Specify Python 3.12 for HF Space to match scikit-learn>=1.8.0 requirement

HF_app/app.py

@@ -1,221 +0,0 @@
-"""
-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

@@ -1,16 +0,0 @@
-[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 }

README.md

@@ -7,6 +7,7 @@ sdk: gradio
 sdk_version: 5.0.0
 app_file: app.py
 pinned: false
+python_version: 3.12
 ---
 
 # Phishing URL Detection

requirements.txt

@@ -1,12 +1,12 @@
 # Dependencies for Hugging Face Space - Phishing Detection App
+# Matches pyproject.toml but only includes inference dependencies
 
 # Core app framework
 gradio>=5.0.0
 
-# ML and Data
+# ML and Data (matching pyproject.toml versions)
 scikit-learn>=1.8.0
 pandas>=2.1.0,<2.2.0
-numpy>=1.24.0
 
 # Hopsworks integration
 hopsworks==4.2.*
@@ -15,8 +15,8 @@ hopsworks==4.2.*
 requests>=2.32.5
 python-dotenv>=1.0.0
 
-# Model serialization
+# Model serialization (included in scikit-learn but explicit for clarity)
 joblib>=1.3.0
 
-# Additional dependencies
+# Additional dependencies (from pyproject.toml)
 pyarrow>=14.0.0