Add Gradio app for phishing detection with URLScan.io integration

README.md

@@ -1,12 +1,50 @@
 ---
-title: Project Phising Detection
-emoji: 🏃
-colorFrom: yellow
-colorTo: indigo
+title: Phishing URL Detection
+emoji: 🛡️
+colorFrom: red
+colorTo: yellow
 sdk: gradio
-sdk_version: 6.2.0
+sdk_version: 5.0.0
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Phishing URL Detection
+
+A machine learning-powered phishing detection app that analyzes URLs using URLScan.io and predicts whether they are phishing or legitimate.
+
+## Features
+
+- **Real-time URL Analysis**: Submit any URL for instant phishing detection
+- **URLScan.io Integration**: Automatically scans URLs and extracts security features
+- **ML-Powered Predictions**: Uses trained models from Hopsworks Model Registry
+- **Detailed Results**: Shows confidence scores and extracted security features
+
+## How It Works
+
+1. Enter a URL in the web interface
+2. The app scans the URL using URLScan.io API
+3. Extracts security features (domain age, TLS certificate, secure requests, etc.)
+4. Runs inference using a trained machine learning model
+5. Displays prediction with confidence score and feature analysis
+
+## Model Information
+
+The model analyzes these security features:
+- **Domain Age**: How old the domain is (days)
+- **Secure Percentage**: Percentage of HTTPS requests
+- **Umbrella Rank**: Cisco Umbrella popularity ranking
+- **TLS Certificate**: Certificate validity period
+- **URL Length**: Length of the URL
+- **Subdomain Count**: Number of subdomains
+
+## Configuration
+
+This app requires the following environment variables (configured as Space secrets):
+- `HOPSWORKS_API_KEY`: Hopsworks API key for model loading
+- `HOPSWORKS_PROJECT`: Hopsworks project name
+- `URLSCAN_API_KEY`: URLScan.io API key for URL scanning
+
+## Disclaimer
+
+This tool is for educational and research purposes only. Predictions are not 100% accurate and should not be the sole basis for security decisions.

app.py

@@ -0,0 +1,221 @@
+"""
+Gradio app for phishing detection using URLScan.io and Hopsworks.
+
+This app:
+1. Loads a trained model from Hopsworks Model Registry
+2. Takes a URL as input
+3. Scans it using URLScan.io API
+4. Extracts features from the scan results
+5. Runs inference using the loaded model
+6. Returns whether the URL is likely phishing or legitimate
+"""
+
+import os
+import logging
+import gradio as gr
+from typing import Tuple
+
+from phising_detection.inference import PhishingDetectionPipeline
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Global inference pipeline
+pipeline = None
+
+
+def initialize_app():
+    """Initialize the app by loading the inference pipeline."""
+    global pipeline
+
+    try:
+        # Get URLScan API key
+        urlscan_api_key = os.getenv("URLSCAN_API_KEY")
+
+        # Initialize pipeline
+        pipeline = PhishingDetectionPipeline(
+            model_name="phishing_detector",
+            model_version=None,  # Use latest version
+            urlscan_api_key=urlscan_api_key
+        )
+
+        # Load model from Hopsworks
+        pipeline.load_model_from_hopsworks()
+        logger.info("Inference pipeline initialized successfully!")
+
+        return True
+    except Exception as e:
+        logger.error(f"Failed to initialize app: {e}")
+        return False
+
+
+def gradio_interface(url: str) -> Tuple[str, str, str]:
+    """
+    Gradio interface function.
+
+    Args:
+        url: URL to analyze
+
+    Returns:
+        Tuple of (result_html, confidence_html, details_html)
+    """
+    if not url or not url.strip():
+        return "Please enter a URL", "", ""
+
+    # Clean URL
+    url = url.strip()
+
+    # Add http:// if no protocol specified
+    if not url.startswith(('http://', 'https://')):
+        url = 'https://' + url
+
+    # Run prediction using pipeline
+    result = pipeline.predict_url(url)
+
+    # Check for errors
+    if "error" in result:
+        return (
+            f'<h2 style="color: orange;">ERROR</h2>',
+            "",
+            f"<p><strong>Error:</strong> {result['error']}</p>"
+        )
+
+    # Format result with color
+    prediction = result["prediction"]
+    confidence = result["confidence"]
+
+    if prediction == "PHISHING":
+        result_html = f'<h2 style="color: red;">PHISHING</h2>'
+        color = "red"
+    elif prediction == "LEGITIMATE":
+        result_html = f'<h2 style="color: green;">LEGITIMATE</h2>'
+        color = "green"
+    else:
+        result_html = f'<h2 style="color: orange;">UNKNOWN</h2>'
+        color = "orange"
+
+    confidence_html = f'<h3 style="color: {color};">Confidence: {confidence * 100:.2f}%</h3>'
+
+    # Format details
+    details_html = f"""
+    <h4>Prediction Details:</h4>
+    <ul>
+        <li><strong>Phishing Probability:</strong> {result['phishing_probability'] * 100:.2f}%</li>
+        <li><strong>Legitimate Probability:</strong> {result['legitimate_probability'] * 100:.2f}%</li>
+        <li><strong>URLScan UUID:</strong> {result.get('scan_uuid', 'N/A')}</li>
+    </ul>
+
+    <h4>Extracted Features:</h4>
+    <ul>
+        <li><strong>Domain Age (days):</strong> {result['features'].get('domain_age_days', 'N/A')}</li>
+        <li><strong>Secure Percentage:</strong> {result['features'].get('secure_percentage', 'N/A')}%</li>
+        <li><strong>Has Umbrella Rank:</strong> {'Yes' if result['features'].get('has_umbrella_rank') else 'No'}</li>
+        <li><strong>Umbrella Rank:</strong> {result['features'].get('umbrella_rank', 'N/A')}</li>
+        <li><strong>Has TLS:</strong> {'Yes' if result['features'].get('has_tls') else 'No'}</li>
+        <li><strong>TLS Valid Days:</strong> {result['features'].get('tls_valid_days', 'N/A')}</li>
+        <li><strong>URL Length:</strong> {result['features'].get('url_length', 'N/A')}</li>
+        <li><strong>Subdomain Count:</strong> {result['features'].get('subdomain_count', 'N/A')}</li>
+    </ul>
+    """
+
+    return result_html, confidence_html, details_html
+
+
+def create_gradio_app():
+    """Create and configure the Gradio interface."""
+
+    # Custom CSS for better styling
+    css = """
+    .output-box {
+        padding: 20px;
+        border-radius: 10px;
+        margin: 10px 0;
+    }
+    """
+
+    with gr.Blocks(css=css, title="Phishing URL Detection") as demo:
+        gr.Markdown(
+            """
+            # Phishing URL Detection
+
+            Enter a URL to check if it's a phishing website or legitimate.
+            This app uses URLScan.io to analyze the website and a machine learning model
+            trained on URLScan features to predict if it's phishing.
+
+            **Note:** Scanning a URL can take up to 90 seconds as we wait for URLScan.io to complete the analysis.
+            """
+        )
+
+        with gr.Row():
+            with gr.Column(scale=3):
+                url_input = gr.Textbox(
+                    label="URL to Check",
+                    placeholder="Enter URL (e.g., example.com or https://example.com)",
+                    lines=1
+                )
+            with gr.Column(scale=1):
+                submit_btn = gr.Button("Check URL", variant="primary", size="lg")
+
+        with gr.Row():
+            result_output = gr.HTML(label="Prediction")
+
+        with gr.Row():
+            confidence_output = gr.HTML(label="Confidence")
+
+        with gr.Row():
+            details_output = gr.HTML(label="Details")
+
+        # Example URLs
+        gr.Markdown("### Example URLs to Try:")
+        gr.Examples(
+            examples=[
+                ["https://google.com"],
+                ["https://github.com"],
+                ["https://facebook.com"],
+            ],
+            inputs=url_input,
+        )
+
+        # Connect button to function
+        submit_btn.click(
+            fn=gradio_interface,
+            inputs=url_input,
+            outputs=[result_output, confidence_output, details_output]
+        )
+
+        gr.Markdown(
+            """
+            ---
+            **Disclaimer:** This tool is for educational and research purposes only.
+            The predictions are not 100% accurate and should not be the sole basis for security decisions.
+            """
+        )
+
+    return demo
+
+
+def main():
+    """Main function to run the Gradio app."""
+    logger.info("Starting Phishing Detection App...")
+
+    # Initialize app (load model and URLScan client)
+    logger.info("Initializing app...")
+    if not initialize_app():
+        logger.error("Failed to initialize app. Exiting.")
+        return
+
+    # Create and launch Gradio app
+    logger.info("Creating Gradio interface...")
+    demo = create_gradio_app()
+
+    logger.info("Launching Gradio app...")
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,22 @@
+# Dependencies for Hugging Face Space - Phishing Detection App
+
+# Core app framework
+gradio>=5.0.0
+
+# ML and Data
+scikit-learn>=1.8.0
+pandas>=2.1.0,<2.2.0
+numpy>=1.24.0
+
+# Hopsworks integration
+hopsworks==4.2.*
+
+# URLScan integration
+requests>=2.32.5
+python-dotenv>=1.0.0
+
+# Model serialization
+joblib>=1.3.0
+
+# Additional dependencies
+pyarrow>=14.0.0