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import streamlit as st
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest
from tensorflow.keras.models import load_model
from sklearn.preprocessing import MinMaxScaler
import joblib
st.set_page_config(page_title="Anomaly Detection", layout="wide")
st.title("🚨 Network Traffic Anomaly Detection")
st.markdown("Upload network traffic data (with same structure as training data) and select a model to detect anomalies.")
# Upload CSV
uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
# Load models
iso_model = joblib.load("iso_model.pkl")
ae_model = load_model("autoencoder_model.h5")
scaler = joblib.load("scaler.pkl")
# Select model
model_choice = st.selectbox("Choose a model", ["Isolation Forest", "Autoencoder", "Hybrid"])
if uploaded_file is not None:
df = pd.read_csv(uploaded_file)
X = scaler.transform(df) # assumes same features
# Isolation Forest
iso_pred = iso_model.predict(X)
iso_pred = np.where(iso_pred == 1, 0, 1)
# Autoencoder
ae_recon = ae_model.predict(X)
ae_mse = np.mean(np.power(X - ae_recon, 2), axis=1)
ae_pred = np.where(ae_mse > 0.0096, 1, 0) # tuned threshold
# Hybrid
union_pred = np.logical_or(iso_pred, ae_pred).astype(int)
# Choose output
if model_choice == "Isolation Forest":
final_pred = iso_pred
elif model_choice == "Autoencoder":
final_pred = ae_pred
else:
final_pred = union_pred
df['anomaly'] = final_pred
st.write(df.head())
st.subheader("📊 Anomaly Distribution")
st.bar_chart(df['anomaly'].value_counts())
# Download predictions
csv = df.to_csv(index=False).encode()
st.download_button("Download Results", csv, "predictions.csv", "text/csv")
st.success("✅ Prediction complete!")
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