scripts/run_gradio.sh

#!/bin/bash

set -e  # Exit immediately on error
set -o pipefail  # Catch errors in pipelines

JOBS_DIR=$(dirname "$(dirname "$0")")
export PYTHONPATH=./

MODEL_BASE=./weights
MODEL_DIR="${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p"
CHECKPOINT_FILE="${MODEL_DIR}/transformers/mp_rank_00_model_states.pt"

# Step 1: Ensure model is downloaded
echo "📦 Checking model checkpoint..."
if [ -f "$CHECKPOINT_FILE" ]; then
    echo "✅ Model checkpoint already exists. Skipping download."
else
    echo "⬇️  Model not found. Downloading from Hugging Face..."
    python3 - <<EOF
from huggingface_hub import snapshot_download
import os
target_dir = "${MODEL_DIR}"
os.makedirs(target_dir, exist_ok=True)
snapshot_download(
    repo_id="tencent/HunyuanVideo-Avatar",
    local_dir=target_dir,
    local_dir_use_symlinks=False
)
EOF

    # Confirm model was downloaded
    if [ ! -f "$CHECKPOINT_FILE" ]; then
        echo "❌ Failed to download model checkpoint. Exiting."
        exit 1
    fi
    echo "✅ Model downloaded successfully."
fi

# Step 2: Launch the Flask audio server
echo "🚀 Starting flask_audio.py..."
torchrun --nnodes=1 --nproc_per_node=8 --master_port=29605 hymm_gradio/flask_audio.py \
    --input 'assets/test.csv' \
    --ckpt "${CHECKPOINT_FILE}" \
    --sample-n-frames 129 \
    --seed 128 \
    --image-size 704 \
    --cfg-scale 7.5 \
    --infer-steps 50 \
    --use-deepcache 1 \
    --flow-shift-eval-video 5.0 &

# Step 3: Run Gradio UI
echo "🟢 Starting gradio_audio.py UI..."
# Add your command to run gradio_audio.py here
# Example (uncomment and adjust as needed):