app.py

# FILE: app.py
# DESCRIPTION: Final Gradio web interface for the ADUC-SDR Video Suite.
# This version definitively removes the guidance settings from the UI for a streamlined experience.

import gradio as gr
import traceback
import sys
import os
import logging

# ==============================================================================
# --- IMPORTAÇÃO DOS SERVIÇOS DE BACKEND E UTILS ---
# ==============================================================================
try:
    from api.ltx_server_refactored_complete import video_generation_service
    from api.utils.debug_utils import log_function_io
    from api.seedvr_server import seedvr_server_singleton as seedvr_inference_server
    logging.info("All backend services (LTX, SeedVR) and debug utils imported successfully.")
except ImportError as e:
    def log_function_io(func): return func
    logging.warning(f"Could not import a module. Some services or debug logs may be unavailable. Details: {e}")
    if 'video_generation_service' not in locals():
        logging.critical(f"FATAL: Main LTX service failed to import.", exc_info=True)
        sys.exit(1)
    if 'seedvr_inference_server' not in locals():
        seedvr_inference_server = None
        logging.warning("SeedVR server could not be initialized. Upscaling tab will be disabled.")
except Exception as e:
    logging.critical(f"FATAL ERROR during backend initialization. Details: {e}", exc_info=True)
    sys.exit(1)

# ==============================================================================
# --- FUNÇÕES WRAPPER (PONTE ENTRE UI E BACKEND) ---
# ==============================================================================

@log_function_io
def run_generate_base_video(
    generation_mode: str, prompt: str, neg_prompt: str, start_img: str, 
    height: int, width: int, duration: float,
    fp_num_inference_steps: int, fp_skip_initial_steps: int, fp_skip_final_steps: int,
    progress=gr.Progress(track_tqdm=True)
) -> tuple:
    """Wrapper that collects UI data and calls the backend (without guidance parameters)."""
    try:
        logging.info(f"[UI] Request received. Selected mode: {generation_mode}")
        initial_conditions = []
        if start_img:
            num_frames_estimate = int(duration * 24)
            items_list = [[start_img, 0, 1.0]]
            initial_conditions = video_generation_service.prepare_condition_items(
                items_list, height, width, num_frames_estimate
            )

        ltx_configs = {
            "num_inference_steps": fp_num_inference_steps,
            "skip_initial_inference_steps": fp_skip_initial_steps,
            "skip_final_inference_steps": fp_skip_final_steps,
        }

        video_path, tensor_path, final_seed = video_generation_service.generate_low_resolution(
            prompt=prompt, negative_prompt=neg_prompt,
            height=height, width=width, duration=duration,
            initial_conditions=initial_conditions, ltx_configs_override=ltx_configs
        )
        
        if not video_path: raise RuntimeError("Backend failed to return a valid video path.")
        new_state = {"low_res_video": video_path, "low_res_latents": tensor_path, "used_seed": final_seed}
        logging.info(f"[UI] Base video generation successful. Seed used: {final_seed}, Path: {video_path}")
        return video_path, new_state, gr.update(visible=True)
        
    except Exception as e:
        error_message = f"❌ An error occurred during base generation:\n{e}"
        logging.error(f"{error_message}\nDetails: {traceback.format_exc()}", exc_info=True)
        raise gr.Error(error_message)

@log_function_io
def run_ltx_refinement(state: dict, prompt: str, neg_prompt: str, progress=gr.Progress(track_tqdm=True)) -> tuple:
    """Wrapper for the LTX texture refinement function."""
    if not state or not state.get("low_res_latents"):
        raise gr.Error("Error: Please generate a base video in Step 1 before refining.")
    try:
        logging.info(f"[UI] Requesting LTX refinement for latents: {state.get('low_res_latents')}")
        video_path, tensor_path = video_generation_service.generate_upscale_denoise(
            latents_path=state["low_res_latents"],
            prompt=prompt, negative_prompt=neg_prompt,
            seed=state["used_seed"]
        )
        state["refined_video_ltx"] = video_path
        state["refined_latents_ltx"] = tensor_path
        logging.info(f"[UI] LTX refinement successful. Path: {video_path}")
        return video_path, state
    except Exception as e:
        error_message = f"❌ An error occurred during LTX Refinement:\n{e}"
        logging.error(f"{error_message}\nDetails: {traceback.format_exc()}", exc_info=True)
        raise gr.Error(error_message)

@log_function_io
def run_seedvr_upscaling(state: dict, seed: int, resolution: int, batch_size: int, fps: int, progress=gr.Progress(track_tqdm=True)) -> tuple:
    """Wrapper for the SeedVR resolution upscaling service."""
    if not state or not state.get("low_res_video"):
        raise gr.Error("Error: Please generate a base video in Step 1 before upscaling.")
    if not seedvr_inference_server:
        raise gr.Error("Error: The SeedVR upscaling server is not available.")
    try:
        logging.info(f"[UI] Requesting SeedVR upscaling for video: {state.get('low_res_video')}")
        def progress_wrapper(p, desc=""): progress(p, desc=desc)
        output_filepath = seedvr_inference_server.run_inference(
            file_path=state["low_res_video"], seed=int(seed), resolution=int(resolution),
            batch_size=int(batch_size), fps=float(fps), progress=progress_wrapper
        )
        status_message = f"✅ Upscaling complete!\nSaved to: {output_filepath}"
        logging.info(f"[UI] SeedVR upscaling successful. Path: {output_filepath}")
        return gr.update(value=output_filepath), gr.update(value=status_message)
    except Exception as e:
        error_message = f"❌ An error occurred during SeedVR Upscaling:\n{e}"
        logging.error(f"{error_message}\nDetails: {traceback.format_exc()}", exc_info=True)
        return None, gr.update(value=error_message)

# ==============================================================================
# --- CONSTRUÇÃO DA INTERFACE GRADIO ---
# ==============================================================================

def build_ui():
    """Builds the entire Gradio application UI."""
    with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo")) as demo:
        app_state = gr.State(value={"low_res_video": None, "low_res_latents": None, "used_seed": None})
        ui_components = {}
        gr.Markdown("# ADUC-SDR Video Suite - LTX & SeedVR Workflow", elem_id="main-title")
        with gr.Row():
            with gr.Column(scale=1): _build_generation_controls(ui_components)
            with gr.Column(scale=1):
                gr.Markdown("### Etapa 1: Vídeo Base Gerado")
                ui_components['low_res_video_output'] = gr.Video(label="O resultado aparecerá aqui", interactive=False)
                ui_components['used_seed_display'] = gr.Textbox(label="Seed Utilizada", interactive=False)
        _build_postprod_controls(ui_components)
        _register_event_handlers(app_state, ui_components)
    return demo

def _build_generation_controls(ui: dict):
    """Builds the UI components for Step 1, with the guidance section removed."""
    gr.Markdown("### Configurações de Geração")
    ui['generation_mode'] = gr.Radio(label="Modo de Geração", choices=["Simples (Prompt Único)", "Narrativa (Múltiplos Prompts)"], value="Narrativa (Múltiplos Prompts)")
    ui['prompt'] = gr.Textbox(label="Prompt(s)", value="Um leão majestoso caminha pela savana\nEle sobe em uma grande pedra e olha o horizonte", lines=4)
    ui['neg_prompt'] = gr.Textbox(label="Negative Prompt", value="blurry, low quality, bad anatomy, deformed", lines=2)
    ui['start_image'] = gr.Image(label="Imagem de Início (Opcional)", type="filepath", sources=["upload"])
    
    with gr.Accordion("Parâmetros Principais", open=True):
        ui['duration'] = gr.Slider(label="Duração Total (s)", value=4, step=1, minimum=1, maximum=30)
        with gr.Row():
            ui['height'] = gr.Slider(label="Height", value=432, step=8, minimum=256, maximum=1024)
            ui['width'] = gr.Slider(label="Width", value=768, step=8, minimum=256, maximum=1024)

    with gr.Accordion("Opções Avançadas LTX", open=False):
        gr.Markdown("#### Configurações de Passos de Inferência (First Pass)")
        gr.Markdown("*Deixe o valor padrão (ex: 20) ou 0 para usar a configuração do `config.yaml`.*")
        ui['fp_num_inference_steps'] = gr.Slider(label="Número de Passos", minimum=0, maximum=100, step=1, value=20, info="Padrão LTX: 20.")
        ui['fp_skip_initial_steps'] = gr.Slider(label="Pular Passos Iniciais", minimum=0, maximum=100, step=1, value=0)
        ui['fp_skip_final_steps'] = gr.Slider(label="Pular Passos Finais", minimum=0, maximum=100, step=1, value=0)
    
    ui['generate_low_btn'] = gr.Button("1. Gerar Vídeo Base", variant="primary")

def _build_postprod_controls(ui: dict):
    """Builds the UI components for Step 2: Post-Production."""
    with gr.Group(visible=False) as ui['post_prod_group']:
        gr.Markdown("--- \n## Etapa 2: Pós-Produção")
        with gr.Tabs():
            with gr.TabItem("🚀 Upscaler de Textura (LTX)"):
                with gr.Row():
                    with gr.Column(scale=1):
                         gr.Markdown("Usa o prompt e a semente originais para refinar o vídeo, adicionando detalhes e texturas de alta qualidade.")
                         ui['ltx_refine_btn'] = gr.Button("2. Aplicar Refinamento LTX", variant="primary")
                    with gr.Column(scale=1):
                        ui['ltx_refined_video_output'] = gr.Video(label="Vídeo com Textura Refinada", interactive=False)
            
            with gr.TabItem("✨ Upscaler de Resolução (SeedVR)"):
                is_seedvr_available = seedvr_inference_server is not None
                if not is_seedvr_available:
                    gr.Markdown("🔴 **AVISO: O serviço SeedVR não está disponível.**")
                with gr.Row():
                    with gr.Column(scale=1):
                        ui['seedvr_seed'] = gr.Slider(minimum=0, maximum=999999, value=42, step=1, label="Seed")
                        ui['seedvr_resolution'] = gr.Slider(minimum=720, maximum=2160, value=1080, step=8, label="Resolução Vertical Alvo")
                        ui['seedvr_batch_size'] = gr.Slider(minimum=1, maximum=16, value=4, step=1, label="Batch Size por GPU")
                        ui['seedvr_fps'] = gr.Number(label="FPS de Saída (0 = original)", value=0)
                        ui['run_seedvr_btn'] = gr.Button("2. Iniciar Upscaling SeedVR", variant="primary", interactive=is_seedvr_available)
                    with gr.Column(scale=1):
                        ui['seedvr_video_output'] = gr.Video(label="Vídeo com Upscale SeedVR", interactive=False)
                        ui['seedvr_status_box'] = gr.Textbox(label="Status do SeedVR", value="Aguardando...", lines=3, interactive=False)

def _register_event_handlers(app_state: gr.State, ui: dict):
    """Registers all Gradio event handlers."""
    def update_seed_display(state):
        return state.get("used_seed", "N/A")

    gen_inputs = [
        ui['generation_mode'], ui['prompt'], ui['neg_prompt'], ui['start_image'],
        ui['height'], ui['width'], ui['duration'],
        ui['fp_num_inference_steps'], ui['fp_skip_initial_steps'], ui['fp_skip_final_steps'],
    ]
    gen_outputs = [ui['low_res_video_output'], app_state, ui['post_prod_group']]
    
    (ui['generate_low_btn'].click(fn=run_generate_base_video, inputs=gen_inputs, outputs=gen_outputs)
     .then(fn=update_seed_display, inputs=[app_state], outputs=[ui['used_seed_display']]))

    refine_inputs = [app_state, ui['prompt'], ui['neg_prompt']]
    refine_outputs = [ui['ltx_refined_video_output'], app_state]
    ui['ltx_refine_btn'].click(fn=run_ltx_refinement, inputs=refine_inputs, outputs=refine_outputs)
    
    if 'run_seedvr_btn' in ui and ui['run_seedvr_btn'].interactive:
        seedvr_inputs = [app_state, ui['seedvr_seed'], ui['seedvr_resolution'], ui['seedvr_batch_size'], ui['seedvr_fps']]
        seedvr_outputs = [ui['seedvr_video_output'], ui['seedvr_status_box']]
        ui['run_seedvr_btn'].click(fn=run_seedvr_upscaling, inputs=seedvr_inputs, outputs=seedvr_outputs)

# ==============================================================================
# --- PONTO DE ENTRADA DA APLICAÇÃO ---
# ==============================================================================
if __name__ == "__main__":
    log_level = os.environ.get("ADUC_LOG_LEVEL", "INFO").upper()
    logging.basicConfig(level=log_level, format='[%(levelname)s] [%(name)s] %(message)s')
    
    print("Building Gradio UI...")
    gradio_app = build_ui()
    print("Launching Gradio app...")
    gradio_app.queue().launch(
        server_name=os.getenv("GRADIO_SERVER_NAME", "0.0.0.0"), 
        server_port=int(os.getenv("GRADIO_SERVER_PORT", "7860")),
        show_error=True
    )