import os
import sys
from pathlib import Path

from huggingface_hub import snapshot_download

# ---------------------------------------------------------------------------
# Optional: pull checkpoints and auxiliary assets at startup.  Set the
# HF_GESTURELSM_WEIGHTS_REPO environment variable in the Space settings to the
# dataset or model repo that hosts the pre-trained weights (e.g. "username/gesturelsm-assets").
# Files will be placed under ckpt/ so the existing config paths keep working.
# ---------------------------------------------------------------------------
BASE_DIR = Path(__file__).parent.resolve()
ROOT_DIR = BASE_DIR.parent.resolve()

# Ensure project root is on sys.path so intra-repo imports (e.g. `optimizers`) work.
if str(ROOT_DIR) not in sys.path:
    sys.path.insert(0, str(ROOT_DIR))

# Quick sanity check in Space logs to confirm the repo root is visible at runtime.
print("[GestureLSM] sys.path:")
for entry in sys.path:
    print("  ", entry)
CKPT_DIR = BASE_DIR / "ckpt"
CKPT_DIR.mkdir(parents=True, exist_ok=True)

weights_repo = os.environ.get("HF_GESTURELSM_WEIGHTS_REPO", "").strip()
if weights_repo:
    snapshot_download(
        repo_id=weights_repo,
        repo_type="dataset",
        local_dir=CKPT_DIR,
        local_dir_use_symlinks=False,
        allow_patterns=["*.pth", "*.bin", "*.npz", "*.npy"],
    )

# Ensure expected runtime directories exist so the demo can write outputs.
for relative in ["outputs/audio2pose", "datasets/BEAT_SMPL"]:
    (BASE_DIR / relative).mkdir(parents=True, exist_ok=True)

# Reuse the existing Gradio interface defined in demo.py.
from demo import demo as gesture_demo  # noqa: E402

if __name__ == "__main__":
    gesture_demo.queue(concurrency_count=1).launch(server_name="0.0.0.0", share=False)