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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.express as px | |
| from sklearn.ensemble import IsolationForest | |
| from io import BytesIO | |
| from reportlab.lib.pagesizes import letter | |
| from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image, PageBreak | |
| from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle | |
| from reportlab.lib.units import inch | |
| from reportlab.lib import colors | |
| import pdfplumber | |
| import base64 | |
| import random | |
| import plotly.io as pio | |
| # Fix for Kaleido | |
| pio.kaleido.scope.mathjax = None | |
| # App Configuration | |
| st.set_page_config( | |
| page_title="WiFi Guardian 🛡️", | |
| page_icon="📶", | |
| layout="wide" | |
| ) | |
| # Custom CSS for a polished interface | |
| st.markdown(""" | |
| <style> | |
| .st-emotion-cache-1kyxreq { | |
| display: flex; | |
| flex-flow: wrap; | |
| gap: 2rem; | |
| } | |
| .reportview-container .main .block-container{ | |
| padding-top: 2rem; | |
| } | |
| .sidebar .sidebar-content { | |
| background: linear-gradient(180deg, #2e3b4e, #1a2639); | |
| } | |
| .stButton>button { | |
| width: 100%; | |
| margin: 5px 0; | |
| transition: all 0.3s; | |
| } | |
| .stButton>button:hover { | |
| transform: scale(1.05); | |
| } | |
| .summary-box { | |
| padding: 20px; | |
| border-radius: 10px; | |
| background-color: #2e3b4e; | |
| margin: 10px 0; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Motivational Quotes | |
| QUOTES = [ | |
| "🛡️ Cybersecurity is not a product, but a process!", | |
| "🔒 Better safe than hacked!", | |
| "📶 A secure network is a happy network!", | |
| "🤖 AI guards while you sleep!", | |
| "🚨 Detect before you regret!", | |
| "💻 Security is always worth the investment!", | |
| "🔍 Stay vigilant, stay secure!" | |
| ] | |
| def show_quote(): | |
| st.markdown(f"<h3 style='text-align: center; color: #4CAF50;'>{random.choice(QUOTES)}</h3>", | |
| unsafe_allow_html=True) | |
| # Main App Function | |
| def main(): | |
| # Initialize session state variables | |
| if 'current_step' not in st.session_state: | |
| st.session_state.current_step = 1 | |
| if 'file_uploaded' not in st.session_state: | |
| st.session_state.file_uploaded = False | |
| if 'df' not in st.session_state: | |
| st.session_state.df = None | |
| # Sidebar Navigation | |
| with st.sidebar: | |
| st.title("🔍 Navigation") | |
| st.markdown("---") | |
| if st.button("📤 1. Upload File", help="Upload your network logs"): | |
| st.session_state.current_step = 1 | |
| if st.button("📊 2. Data Visualization", disabled=not st.session_state.file_uploaded): | |
| st.session_state.current_step = 2 | |
| if st.button("📈 3. Statistics Analysis", disabled=not st.session_state.file_uploaded): | |
| st.session_state.current_step = 3 | |
| if st.button("📥 4. Download Report", disabled=not st.session_state.file_uploaded): | |
| st.session_state.current_step = 4 | |
| # Main Content Area | |
| if st.session_state.current_step == 1: | |
| upload_file_section() | |
| elif st.session_state.current_step == 2: | |
| visualization_section() | |
| elif st.session_state.current_step == 3: | |
| statistics_section() | |
| elif st.session_state.current_step == 4: | |
| download_section() | |
| def upload_file_section(): | |
| st.title("📤 Upload Network Logs") | |
| st.markdown("---") | |
| if not st.session_state.file_uploaded: | |
| show_quote() | |
| st.markdown(""" | |
| ### Welcome to WiFi Guardian! 🤖 | |
| **Protect your network with AI-powered anomaly detection** | |
| 1. Upload network logs 📤 | |
| 2. Visualize patterns 📊 | |
| 3. Generate reports 📄 | |
| """) | |
| uploaded_file = st.file_uploader( | |
| "Choose network logs (CSV/TXT/PDF)", | |
| type=["csv", "txt", "pdf"], | |
| label_visibility="collapsed" | |
| ) | |
| if uploaded_file: | |
| try: | |
| process_file(uploaded_file) | |
| st.session_state.file_uploaded = True | |
| st.success("✅ File processed successfully!") | |
| # Show file summary | |
| st.subheader("📋 Upload Summary") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Total Records", len(st.session_state.df)) | |
| with col2: | |
| anomalies = sum(st.session_state.df['anomaly'] == -1) | |
| st.metric("Anomalies Detected", f"{anomalies} ({anomalies/len(st.session_state.df)*100:.1f}%)") | |
| with col3: | |
| st.metric("Max Traffic", f"{st.session_state.df['traffic'].max():.2f} Mbps") | |
| except Exception as e: | |
| st.error(f"Error processing file: {str(e)}") | |
| def visualization_section(): | |
| st.title("📊 Data Visualization") | |
| st.markdown("---") | |
| # 2D Visualization | |
| st.subheader("2D Traffic Analysis 🌐") | |
| # Use 'timestamp' if available; if not, generate a dummy one | |
| df = st.session_state.df.copy() | |
| if 'timestamp' not in df.columns: | |
| df['timestamp'] = pd.date_range(start="2021-01-01", periods=len(df), freq="T") | |
| fig2d = px.scatter( | |
| df, | |
| x='timestamp', | |
| y='traffic', | |
| color='anomaly', | |
| color_discrete_map={-1: 'orange', 1: 'blue'}, | |
| title="2D Traffic Analysis" | |
| ) | |
| st.plotly_chart(fig2d, use_container_width=True) | |
| # 3D Visualization | |
| st.subheader("3D Network Health 🌍") | |
| fig3d = px.scatter_3d( | |
| df, | |
| x='latency', | |
| y='packet_loss', | |
| z='traffic', | |
| color='anomaly', | |
| color_discrete_map={-1: 'orange', 1: 'blue'}, | |
| title="3D Network Analysis" | |
| ) | |
| st.plotly_chart(fig3d, use_container_width=True) | |
| def statistics_section(): | |
| st.title("📈 Statistical Analysis") | |
| st.markdown("---") | |
| st.subheader("Data Summary 📝") | |
| st.dataframe(st.session_state.df.describe(), use_container_width=True) | |
| st.subheader("Anomaly Distribution 📊") | |
| anomaly_counts = st.session_state.df['anomaly'].value_counts() | |
| fig = px.pie( | |
| names=['Normal', 'Anomaly'], | |
| values=[anomaly_counts.get(1, 0), anomaly_counts.get(-1, 0)], | |
| hole=0.4, | |
| color_discrete_sequence=['blue', 'orange'], | |
| title="Anomaly Distribution" | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| def download_section(): | |
| st.title("📥 Download Report") | |
| st.markdown("---") | |
| if st.button("🖨️ Generate Full Report"): | |
| with st.spinner("Generating PDF report..."): | |
| generate_pdf_report() | |
| st.success("Report generated successfully!") | |
| if 'pdf_report' in st.session_state: | |
| st.markdown("---") | |
| b64 = base64.b64encode(st.session_state.pdf_report).decode() | |
| href = f'<a href="data:application/octet-stream;base64,{b64}" download="wifi_report.pdf">📥 Download Full Report</a>' | |
| st.markdown(href, unsafe_allow_html=True) | |
| def process_file(uploaded_file): | |
| try: | |
| # Process CSV files | |
| if uploaded_file.name.endswith('.csv'): | |
| df = pd.read_csv(uploaded_file) | |
| # Process TXT files | |
| elif uploaded_file.name.endswith('.txt'): | |
| lines = [line.decode().strip().split(',') for line in uploaded_file.readlines()] | |
| df = pd.DataFrame(lines[1:], columns=lines[0]) | |
| # Process PDF files using pdfplumber | |
| elif uploaded_file.name.endswith('.pdf'): | |
| with pdfplumber.open(uploaded_file) as pdf: | |
| text = '\n'.join([page.extract_text() for page in pdf.pages]) | |
| lines = [line.split(',') for line in text.split('\n') if line] | |
| df = pd.DataFrame(lines[1:], columns=lines[0]) | |
| else: | |
| raise ValueError("Unsupported file type.") | |
| # Ensure required numeric columns exist and convert them | |
| numeric_cols = ['traffic', 'latency', 'packet_loss'] | |
| for col in numeric_cols: | |
| if col not in df.columns: | |
| raise ValueError(f"Column '{col}' not found in data.") | |
| df[col] = pd.to_numeric(df[col], errors='coerce') | |
| # Run anomaly detection using IsolationForest with 40% contamination | |
| clf = IsolationForest(contamination=0.4, random_state=42) | |
| df['anomaly'] = clf.fit_predict(df[numeric_cols]) | |
| st.session_state.df = df | |
| except Exception as e: | |
| st.error(f"Error processing file: {str(e)}") | |
| raise | |
| def generate_pdf_report(): | |
| try: | |
| buffer = BytesIO() | |
| doc = SimpleDocTemplate(buffer, pagesize=letter) | |
| styles = getSampleStyleSheet() | |
| elements = [] | |
| # Custom Title Style | |
| title_style = ParagraphStyle( | |
| name='Title', | |
| parent=styles['Heading1'], | |
| fontSize=18, | |
| textColor=colors.darkblue, | |
| spaceAfter=14 | |
| ) | |
| # Add Title | |
| elements.append(Paragraph("WiFi Network Anomaly Detection", title_style)) | |
| elements.append(Spacer(1, 12)) | |
| # Add Summary Section | |
| elements.append(Paragraph("<b>Detection Summary:</b>", styles['Heading2'])) | |
| summary_text = f""" | |
| • Total Data Points: {len(st.session_state.df)}<br/> | |
| • Anomalies Detected: {sum(st.session_state.df['anomaly'] == -1)}<br/> | |
| • Maximum Traffic: {st.session_state.df['traffic'].max():.2f} Mbps<br/> | |
| • Average Latency: {st.session_state.df['latency'].mean():.2f} ms<br/> | |
| • Peak Packet Loss: {st.session_state.df['packet_loss'].max():.2f}%<br/> | |
| """ | |
| elements.append(Paragraph(summary_text, styles['BodyText'])) | |
| elements.append(PageBreak()) | |
| # Generate and embed plots in memory using BytesIO | |
| # 2D Plot | |
| df = st.session_state.df.copy() | |
| if 'timestamp' not in df.columns: | |
| df['timestamp'] = pd.date_range(start="2021-01-01", periods=len(df), freq="T") | |
| fig2d = px.scatter(df, x='timestamp', y='traffic', | |
| color='anomaly', title="2D Traffic Analysis", | |
| color_discrete_map={-1: 'orange', 1: 'blue'}) | |
| img_bytes_2d = fig2d.to_image(format="png", engine="kaleido") | |
| img2d_io = BytesIO(img_bytes_2d) | |
| # 3D Plot | |
| fig3d = px.scatter_3d(df, x='latency', y='packet_loss', | |
| z='traffic', color='anomaly', title="3D Network Analysis", | |
| color_discrete_map={-1: 'orange', 1: 'blue'}) | |
| img_bytes_3d = fig3d.to_image(format="png", engine="kaleido") | |
| img3d_io = BytesIO(img_bytes_3d) | |
| # Add 2D Plot | |
| elements.append(Paragraph("<b>2D Traffic Analysis</b>", styles['Heading2'])) | |
| elements.append(Image(img2d_io, width=6*inch, height=4*inch)) | |
| elements.append(Spacer(1, 12)) | |
| # Add 3D Plot | |
| elements.append(Paragraph("<b>3D Network Analysis</b>", styles['Heading2'])) | |
| elements.append(Image(img3d_io, width=6*inch, height=4*inch)) | |
| elements.append(PageBreak()) | |
| # Add Statistics Section | |
| elements.append(Paragraph("<b>Statistical Report</b>", styles['Heading1'])) | |
| stats = st.session_state.df.describe() | |
| for col in ['traffic', 'latency', 'packet_loss']: | |
| elements.append(Paragraph(f"<b>{col.capitalize()} Statistics:</b>", styles['Heading3'])) | |
| stats_text = f""" | |
| • Mean: {stats[col]['mean']:.2f}<br/> | |
| • Std Dev: {stats[col]['std']:.2f}<br/> | |
| • Min: {stats[col]['min']:.2f}<br/> | |
| • 25%: {stats[col]['25%']:.2f}<br/> | |
| • 50%: {stats[col]['50%']:.2f}<br/> | |
| • 75%: {stats[col]['75%']:.2f}<br/> | |
| • Max: {stats[col]['max']:.2f}<br/> | |
| """ | |
| elements.append(Paragraph(stats_text, styles['BodyText'])) | |
| elements.append(Spacer(1, 12)) | |
| doc.build(elements) | |
| st.session_state.pdf_report = buffer.getvalue() | |
| except Exception as e: | |
| st.error(f"Error generating report: {str(e)}") | |
| if __name__ == "__main__": | |
| main() | |