app.py

# app.py (Interface final com 3 abas: SeedVR, V-INT Edit, V-INT Pipeline)

import gradio as gr
import os
import subprocess
import shutil
import uuid
from huggingface_hub import snapshot_download
import spaces

# --- 1. CONFIGURAÇÃO E DOWNLOAD DOS MODELOS ---

SEEDVR_DIR = "/app/SeedVR"
VINCIE_DIR = "/app/VINCIE"
HF_TOKEN = os.environ.get("HF_TOKEN")

@spaces.GPU
def download_models():
    """Baixa os modelos para SeedVR e V-INT se eles não existirem."""
    # Download do SeedVR
    if not os.path.exists(os.path.join(SEEDVR_DIR, "ckpts", "seedvr2_ema_3b.pth")):
        print("Baixando modelo do SeedVR-3B...")
        snapshot_download(repo_id="ByteDance-Seed/SeedVR2-3B", local_dir=os.path.join(SEEDVR_DIR, "ckpts"), token=HF_TOKEN, local_dir_use_symlinks=False)
    else: print("Modelo do SeedVR já existe.")

    # Download do V-INT
    if not os.path.exists(os.path.join(VINCIE_DIR, "ckpt", "VINCIE-3B")):
        print("Baixando modelo do VINCIE-3B...")
        snapshot_download(repo_id="ByteDance-Seed/VINCIE-3B", local_dir=os.path.join(VINCIE_DIR, "ckpt", "VINCIE-3B"), token=HF_TOKEN, local_dir_use_symlinks=False)
    else: print("Modelo do VINCIE já existe.")

    # Download de assets e configs do V-INT (para os exemplos)
    if not os.path.exists(os.path.join(VINCIE_DIR, "assets", "woman_pineapple.png")):
         print("Baixando assets e configs do V-INT...")
         snapshot_download(repo_id="ByteDance-Seed/VINCIE", repo_type="space", local_dir=VINCIE_DIR, token=HF_TOKEN, allow_patterns=["assets/*", "configs/*"], local_dir_use_symlinks=False)
    else: print("Assets e configs do V-INT já existem.")

download_models()

# --- 2. LÓGICA DE INFERÊNCIA ---

def run_subprocess_with_logs(command, cwd):
    """Função genérica para rodar um subprocesso e streamar os logs para o Gradio."""
    log_output = f"Executando comando:\n{' '.join(command)}\n\n"
    yield [], log_output
    
    env = os.environ.copy(); env["PYTHONUNBUFFERED"] = "1"
    process = subprocess.Popen(command, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, encoding='utf-8', env=env)
    
    while True:
        output = process.stdout.readline()
        if output == '' and process.poll() is not None: break
        if output: log_output += output; yield [], log_output
            
    if process.poll() != 0: raise gr.Error("A inferência falhou. Verifique os logs.")

def run_seedvr_inference(video_path, seed):
    # (Lógica específica do SeedVR)
    if video_path is None: raise gr.Error("Por favor, faça o upload de um arquivo para o SeedVR.")
    job_id = str(uuid.uuid4())
    input_dir = os.path.join("/app", "temp_inputs", job_id); os.makedirs(input_dir, exist_ok=True)
    output_dir = os.path.join("/app", "temp_outputs", job_id); os.makedirs(output_dir, exist_ok=True)
    shutil.copy(video_path, input_dir)
    
    input_folder_relative = os.path.relpath(input_dir, SEEDVR_DIR)
    output_folder_relative = os.path.relpath(output_dir, SEEDVR_DIR)
    command = ["torchrun", "--nproc-per-node=4", "projects/inference_seedvr2_3b.py", "--video_path", input_folder_relative, "--output_dir", output_folder_relative, "--seed", str(seed), "--res_h", "720", "--res_w", "1280"]
    
    for gallery, logs in run_subprocess_with_logs(command, SEEDVR_DIR):
        yield None, logs # Retorna None para o output enquanto os logs são atualizados
    
    output_files = [os.path.join(output_dir, f) for f in os.listdir(output_dir) if f.endswith(('.mp4', '.png'))]
    if not output_files: raise gr.Error("Nenhum arquivo de saída encontrado.")
    yield output_files[0], logs

def run_vincie_edit_inference(files, prompts_str):
    # (Lógica específica para o modo de EDIÇÃO do V-INT)
    if not files: raise gr.Error("Por favor, faça o upload de uma imagem para editar.")
    if not prompts_str: raise gr.Error("Por favor, forneça os prompts de edição.")

# app.py (Interface final com 3 abas: SeedVR, V-INT Edit, V-INT Pipeline)

import gradio as gr
import os
import subprocess
import shutil
import uuid
from huggingface_hub import snapshot_download
import spaces

# --- 1. CONFIGURAÇÃO E DOWNLOAD DOS MODELOS ---

SEEDVR_DIR = "/app/SeedVR"
VINCIE_DIR = "/app/VINCIE"
HF_TOKEN = os.environ.get("HF_TOKEN")

@spaces.GPU
def download_models():
    """Baixa os modelos para SeedVR e V-INT se eles não existirem."""
    # Download do SeedVR
    if not os.path.exists(os.path.join(SEEDVR_DIR, "ckpts", "seedvr2_ema_3b.pth")):
        print("Baixando modelo do SeedVR-3B...")
        snapshot_download(repo_id="ByteDance-Seed/SeedVR2-3B", local_dir=os.path.join(SEEDVR_DIR, "ckpts"), token=HF_TOKEN, local_dir_use_symlinks=False)
    else: print("Modelo do SeedVR já existe.")

    # Download do V-INT
    if not os.path.exists(os.path.join(VINCIE_DIR, "ckpt", "VINCIE-3B")):
        print("Baixando modelo do VINCIE-3B...")
        snapshot_download(repo_id="ByteDance-Seed/VINCIE-3B", local_dir=os.path.join(VINCIE_DIR, "ckpt", "VINCIE-3B"), token=HF_TOKEN, local_dir_use_symlinks=False)
    else: print("Modelo do VINCIE já existe.")

    # Download de assets e configs do V-INT (para os exemplos)
    if not os.path.exists(os.path.join(VINCIE_DIR, "assets", "woman_pineapple.png")):
         print("Baixando assets e configs do V-INT...")
         snapshot_download(repo_id="ByteDance-Seed/VINCIE", repo_type="space", local_dir=VINCIE_DIR, token=HF_TOKEN, allow_patterns=["assets/*", "configs/*"], local_dir_use_symlinks=False)
    else: print("Assets e configs do V-INT já existem.")

download_models()

# --- 2. LÓGICA DE INFERÊNCIA ---

def run_subprocess_with_logs(command, cwd):
    """Função genérica para rodar um subprocesso e streamar os logs para o Gradio."""
    log_output = f"Executando comando:\n{' '.join(command)}\n\n"
    yield [], log_output
    
    env = os.environ.copy(); env["PYTHONUNBUFFERED"] = "1"
    process = subprocess.Popen(command, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, encoding='utf-8', env=env)
    
    while True:
        output = process.stdout.readline()
        if output == '' and process.poll() is not None: break
        if output: log_output += output; yield [], log_output
            
    if process.poll() != 0: raise gr.Error("A inferência falhou. Verifique os logs.")

def run_seedvr_inference(video_path, seed):
    # (Lógica específica do SeedVR)
    if video_path is None: raise gr.Error("Por favor, faça o upload de um arquivo para o SeedVR.")
    job_id = str(uuid.uuid4())
    input_dir = os.path.join("/app", "temp_inputs", job_id); os.makedirs(input_dir, exist_ok=True)
    output_dir = os.path.join("/app", "temp_outputs", job_id); os.makedirs(output_dir, exist_ok=True)
    shutil.copy(video_path, input_dir)
    
    input_folder_relative = os.path.relpath(input_dir, SEEDVR_DIR)
    output_folder_relative = os.path.relpath(output_dir, SEEDVR_DIR)
    command = ["torchrun", "--nproc-per-node=4", "projects/inference_seedvr2_3b.py", "--video_path", input_folder_relative, "--output_dir", output_folder_relative, "--seed", str(seed), "--res_h", "720", "--res_w", "1280"]
    
    for gallery, logs in run_subprocess_with_logs(command, SEEDVR_DIR):
        yield None, logs # Retorna None para o output enquanto os logs são atualizados
    
    output_files = [os.path.join(output_dir, f) for f in os.listdir(output_dir) if f.endswith(('.mp4', '.png'))]
    if not output_files: raise gr.Error("Nenhum arquivo de saída encontrado.")
    yield output_files[0], logs

def run_vincie_edit_inference(files, prompts_str):
    # (Lógica específica para o modo de EDIÇÃO do V-INT)
    if not files: raise gr.Error("Por favor, faça o upload de uma imagem para editar.")
    if not prompts_str: raise gr.Error("Por favor, forneça os prompts de edição.")

    job_id = str(uuid.uuid4())
    output_dir = os.path.join(VINCIE_DIR, "output", job_id)
    
    image_paths_str = ", ".join([f'"{f.name}"' for f in files])
    prompts_list_str = ", ".join([f'"{p.strip()}"' for p in prompts_str.split(';')])
    command = ["python", "main.py", "configs/generate.yaml", f'generation.positive_prompt.image_path=[{image_paths_str}]', f'generation.positive_prompt.prompts=[{prompts_list_str}]', f'generation.output.dir={output_dir}']
    
    for gallery, logs in run_subprocess_with_logs(command, VINCIE_DIR):
        yield gallery, logs
    
    output_files = [os.path.join(output_dir, f) for f in os.listdir(output_dir) if f.endswith(('.png', '.jpg'))]
    if not output_files: raise gr.Error("Nenhum arquivo de saída encontrado.")
    yield output_files, logs

def run_vincie_pipeline_inference(files, final_prompt):
    # (NOVA LÓGICA para o modo de PIPELINE do V-INT)
    if not files: raise gr.Error("Por favor, faça o upload de múltiplas imagens para a composição.")
    if not final_prompt: raise gr.Error("Por favor, forneça o prompt de composição final.")

    job_id = str(uuid.uuid4())
    output_dir = os.path.join(VINCIE_DIR, "output", job_id)

    # Gera os prompts de placeholder
    placeholder_prompts = [f"<IMG{i}>: " for i in range(len(files))]
    all_prompts = placeholder_prompts + [final_prompt]

    image_paths_str = ", ".join([f'"{f.name}"' for f in files])
    prompts_list_str = ", ".join([f'"{p}"' for p in all_prompts])
    command = ["python", "main.py", "configs/generate.yaml", "generation.pad_img_placehoder=False", f'generation.positive_prompt.image_path=[{image_paths_str}]', f'generation.positive_prompt.prompts=[{prompts_list_str}]', f'generation.output.dir={output_dir}']

    for gallery, logs in run_subprocess_with_logs(command, VINCIE_DIR):
        yield gallery, logs

    output_files = [os.path.join(output_dir, f) for f in os.listdir(output_dir) if f.endswith(('.png', '.jpg'))]
    if not output_files: raise gr.Error("Nenhum arquivo de saída foi encontrado.")
    yield output_files, logs

# --- 3. INTERFACE GRADIO COM 3 ABAS ---

with gr.Blocks() as demo:
    gr.Markdown("<h1><center>Super-Space: SeedVR & V-INT</center></h1>")
    
    with gr.Tabs():
        # --- ABA SEEDVR ---
        with gr.TabItem("SeedVR (Restauração de Vídeo)"):
            with gr.Row():
                with gr.Column(scale=1):
                    seedvr_input_video = gr.Video(label="Upload de Vídeo")
                    seedvr_seed = gr.Number(value=666, label="Seed")
                    seedvr_run_button = gr.Button("Executar SeedVR", variant="primary")
                with gr.Column(scale=2):
                    seedvr_output = gr.Video(label="Vídeo Restaurado")
                    seedvr_logs = gr.Textbox(label="Logs", lines=10, interactive=False)
            seedvr_run_button.click(fn=run_seedvr_inference, inputs=[seedvr_input_video, seedvr_seed], outputs=[seedvr_output, seedvr_logs])

        # --- ABA V-INT EDIÇÃO ---
        with gr.TabItem("V-INT (Edição de Imagem)"):
            with gr.Row():
                with gr.Column(scale=1):
                    vincie_edit_input = gr.Files(label="Upload de Imagem(ns) para Editar", file_types=["image"])
                    vincie_edit_prompts = gr.Textbox(label="Prompts de Edição (separados por ';')", lines=5, placeholder="Ex: Add a crown to her head; Change the background...")
                    vincie_edit_button = gr.Button("Executar Edição", variant="primary")
                with gr.Column(scale=2):
                    vincie_edit_output = gr.Gallery(label="Imagens Editadas")
                    vincie_edit_logs = gr.Textbox(label="Logs", lines=10, interactive=False)
            gr.Examples(examples=[[[os.path.join(VINCIE_DIR, "assets/woman_pineapple.png")], "Lower the pineapple beside her face; Add a crown to the woman's head."]], inputs=[vincie_edit_input, vincie_edit_prompts])
            vincie_edit_button.click(fn=run_vincie_edit_inference, inputs=[vincie_edit_input, vincie_edit_prompts], outputs=[vincie_edit_output, vincie_edit_logs])

        # --- NOVA ABA V-INT PIPELINE ---
        with gr.TabItem("V-INT (Pipeline/Composição)"):
            with gr.Row():
                with gr.Column(scale=1):
                    vincie_pipe_inputs = gr.Files(label="Upload de Múltiplas Imagens para Composição (<IMG0>, <IMG1>, etc.)", file_types=["image"])
                    vincie_pipe_prompt = gr.Textbox(label="Prompt de Composição Final", lines=5, placeholder="Ex: Based on <IMG0> and <IMG1>, a woman in <IMG0> is holding the cat from <IMG1>. Output <IMG2>:")
                    vincie_pipe_button = gr.Button("Executar Pipeline", variant="primary")
                with gr.Column(scale=2):
                    vincie_pipe_output = gr.Gallery(label="Imagem Composta")
                    vincie_pipe_logs = gr.Textbox(label="Logs", lines=10, interactive=False)
            
            # Prepara os caminhos para os arquivos de exemplo
            father_path = os.path.join(VINCIE_DIR, "assets/father.png")
            mother_path = os.path.join(VINCIE_DIR, "assets/mother.png")
            son_path = os.path.join(VINCIE_DIR, "assets/son.png")
            family_prompt = "Based on <IMG0>, <IMG1>, and <IMG2>, A smiling family with the father from <IMG0>, mother from <IMG1>, and son from <IMG2>, poses for a portrait amidst the sunlit trees. Output <IMG3>:"
            gr.Examples(examples=[[[father_path, mother_path, son_path], family_prompt]], inputs=[vincie_pipe_inputs, vincie_pipe_prompt])
            
            vincie_pipe_button.click(fn=run_vincie_pipeline_inference, inputs=[vincie_pipe_inputs, vincie_pipe_prompt], outputs=[vincie_pipe_output, vincie_pipe_logs])

demo.queue().launch()