setup.py

# #!/usr/bin/env python3
# """
# Quick setup script to optimize your existing ML microservice.
# Run this to set up caching and pre-download the model.
# """

# import os
# import sys
# import logging
# from pathlib import Path

# # Configure logging
# logging.basicConfig(level=logging.INFO)
# logger = logging.getLogger(__name__)

# def setup_cache_directory():
#     """Create cache directory for models"""
#     cache_dir = Path("./model_cache")
#     cache_dir.mkdir(exist_ok=True)
#     logger.info(f"✅ Cache directory created: {cache_dir.absolute()}")
#     return cache_dir

# def set_environment_variables():
#     """Set environment variables for optimization"""
#     env_vars = {
#         "TRANSFORMERS_CACHE": "./model_cache",
#         "HF_HOME": "./model_cache", 
#         "TORCH_HOME": "./model_cache",
#         "TOKENIZERS_PARALLELISM": "false",
#         "OMP_NUM_THREADS": "4"
#     }
    
#     for key, value in env_vars.items():
#         os.environ[key] = value
#         logger.info(f"Set {key}={value}")

# def pre_download_model():
#     """Pre-download the model to cache"""
#     try:
#         from transformers import AutoTokenizer, AutoModelForCausalLM
        
#         model_id = "deepseek-ai/deepseek-coder-1.3b-instruct"
#         cache_dir = "./model_cache"
        
#         logger.info(f"🔧 Pre-downloading model: {model_id}")
#         logger.info("This may take a few minutes on first run...")
        
#         # Download tokenizer
#         logger.info("📝 Downloading tokenizer...")
#         tokenizer = AutoTokenizer.from_pretrained(
#             model_id,
#             cache_dir=cache_dir,
#             trust_remote_code=True
#         )
        
#         # Download model  
#         logger.info("🧠 Downloading model...")
#         model = AutoModelForCausalLM.from_pretrained(
#             model_id,
#             cache_dir=cache_dir,
#             trust_remote_code=True,
#             torch_dtype="auto",  # Let it choose the best dtype
#             low_cpu_mem_usage=True,
#         )
        
#         logger.info("✅ Model downloaded and cached successfully!")
#         logger.info(f"📁 Model cached in: {Path(cache_dir).absolute()}")
        
#         # Test that everything works
#         logger.info("🧪 Testing model loading...")
#         del model, tokenizer  # Free memory
        
#         return True
        
#     except Exception as e:
#         logger.error(f"❌ Failed to pre-download model: {e}")
#         return False

# def main():
#     """Main setup function"""
#     logger.info("🚀 Setting up ML Microservice Optimizations")
#     logger.info("=" * 50)
    
#     # Step 1: Setup cache directory
#     setup_cache_directory()
    
#     # Step 2: Set environment variables
#     set_environment_variables()
    
#     # Step 3: Pre-download model
#     success = pre_download_model()
    
#     if success:
#         logger.info("\n✅ Setup completed successfully!")
#         logger.info("📋 Next steps:")
#         logger.info("1. Replace your main.py with the optimized version")
#         logger.info("2. Replace your model.py with the optimized version") 
#         logger.info("3. Run: python main.py")
#         logger.info("\n🚀 Your server will now start much faster!")
#     else:
#         logger.error("\n❌ Setup failed!")
#         logger.error("Please check your internet connection and try again.")
#         sys.exit(1)

# if __name__ == "__main__":
#     main()

# setup.py
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import os

MODEL_ID = "Salesforce/codet5p-220m"

def download_model():
    print(f"[SETUP] Downloading model: {MODEL_ID}")
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
    model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
    print("[SETUP] Model and tokenizer downloaded ✅")

if __name__ == "__main__":
    os.makedirs("model_cache", exist_ok=True)
    download_model()