Upload app (1).py

app (1).py

@@ -0,0 +1,262 @@
+# FILE: app.py
+# DESCRIPTION: Final Gradio web interface for the ADUC-SDR Video Suite.
+# Features dimension sliders locked to multiples of 8, a unified LTX workflow,
+# advanced controls, integrated SeedVR upscaling, and detailed debug logging.
+
+import gradio as gr
+import traceback
+import sys
+import os
+import logging
+
+# ==============================================================================
+# --- IMPORTAÇÃO DOS SERVIÇOS DE BACKEND E UTILS ---
+# ==============================================================================
+
+try:
+    # Serviço principal para geração LTX
+    from api.ltx_server_refactored_complete import video_generation_service
+    
+    # Nosso decorador de logging para depuração
+    from api.utils.debug_utils import log_function_io
+
+    # Serviço especialista para upscaling de resolução (SeedVR)
+    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, debug logger might be disabled. SeedVR might 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. The SeedVR upscaling tab will be disabled.")
+except Exception as e:
+    logging.critical(f"FATAL ERROR: An unexpected error occurred 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_guidance_preset: str, fp_guidance_scale_list: str, fp_stg_scale_list: str,
+    fp_num_inference_steps: int, fp_skip_initial_steps: int, fp_skip_final_steps: int,
+    progress=gr.Progress(track_tqdm=True)
+) -> tuple:
+    """Wrapper para a geração do vídeo base LTX."""
+    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 = {
+            "guidance_preset": fp_guidance_preset,
+            "guidance_scale_list": fp_guidance_scale_list,
+            "stg_scale_list": fp_stg_scale_list,
+            "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 para o refinamento de textura LTX."""
+    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 para o upscale de resolução SeedVR."""
+    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():
+    """Constrói a interface completa do Gradio."""
+    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):
+    """Constrói os componentes da UI para a Etapa 1: Geração."""
+    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)", info="Simples para uma ação contínua, Narrativa para uma sequência (uma cena por linha).")
+    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)
+        with gr.Tabs():
+            with gr.TabItem("Configurações de Guiagem (First Pass)"):
+                ui['fp_guidance_preset'] = gr.Dropdown(label="Preset de Guiagem", choices=["Padrão (Recomendado)", "Agressivo", "Suave", "Customizado"], value="Padrão (Recomendado)", info="Controla o comportamento da guiagem durante a difusão.")
+                with gr.Group(visible=False) as ui['custom_guidance_group']:
+                    gr.Markdown("⚠️ Edite as listas em formato JSON. Ex: `[1.0, 2.5, 3.0]`")
+                    ui['fp_guidance_scale_list'] = gr.Textbox(label="Lista de Guidance Scale", value="[1, 1, 6, 8, 6, 1, 1]")
+                    ui['fp_stg_scale_list'] = gr.Textbox(label="Lista de STG Scale (Movimento)", value="[0, 0, 4, 4, 4, 2, 1]")
+    
+    ui['generate_low_btn'] = gr.Button("1. Gerar Vídeo Base", variant="primary")
+
+def _build_postprod_controls(ui: dict):
+    """Constrói os componentes da UI para a Etapa 2: Pós-Produção."""
+    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):
+    """Registra todos os manipuladores de eventos do Gradio."""
+    def toggle_custom_guidance(preset_choice: str) -> gr.update:
+        return gr.update(visible=(preset_choice == "Customizado"))
+    
+    ui['fp_guidance_preset'].change(fn=toggle_custom_guidance, inputs=ui['fp_guidance_preset'], outputs=ui['custom_guidance_group'])
+
+    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_guidance_preset'], ui['fp_guidance_scale_list'], ui['fp_stg_scale_list'],
+        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
+    )