Add Gradio app and inference pipeline for phishing detection

HF_app/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()

HF_app/pyproject.toml

@@ -0,0 +1,16 @@
+[project]
+name = "hf-app"
+version = "0.1.0"
+description = "Hugging Face Gradio app for phishing detection using URLScan.io and Hopsworks"
+readme = "README.md"
+requires-python = ">=3.12, <3.14"
+dependencies = [
+    "phising-detection",
+    "gradio>=5.0.0",
+    "hopsworks==4.2.*",
+    "python-dotenv>=1.0.0",
+    "joblib>=1.3.0",
+]
+
+[tool.uv.sources]
+phising-detection = { workspace = true }

pyproject.toml

@@ -23,3 +23,8 @@ dev = [
     "responses>=0.25.8",
 ]
 
+[tool.uv.workspace]
+members = [
+    "HF_app",
+]
+

src/phising_detection/features/batch_url_scanner.py

@@ -16,9 +16,9 @@ import argparse
 from typing import List, Dict, Any
 import pandas as pd
 
-from src.phising_detection.features.urlscan_features import extract_features_to_dataframe
-from src.phising_detection.utils.urlscan import URLScanClient, URLScanError
-import src.phising_detection.utils.hopsworks_utils as hw
+from phising_detection.features.urlscan_features import extract_features_to_dataframe
+from phising_detection.utils.urlscan import URLScanClient, URLScanError
+import phising_detection.utils.hopsworks_utils as hw
 
 # Add src folder to path
 

src/phising_detection/inference/__init__.py

@@ -0,0 +1,9 @@
+"""
+Inference pipeline for phishing detection.
+
+This module provides inference capabilities for the trained phishing detection models.
+"""
+
+from .pipeline import PhishingDetectionPipeline
+
+__all__ = ['PhishingDetectionPipeline']

src/phising_detection/inference/pipeline.py

@@ -0,0 +1,250 @@
+"""
+Phishing detection inference pipeline.
+
+This module provides a complete inference pipeline for phishing detection including:
+- Model loading from Hopsworks Model Registry
+- Feature preprocessing
+- Prediction with confidence scores
+- Optional end-to-end URL scanning and prediction
+"""
+
+import os
+import logging
+from typing import Tuple, Optional, Dict, Any
+import pandas as pd
+import joblib
+
+from phising_detection.utils.hopsworks_utils import connect_to_hopsworks
+from phising_detection.models.model_utils.data_prep import FEATURE_COLUMNS, CONTINUOUS_FEATURES
+from phising_detection.features.urlscan_features import extract_features
+from phising_detection.utils.urlscan import URLScanClient
+
+logger = logging.getLogger(__name__)
+
+
+class PhishingDetectionPipeline:
+    """
+    End-to-end inference pipeline for phishing detection.
+
+    This class handles:
+    - Loading trained models from Hopsworks
+    - Preprocessing extracted features
+    - Running inference
+    - Optionally scanning URLs with URLScan.io
+    """
+
+    def __init__(
+        self,
+        model_name: str = "phishing_detector",
+        model_version: Optional[int] = None,
+        urlscan_api_key: Optional[str] = None
+    ):
+        """
+        Initialize the inference pipeline.
+
+        Args:
+            model_name: Name of the model in Hopsworks Model Registry
+            model_version: Specific version to load (None = latest)
+            urlscan_api_key: URLScan.io API key (optional, only needed for URL scanning)
+        """
+        self.model_name = model_name
+        self.model_version = model_version
+        self.model = None
+        self.scaler = None
+        self.feature_names = None
+        self.urlscan_client = None
+
+        if urlscan_api_key:
+            self.urlscan_client = URLScanClient(api_key=urlscan_api_key)
+
+    def load_model_from_hopsworks(self) -> None:
+        """
+        Load model, scaler, and feature names from Hopsworks Model Registry.
+
+        Raises:
+            Exception: If model loading fails
+        """
+        logger.info(f"Loading model '{self.model_name}' from Hopsworks Model Registry...")
+
+        # Connect to Hopsworks
+        project = connect_to_hopsworks()
+        mr = project.get_model_registry()
+
+        # Get model from registry
+        if self.model_version:
+            model_registry = mr.get_model(self.model_name, version=self.model_version)
+        else:
+            model_registry = mr.get_model(self.model_name)
+
+        logger.info(f"Found model: {self.model_name} version {model_registry.version}")
+
+        # Download model artifacts to a temporary directory
+        model_dir = model_registry.download()
+        logger.info(f"Model artifacts downloaded to: {model_dir}")
+
+        # Load model
+        model_path = os.path.join(model_dir, "model.pkl")
+        self.model = joblib.load(model_path)
+        logger.info(f"Model loaded from {model_path}")
+
+        # Load scaler
+        scaler_path = os.path.join(model_dir, "scaler.pkl")
+        self.scaler = joblib.load(scaler_path)
+        logger.info(f"Scaler loaded from {scaler_path}")
+
+        # Load feature names
+        feature_names_path = os.path.join(model_dir, "feature_names.txt")
+        with open(feature_names_path, 'r') as f:
+            self.feature_names = [line.strip() for line in f.readlines()]
+        logger.info(f"Feature names loaded: {self.feature_names}")
+
+        logger.info("Model pipeline initialized successfully!")
+
+    def preprocess_features(self, features_dict: Dict[str, Any]) -> pd.DataFrame:
+        """
+        Preprocess extracted features to match model input format.
+
+        Args:
+            features_dict: Dictionary of extracted features
+
+        Returns:
+            Preprocessed DataFrame ready for model inference
+
+        Raises:
+            ValueError: If model is not loaded
+        """
+        if self.model is None or self.scaler is None or self.feature_names is None:
+            raise ValueError("Model not loaded. Call load_model_from_hopsworks() first.")
+
+        # Create DataFrame with a single row
+        df = pd.DataFrame([features_dict])
+
+        # Ensure all required features are present
+        for feature in self.feature_names:
+            if feature not in df.columns:
+                logger.warning(f"Feature '{feature}' missing, filling with default value")
+                df[feature] = 0
+
+        # Select and order features to match training
+        df = df[self.feature_names]
+
+        # Handle missing values (fill with median approximation)
+        df = df.fillna(df.median())
+
+        # Apply scaling to continuous features
+        df_scaled = df.copy()
+        df_scaled[CONTINUOUS_FEATURES] = self.scaler.transform(df[CONTINUOUS_FEATURES])
+
+        return df_scaled
+
+    def predict(self, features_dict: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Run inference on extracted features.
+
+        Args:
+            features_dict: Dictionary of extracted features
+
+        Returns:
+            Dictionary containing prediction results:
+            - prediction: "PHISHING" or "LEGITIMATE"
+            - confidence: Confidence score (0-1)
+            - phishing_probability: Probability of phishing (0-1)
+            - legitimate_probability: Probability of legitimate (0-1)
+            - is_phishing: Boolean flag
+
+        Raises:
+            ValueError: If model is not loaded
+        """
+        if self.model is None:
+            raise ValueError("Model not loaded. Call load_model_from_hopsworks() first.")
+
+        # Preprocess features
+        X = self.preprocess_features(features_dict)
+
+        # Run inference
+        logger.info("Running model inference...")
+        prediction_proba = self.model.predict_proba(X)[0]
+        prediction = self.model.predict(X)[0]
+
+        # prediction: 0 = legitimate, 1 = phishing
+        is_phishing = bool(prediction)
+        confidence = prediction_proba[1] if is_phishing else prediction_proba[0]
+
+        result = {
+            "prediction": "PHISHING" if is_phishing else "LEGITIMATE",
+            "confidence": float(confidence),
+            "phishing_probability": float(prediction_proba[1]),
+            "legitimate_probability": float(prediction_proba[0]),
+            "is_phishing": is_phishing
+        }
+
+        logger.info(f"Prediction: {result['prediction']} (confidence: {result['confidence']:.4f})")
+
+        return result
+
+    def predict_url(self, url: str) -> Dict[str, Any]:
+        """
+        End-to-end prediction: scan URL and predict if it's phishing.
+
+        Args:
+            url: URL to analyze
+
+        Returns:
+            Dictionary containing:
+            - prediction: "PHISHING" or "LEGITIMATE"
+            - confidence: Confidence score (0-1)
+            - phishing_probability: Probability of phishing (0-1)
+            - legitimate_probability: Probability of legitimate (0-1)
+            - is_phishing: Boolean flag
+            - features: Dictionary of extracted features
+            - scan_uuid: URLScan UUID (if available)
+            - error: Error message (if any)
+
+        Raises:
+            ValueError: If URLScan client is not initialized
+        """
+        if self.urlscan_client is None:
+            raise ValueError("URLScan client not initialized. Provide urlscan_api_key in constructor.")
+
+        try:
+            # Step 1: Submit URL to URLScan.io and wait for results
+            logger.info(f"Scanning URL: {url}")
+            scan_result = self.urlscan_client.submit_and_wait(url)
+
+            if not scan_result:
+                return {
+                    "error": "Failed to get scan results from URLScan.io",
+                    "prediction": "ERROR",
+                    "confidence": 0.0
+                }
+
+            # Step 2: Extract features from scan results
+            logger.info("Extracting features from scan results...")
+            features = extract_features(scan_result)
+            logger.info(f"Extracted features: {features}")
+
+            # Step 3: Run inference
+            prediction_result = self.predict(features)
+
+            # Add additional information
+            prediction_result["features"] = features
+            prediction_result["scan_uuid"] = scan_result.get("task", {}).get("uuid", "N/A")
+
+            return prediction_result
+
+        except Exception as e:
+            logger.error(f"Error during prediction: {e}", exc_info=True)
+            return {
+                "error": str(e),
+                "prediction": "ERROR",
+                "confidence": 0.0
+            }
+
+    def is_loaded(self) -> bool:
+        """
+        Check if the model is loaded and ready for inference.
+
+        Returns:
+            True if model is loaded, False otherwise
+        """
+        return self.model is not None and self.scaler is not None and self.feature_names is not None

src/phising_detection/models/model_selection.py

@@ -9,8 +9,8 @@ from typing import Tuple, Any
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
 
-from src.phising_detection.models import data_prep, model_configs, evaluation
-from src.phising_detection.utils.hopsworks_utils import connect_to_hopsworks
+from phising_detection.models import data_prep, model_configs, evaluation
+from phising_detection.utils.hopsworks_utils import connect_to_hopsworks
 
 logger = logging.getLogger(__name__)
 

src/phising_detection/models/model_utils/data_prep.py

@@ -13,7 +13,7 @@ import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 
-from src.phising_detection.utils import hopsworks_utils
+from phising_detection.utils import hopsworks_utils
 
 logger = logging.getLogger(__name__)
 

src/phising_detection/models/model_utils/train_pipeline.py

@@ -9,10 +9,10 @@ import os
 import logging
 
 
-from src.phising_detection.utils import hopsworks_utils as hw
-from src.phising_detection.models import data_prep
-from src.phising_detection.models import model_configs
-from src.phising_detection.models import evaluation
+from phising_detection.utils import hopsworks_utils as hw
+from phising_detection.models import data_prep
+from phising_detection.models import model_configs
+from phising_detection.models import evaluation
 
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

src/phising_detection/models/train_final_model.py

@@ -15,8 +15,8 @@ import argparse
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
 
-from src.phising_detection.models import data_prep, model_configs, evaluation
-from src.phising_detection.utils import hopsworks_utils as hw
+from phising_detection.models import data_prep, model_configs, evaluation
+from phising_detection.utils import hopsworks_utils as hw
 
 # Configure logging
 logging.basicConfig(

uv.lock

@@ -1,6 +1,96 @@
 version = 1
 revision = 3
 requires-python = ">=3.12, <3.14"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version < '3.13'",
+]
+
+[manifest]
+members = [
+    "hf-app",
+    "phising-detection",
+]
+
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
+wheels = [
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+]
+
+[[package]]
+name = "anyio"
+version = "4.12.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
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