Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from sklearn.preprocessing import MinMaxScaler
+import plotly.express as px
+import os
+from groq import Groq
+
+# Initialize session state
+if 'model' not in st.session_state:
+    st.session_state.model = None
+if 'threshold' not in st.session_state:
+    st.session_state.threshold = None
+
+# Autoencoder model definition
+def build_autoencoder(input_dim):
+    model = tf.keras.Sequential([
+        tf.keras.layers.Dense(64, activation='relu', input_shape=(input_dim,)),
+        tf.keras.layers.Dense(32, activation='relu'),
+        tf.keras.layers.Dense(64, activation='relu'),
+        tf.keras.layers.Dense(input_dim)
+    ])
+    model.compile(optimizer='adam', loss='mse')
+    return model
+
+# Sidebar controls
+st.sidebar.title("Configuration")
+fine_tune = st.sidebar.button("Fine-tune Model")
+groq_api_key = st.sidebar.text_input("Groq API Key (optional)", type="password")
+
+# Main interface
+st.title("🛰️ AI Network Anomaly Detector")
+st.write("Upload your network data (CSV) to detect anomalies")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+# Load or generate sample data
+if uploaded_file is not None:
+    data = pd.read_csv(uploaded_file)
+else:
+    st.info("Using sample data. Upload a file to use your own dataset.")
+    data = pd.read_csv("sample_wifi_data.csv")  # You should provide this sample file
+
+# Preprocessing
+features = ['device_count', 'connection_attempts', 'packet_loss', 'latency']
+scaler = MinMaxScaler()
+data_scaled = scaler.fit_transform(data[features])
+
+# Model training/fine-tuning
+if fine_tune or st.session_state.model is None:
+    with st.spinner("Training model..."):
+        autoencoder = build_autoencoder(data_scaled.shape[1])
+        autoencoder.fit(data_scaled, data_scaled, 
+                       epochs=100, 
+                       batch_size=32,
+                       verbose=0,
+                       validation_split=0.1)
+        st.session_state.model = autoencoder
+        
+        # Calculate threshold
+        reconstructions = autoencoder.predict(data_scaled)
+        mse = np.mean(np.power(data_scaled - reconstructions, 2), axis=1)
+        st.session_state.threshold = np.percentile(mse, 95)
+
+# Anomaly detection
+if st.session_state.model and st.button("Detect Anomalies"):
+    reconstructions = st.session_state.model.predict(data_scaled)
+    mse = np.mean(np.power(data_scaled - reconstructions, 2), axis=1)
+    anomalies = mse > st.session_state.threshold
+    
+    # Visualization
+    fig = px.line(data, x=data.index, y='connection_attempts', 
+                 title='Network Traffic with Anomalies')
+    fig.add_scatter(x=data[anomalies].index, y=data[anomalies]['connection_attempts'], 
+                   mode='markers', name='Anomalies')
+    st.plotly_chart(fig)
+    
+    # Generate alert with Groq/Llama3
+    if groq_api_key:
+        try:
+            client = Groq(api_key=groq_api_key)
+            response = client.chat.completions.create(
+                model="llama3-70b-8192",
+                messages=[{
+                    "role": "user",
+                    "content": f"Generate a network security alert for {sum(anomalies)} anomalies detected. Max connection attempts: {data['connection_attempts'].max()}"
+                }]
+            )
+            st.warning(response.choices[0].message.content)
+        except Exception as e:
+            st.error(f"Groq API Error: {str(e)}")
+    else:
+        st.warning(f"Detected {sum(anomalies)} anomalies! Consider adding Groq API key for detailed analysis.")
+
+# Download button for results
+if st.session_state.threshold:
+    st.download_button(
+        label="Download Anomaly Report",
+        data=data[anomalies].to_csv().encode('utf-8'),
+        file_name='anomalies_report.csv',
+        mime='text/csv'
+    )