import os
import subprocess
import sys

print("--- STEP 1: Installing Pre-Compiled llama-cpp-python Wheel (Fast Track) ---")
# This forces pip to pull a ready-made binary instead of compiling it from C++ source
subprocess.run([
    sys.executable, "-m", "pip", "install", "llama-cpp-python[server]",
    "--extra-index-url", "https://abetlen.github.io/llama-cpp-python/whl/cpu"
])

# Now that it's installed, we can safely import huggingface tools
from huggingface_hub import hf_hub_download

# =========================================================================
# CONFIGURATION: Targets the exact repository and 4-bit model file
# =========================================================================
REPO_ID = "bartowski/google_gemma-3-4b-it-GGUF"
FILENAME = "google_gemma-3-4b-it-Q4_K_M.gguf"

print("--- STEP 2: Downloading Gemma 3 4B Model Weights ---")
model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
print(f"Model successfully saved to cache area: {model_path}")

print("--- STEP 3: Initializing OpenAI-Compatible Server ---")
cmd = [
    "python3", "-m", "llama_cpp.server",
    "--model", model_path,
    "--model_alias", "gemma-3",
    "--host", "0.0.0.0",
    "--port", "7860",       # Mandatory port required by Hugging Face
    "--n_ctx", "2048",      # Context limit optimized for RAM protection 
    "--n_threads", "2"      # Uses exactly the 2 free vCPUs allocated
]

# Run server engine
subprocess.run(cmd)