Delete app.py

app.py

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-# 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
-    )