app.py

# app_refactored_with_postprod.py (FINAL VERSION with LTX Refinement)

import gradio as gr
import os
import sys
import traceback
from pathlib import Path
import torch
import numpy as np
from PIL import Image

# --- Import dos Serviços de Backend ---

# Serviço LTX para geração de vídeo base e refinamento de textura
from api.ltx_server_refactored import video_generation_service

# Serviço SeedVR para upscaling de alta qualidade
from api.seedvr_server import SeedVRServer

# Inicializa o servidor SeedVR uma vez, se disponível
seedvr_inference_server = SeedVRServer() if SeedVRServer else None

# --- ESTADO DA SESSÃO ---
def create_initial_state():
    return {
        "low_res_video": None,
        "low_res_latents": None,
        "refined_video_ltx": None,
        "refined_latents_ltx": None,
        "used_seed": None
    }

# --- FUNÇÕES WRAPPER PARA A UI ---

def run_generate_low(prompt, neg_prompt, start_img, height, width, duration, cfg, seed, randomize_seed, progress=gr.Progress(track_tqdm=True)):
    """Executa a primeira etapa: geração de um vídeo base em baixa resolução."""
    print("UI: Chamando generate_low")
    if True:
        
        conditioning_items = []
        if start_img:
            num_frames_estimate = int(duration * 24)
            items_list = [[start_img, 0, 1.0]]
            conditioning_items = video_generation_service._prepare_condition_items(items_list, height, width, num_frames_estimate)

            
        used_seed = None if randomize_seed else seed
        
        video_path, tensor_path, final_seed = video_generation_service.generate_low_resolution(
            prompt=prompt, negative_prompt=neg_prompt,
            height=height, width=width, duration_secs=duration,
            guidance_scale=cfg, seed=used_seed,
            conditioning_items=conditioning_items
        )
        
        new_state = {
            "low_res_video": video_path,
            "low_res_latents": tensor_path,
            "refined_video_ltx": None,
            "refined_latents_ltx": None,
            "used_seed": final_seed
        }
        
        return video_path, new_state, gr.update(visible=True)
    
def run_ltx_refinement(state, prompt, neg_prompt, cfg, progress=gr.Progress(track_tqdm=True)):
    """Executa o processo de refinamento e upscaling de textura com o pipeline LTX."""
    print("UI: Chamando run_ltx_refinement (generate_upscale_denoise)")
    
    if True:
        video_path, tensor_path = video_generation_service.generate_upscale_denoise(
            latents_path=state["low_res_latents"],
            prompt=prompt,
            negative_prompt=neg_prompt,
            guidance_scale=cfg,
            seed=state["used_seed"]
        )
        
        # Atualiza o estado com os novos artefatos refinados
        state["refined_video_ltx"] = video_path
        state["refined_latents_ltx"] = tensor_path
        
        return video_path, state
    
def run_seedvr_upscaling(state, seed, resolution, batch_size, fps, progress=gr.Progress(track_tqdm=True)):
    """Executa o processo de upscaling com SeedVR."""
            
    video_path = state["low_res_video"]
    print(f"▶️  Iniciando processo de upscaling SeedVR para o vídeo: {video_path}")
    
    if True:
        def progress_wrapper(p, desc=""):
            progress(p, desc=desc)
        output_filepath = seedvr_inference_server.run_inference(
            file_path=video_path, seed=seed, resolution=resolution,
            batch_size=batch_size, fps=fps, progress=progress_wrapper
        )
        final_message = f"✅ Processo SeedVR concluído!\nVídeo salvo em: {output_filepath}"
        return gr.update(value=output_filepath, interactive=True), gr.update(value=final_message, interactive=False)
    
# --- DEFINIÇÃO DA INTERFACE GRADIO ---
with gr.Blocks() as demo:
    gr.Markdown("# LTX Video - Geração e Pós-Produção por Etapas")
    
    app_state = gr.State(value=create_initial_state())

    # --- ETAPA 1: Geração Base ---
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### Etapa 1: Configurações de Geração")
            prompt_input = gr.Textbox(label="Prompt", value="A majestic dragon flying over a medieval castle", lines=3)
            neg_prompt_input = gr.Textbox(visible=False, label="Negative Prompt", value="worst quality, blurry, low quality, jittery", lines=2)
            start_image = gr.Image(label="Imagem de Início (Opcional)", type="filepath", sources=["upload", "clipboard"])
            
            with gr.Accordion("Parâmetros Avançados", open=False):
                height_input = gr.Slider(label="Height", value=512, step=64, minimum=256, maximum=1024)
                width_input = gr.Slider(label="Width", value=512, step=64, minimum=256, maximum=1024)
                duration_input = gr.Slider(label="Duração (s)", value=8, step=0.5, minimum=1, maximum=16)
                cfg_input = gr.Slider(label="Guidance Scale (CFG)", value=5.0, step=1, minimum=1, maximum=10.0)
                seed_input = gr.Number(label="Seed", value=42, precision=0)
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)

            generate_low_btn = gr.Button("1. Gerar Vídeo Base (Low-Res)", variant="primary")
        
        with gr.Column(scale=1):
            gr.Markdown("### Vídeo Base Gerado")
            low_res_video_output = gr.Video(label="O resultado da Etapa 1 aparecerá aqui", interactive=False)

    # --- ETAPA 2: Pós-Produção (no rodapé, em abas) ---
    with gr.Group(visible=False) as post_prod_group:
        gr.Markdown("<hr style='margin-top: 20px; margin-bottom: 20px;'>")
        gr.Markdown("## Etapa 2: Pós-Produção")
        gr.Markdown("Use o vídeo gerado acima como entrada para as ferramentas abaixo. **O prompt e a CFG da Etapa 1 serão reutilizados.**")

        with gr.Tabs():
            # --- ABA LTX REFINEMENT (AGORA FUNCIONAL) ---
            with gr.TabItem("🚀 Upscaler Textura (LTX)"):
                with gr.Row():
                    with gr.Column(scale=1):
                         gr.Markdown("### Parâmetros de Refinamento")
                         gr.Markdown("Esta etapa reutiliza o prompt, o prompt negativo e a CFG da Etapa 1 para manter a consistência.")
                         ltx_refine_btn = gr.Button("Aplicar Refinamento de Textura LTX", variant="primary")
                    with gr.Column(scale=1):
                        gr.Markdown("### Resultado do Refinamento")
                        ltx_refined_video_output = gr.Video(label="Vídeo com Textura Refinada (LTX)", interactive=False)

            # --- ABA SEEDVR UPSCALER ---
            with gr.TabItem("✨ Upscaler SeedVR"):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("### Parâmetros do SeedVR")
                        seedvr_seed = gr.Slider(minimum=0, maximum=999999, value=42, step=1, label="Seed")
                        seedvr_resolution = gr.Slider(minimum=720, maximum=1440, value=1072, step=8, label="Resolução Vertical (Altura)")
                        seedvr_batch_size = gr.Slider(minimum=1, maximum=16, value=4, step=1, label="Batch Size por GPU")
                        seedvr_fps_output = gr.Number(label="FPS de Saída (0 = original)", value=0)
                        run_seedvr_button = gr.Button("Iniciar Upscaling SeedVR", variant="primary", interactive=(seedvr_inference_server is not None))
                        if not seedvr_inference_server:
                             gr.Markdown("<p style='color: red;'>Serviço SeedVR não disponível.</p>")
                    with gr.Column(scale=1):
                        gr.Markdown("### Resultado do Upscaling")
                        seedvr_video_output = gr.Video(label="Vídeo com Upscale SeedVR", interactive=False)
                        seedvr_status_box = gr.Textbox(label="Status do Processamento", value="Aguardando...", lines=3, interactive=False)

            # --- ABA MM-AUDIO ---
            with gr.TabItem("🔊 Áudio (MM-Audio)"):
                gr.Markdown("*(Funcionalidade futura para adicionar som aos vídeos)*")

    # --- LÓGICA DE EVENTOS DA UI ---

    # Botão da Etapa 1
    generate_low_btn.click(
        fn=run_generate_low,
        inputs=[prompt_input, neg_prompt_input, start_image, height_input, width_input, duration_input, cfg_input, seed_input, randomize_seed],
        outputs=[low_res_video_output, app_state, post_prod_group]
    )

    # Botão da Aba LTX Refinement
    ltx_refine_btn.click(
        fn=run_ltx_refinement,
        inputs=[app_state, prompt_input, neg_prompt_input, cfg_input],
        outputs=[ltx_refined_video_output, app_state]
    )

    # Botão da Aba SeedVR
    run_seedvr_button.click(
        fn=run_seedvr_upscaling,
        inputs=[app_state, seedvr_seed, seedvr_resolution, seedvr_batch_size, seedvr_fps_output],
        outputs=[seedvr_video_output, seedvr_status_box]
    )

if __name__ == "__main__":
    demo.queue().launch(server_name="0.0.0.0", server_port=7860, debug=True, show_error=True)