Brett Fox
22fe08a1a6
Features: - Created download_pretrained_model.py for downloading models from URLs - Created setup_real_model.py for integrating pre-trained LightGBM models - Generated MODEL_INFO.md with model usage documentation - Created COMPLETION_ASSESSMENT.md with comprehensive project evaluation - Framework complete: all 16 phases implemented, 27/30 tests passing - Model integration ready: tools to download/setup real LightGBM models - Clear path to production: real model, Gmail OAuth, and deployment ready This enables: 1. Immediate real model integration without code changes 2. Clear path from mock framework testing to production 3. Support for both downloaded and self-trained models 4. Documented deployment process for 80k+ email processing Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
265 lines
8.3 KiB
Python
265 lines
8.3 KiB
Python
"""Download and integrate pre-trained LightGBM model for email classification.
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This script can:
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1. Download a pre-trained LightGBM model from an online source (e.g., GitHub releases, S3)
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2. Validate the model format and compatibility
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3. Replace the mock model with the real model
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4. Update configuration to use the real model
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"""
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import logging
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import json
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import hashlib
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from pathlib import Path
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from typing import Optional, Dict, Any
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import pickle
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import urllib.request
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import sys
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logger = logging.getLogger(__name__)
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class ModelDownloader:
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"""Download and integrate pre-trained models."""
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def __init__(self, project_root: Optional[Path] = None):
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"""Initialize downloader.
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Args:
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project_root: Path to email-sorter project root
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"""
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self.project_root = project_root or Path(__file__).parent.parent
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self.models_dir = self.project_root / "models"
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self.models_dir.mkdir(exist_ok=True)
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def download_model(
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self,
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url: str,
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filename: str = "lightgbm_real.pkl",
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expected_hash: Optional[str] = None
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) -> bool:
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"""Download model from URL.
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Args:
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url: URL to download model from
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filename: Local filename to save
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expected_hash: Optional SHA256 hash to verify
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Returns:
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True if successful
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"""
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filepath = self.models_dir / filename
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logger.info(f"Downloading model from {url}...")
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try:
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urllib.request.urlretrieve(url, filepath)
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logger.info(f"Downloaded to {filepath}")
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# Verify hash if provided
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if expected_hash:
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file_hash = self._compute_hash(filepath)
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if file_hash != expected_hash:
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logger.error(f"Hash mismatch! Expected {expected_hash}, got {file_hash}")
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filepath.unlink()
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return False
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logger.info("Hash verification passed")
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return True
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except Exception as e:
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logger.error(f"Download failed: {e}")
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return False
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def load_model(self, filename: str = "lightgbm_real.pkl") -> Optional[Any]:
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"""Load model from disk.
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Args:
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filename: Model filename
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Returns:
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Model object or None if failed
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"""
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filepath = self.models_dir / filename
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if not filepath.exists():
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logger.error(f"Model not found: {filepath}")
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return None
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try:
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with open(filepath, 'rb') as f:
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model = pickle.load(f)
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logger.info(f"Loaded model from {filepath}")
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return model
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except Exception as e:
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logger.error(f"Failed to load model: {e}")
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return None
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def validate_model(self, model: Any) -> bool:
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"""Validate model structure.
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Args:
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model: Model object to validate
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Returns:
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True if valid LightGBM model
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"""
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try:
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# Check for LightGBM model methods
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required_methods = ['predict', 'predict_proba', 'get_params', 'set_params']
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for method in required_methods:
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if not hasattr(model, method):
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logger.error(f"Model missing method: {method}")
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return False
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logger.info("Model validation passed")
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return True
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except Exception as e:
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logger.error(f"Model validation failed: {e}")
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return False
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def configure_model_usage(self, use_real_model: bool = True) -> bool:
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"""Update configuration to use real model.
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Args:
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use_real_model: True to use real model, False for mock
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Returns:
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True if successful
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"""
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config_file = self.project_root / "config" / "model_config.json"
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config = {
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'use_real_model': use_real_model,
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'model_path': str(self.models_dir / "lightgbm_real.pkl"),
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'fallback_to_mock': True,
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'mock_warning': 'MOCK MODEL - Framework testing ONLY. Not for production use.'
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}
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try:
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config_file.parent.mkdir(parents=True, exist_ok=True)
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with open(config_file, 'w') as f:
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json.dump(config, f, indent=2)
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logger.info(f"Configuration updated: {config_file}")
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return True
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except Exception as e:
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logger.error(f"Failed to update configuration: {e}")
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return False
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def _compute_hash(self, filepath: Path) -> str:
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"""Compute SHA256 hash of file."""
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sha256 = hashlib.sha256()
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with open(filepath, 'rb') as f:
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for chunk in iter(lambda: f.read(4096), b''):
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sha256.update(chunk)
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return sha256.hexdigest()
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def get_model_info(self) -> Dict[str, Any]:
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"""Get information about available models.
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Returns:
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Dict with model info
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"""
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real_model_path = self.models_dir / "lightgbm_real.pkl"
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mock_model_path = self.models_dir / "lightgbm_mock.pkl"
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info = {
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'models_directory': str(self.models_dir),
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'real_model_available': real_model_path.exists(),
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'real_model_path': str(real_model_path) if real_model_path.exists() else None,
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'real_model_size': f"{real_model_path.stat().st_size / 1024 / 1024:.2f} MB" if real_model_path.exists() else None,
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'mock_model_available': mock_model_path.exists(),
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'mock_model_path': str(mock_model_path) if mock_model_path.exists() else None,
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}
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return info
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def main():
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"""Command-line interface."""
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import argparse
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parser = argparse.ArgumentParser(description="Download and integrate pre-trained LightGBM model")
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parser.add_argument('--url', help='URL to download model from')
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parser.add_argument('--hash', help='Expected SHA256 hash of model file')
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parser.add_argument('--load', action='store_true', help='Load and validate existing model')
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parser.add_argument('--info', action='store_true', help='Show model information')
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parser.add_argument('--enable', action='store_true', help='Enable real model usage')
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parser.add_argument('--disable', action='store_true', help='Disable real model usage (use mock)')
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args = parser.parse_args()
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# Setup logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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downloader = ModelDownloader()
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# Show info
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if args.info:
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info = downloader.get_model_info()
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print("\n=== Model Information ===")
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for key, value in info.items():
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print(f"{key}: {value}")
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return 0
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# Download model
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if args.url:
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success = downloader.download_model(args.url, expected_hash=args.hash)
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if not success:
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return 1
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# Validate
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model = downloader.load_model()
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if not model or not downloader.validate_model(model):
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return 1
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# Configure
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if not downloader.configure_model_usage(use_real_model=True):
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return 1
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print("\nModel successfully downloaded and integrated!")
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return 0
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# Load existing model
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if args.load:
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model = downloader.load_model()
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if not model:
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return 1
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if not downloader.validate_model(model):
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return 1
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print("\nModel validation successful!")
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return 0
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# Enable real model
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if args.enable:
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if not downloader.configure_model_usage(use_real_model=True):
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return 1
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print("Real model usage enabled")
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return 0
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# Disable real model
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if args.disable:
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if not downloader.configure_model_usage(use_real_model=False):
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return 1
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print("Switched to mock model")
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return 0
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# Show usage
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if not any([args.url, args.load, args.info, args.enable, args.disable]):
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parser.print_help()
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print("\nExample usage:")
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print(" python download_pretrained_model.py --info")
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print(" python download_pretrained_model.py --url https://example.com/model.pkl --hash abc123")
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print(" python download_pretrained_model.py --load")
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print(" python download_pretrained_model.py --enable")
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return 0
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if __name__ == '__main__':
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sys.exit(main())
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