File size: 2,540 Bytes
a284aa3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import os
import shutil
import subprocess
import spaces
import gradio as gr
from pathlib import Path
from huggingface_hub import snapshot_download
# --- 1. CONFIGURATION & WEIGHT HYDRATION ---
REPO_ID = "Macfeigh/visor-weights"
CHECKPOINT_DIR = "/app/SadTalker/checkpoints"
def hydrate_workspace():
# Clone SadTalker and Wav2Lip if they don't exist
if not os.path.exists("/app/SadTalker"):
print("📥 Cloning SadTalker...")
subprocess.run(["git", "clone", "https://github.com/OpenTalker/SadTalker.git", "/app/SadTalker"])
if not os.path.exists("/app/Wav2Lip"):
print("📥 Cloning Wav2Lip...")
subprocess.run(["git", "clone", "https://github.com/Rudrabha/Wav2Lip.git", "/app/Wav2Lip"])
# Download weights from your model repo
print(f"🌡️ Pulling weights from {REPO_ID}...")
snapshot_download(
repo_id=REPO_ID,
local_dir=CHECKPOINT_DIR,
local_dir_use_symlinks=False
)
print("✅ Weights ready.")
# Initialize workspace before Gradio starts
hydrate_workspace()
# --- 2. GPU INFERENCE ENGINE ---
@spaces.GPU(duration=120)
def visor_execute(image_path, audio_path):
output_dir = "/tmp/visor_output"
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.makedirs(output_dir, exist_ok=True)
print("🚀 Executing Phase 3: Animation...")
# Run SadTalker
st_proc = subprocess.run([
"python", "/app/SadTalker/inference.py",
"--driven_audio", audio_path,
"--source_image", image_path,
"--result_dir", output_dir,
"--still",
"--preprocess", "full",
"--checkpoint_dir", CHECKPOINT_DIR
], env={**os.environ, "PYTHONPATH": "/app/SadTalker"})
# Find result
generated_videos = list(Path(output_dir).glob("**/*.mp4"))
if not generated_videos:
return None
return str(generated_videos[0])
# --- 3. INTERFACE ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🛡️ VisorFlow Core: Sovereign Intelligence Node")
gr.Markdown("Zero-cost execution environment running on NVIDIA H200.")
with gr.Row():
with gr.Column():
img = gr.Image(type="filepath", label="Source Portrait")
aud = gr.Audio(type="filepath", label="Voice Command")
btn = gr.Button("RUN PHASE 3", variant="primary")
with gr.Column():
out = gr.Video(label="Generated Output")
btn.click(fn=visor_execute, inputs=[img, aud], outputs=[out])
demo.launch() |