app.py

import gradio as gr
import subprocess, os, uuid

BASE = "/app"
WAV2LIP_DIR = f"{BASE}/Wav2Lip"
MODEL_PATH = f"{WAV2LIP_DIR}/checkpoints/wav2lip_gan.pth"

def setup():
    if not os.path.exists(f"{WAV2LIP_DIR}/inference.py"):
        print("Cloning Wav2Lip...", flush=True)
        subprocess.run([
            "git", "clone",
            "https://github.com/Rudrabha/Wav2Lip.git",
            WAV2LIP_DIR
        ], check=True, cwd=BASE)
        print("Clone done!", flush=True)

    os.makedirs(f"{WAV2LIP_DIR}/checkpoints", exist_ok=True)
    os.makedirs(f"{WAV2LIP_DIR}/temp", exist_ok=True)
    os.makedirs(f"{BASE}/temp", exist_ok=True)

    if not os.path.exists(MODEL_PATH):
        print("Downloading model...", flush=True)
        subprocess.run([
            "wget", "-q",
            "https://huggingface.co/numz/wav2lip_studio/resolve/main/Wav2Lip/checkpoints/wav2lip_gan.pth",
            "-O", MODEL_PATH
        ], check=True)
        print("Model ready!", flush=True)
    
    print(f"inference.py exists: {os.path.exists(f'{WAV2LIP_DIR}/inference.py')}", flush=True)

try:
    setup()
    print("Setup complete!", flush=True)
except Exception as e:
    print(f"Setup error: {e}", flush=True)

def lipsync(face, audio):
    os.makedirs(f"{BASE}/temp", exist_ok=True)
    os.makedirs(f"{WAV2LIP_DIR}/temp", exist_ok=True)
    out_path = f"/tmp/{str(uuid.uuid4())[:8]}.mp4"

    cmd = [
        "python", f"{WAV2LIP_DIR}/inference.py",
        "--checkpoint_path", MODEL_PATH,
        "--face", face,
        "--audio", audio,
        "--outfile", out_path,
        "--nosmooth"
    ]

    result = subprocess.run(cmd, capture_output=True, text=True, cwd=WAV2LIP_DIR)
    print(result.stdout[-500:], flush=True)
    print(result.stderr[-500:], flush=True)

    if result.returncode != 0:
        raise gr.Error(result.stderr[-400:])

    return out_path

demo = gr.Interface(
    fn=lipsync,
    inputs=[
        gr.Image(type="filepath", label="📸 Photo du visage"),
        gr.Audio(type="filepath", label="🎤 Audio"),
    ],
    outputs=gr.Video(label="🎬 Résultat"),
    title="LipSync AI 👄",
    api_name="lipsync"
)

demo.launch()