", 100, 1000, 500)
random_state = st.sidebar.slider("Random State:", 0, 100, 42)

np.random.seed(random_state)
X = np.random.randn(num_samples, 2) * 0.5 + np.array([[0], [0]])
# Adding some anomalies
a = np.random.uniform(low=-3, high=3, size=(50, 2))
X = np.concatenate((X, a), axis=0)

# Visualizing generated data
st.subheader("Generated Data")
plt.scatter(X[:, 0], X[:, 1], color='blue')
plt.title("Generated Data Points")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.grid()
st.pyplot(plt)

# Anomaly detection
st.header("Anomaly Detection")
clf = IsolationForest(contamination=0.1, random_state=random_state)
clf.fit(X)

# Predict anomalies
predictions = clf.predict(X)
# Anomalies are labeled as -1
anomalies = X[predictions == -1]

# Visualize results
st.subheader("Anomaly Detection Results")
plt.scatter(X[:, 0], X[:, 1], color='blue', label='Sample Data')
plt.scatter(anomalies[:, 0], anomalies[:, 1], color='red', label='Anomalies')
plt.title("Detected Anomalies")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.legend()
plt.grid()
st.pyplot(plt)