#!/usr/bin/env python3
"""
Individual model file upload script
Uses the successful single-file upload approach
"""

import os
import logging
from huggingface_hub import upload_file

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def upload_individual_models():
    """Upload individual model files using proven method"""
    
    token = os.getenv('HF_TOKEN')
    if not token:
        raise ValueError("HF_TOKEN environment variable not set")
    
    # Find all model files (excluding extremely large ones)
    model_files = []
    experiments_path = "/data/experiments"
    
    if os.path.exists(experiments_path):
        for root, _, files in os.walk(experiments_path):
            for file in files:
                if file.endswith(('.safetensors', '.pt', '.bin')):
                    file_path = os.path.join(root, file)
                    try:
                        file_size = os.path.getsize(file_path)
                        # Skip files larger than 10GB
                        if file_size > 10 * 1024**3:
                            logger.warning(f"Skipping extremely large file: {file_path} ({file_size/1024**3:.1f}GB)")
                            continue
                        model_files.append(file_path)
                    except OSError:
                        logger.warning(f"Could not get size for {file_path}")
    
    logger.info(f"Found {len(model_files)} model files to upload")
    
    # Upload files individually
    success_count = 0
    failed_count = 0
    
    for file_path in model_files:
        try:
            # Create repository path
            rel_path = file_path.replace('/data/experiments/', '')
            
            logger.info(f"Uploading: {file_path} -> LevelUp2x/dto-models/{rel_path}")
            
            # Upload individual file (this method worked for the test file)
            upload_file(
                path_or_fileobj=file_path,
                path_in_repo=rel_path,
                repo_id='LevelUp2x/dto-models',
                token=token,
                commit_message=f"DTO Archive: Uploading {os.path.basename(file_path)}"
            )
            
            logger.info(f"✅ Successfully uploaded {file_path}")
            success_count += 1
            
        except Exception as e:
            logger.error(f"❌ Failed to upload {file_path}: {e}")
            failed_count += 1
    
    logger.info(f"Upload Summary: {success_count} successful, {failed_count} failed")
    
    if success_count > 0:
        logger.info("✅ Individual file upload completed successfully")
        return True
    else:
        logger.error("❌ Individual file upload failed completely")
        return False

if __name__ == "__main__":
    # Load environment variables
    env_file = "/data/adaptai/platform/dataops/dto/.env"
    if os.path.exists(env_file):
        with open(env_file) as f:
            for line in f:
                if line.strip() and not line.startswith('#'):
                    key, value = line.strip().split('=', 1)
                    os.environ[key] = value
    
    upload_individual_models()