app.py

# FILE: app_complete.py
# DESCRIPTION: Gradio web interface for the LTX-Video generation service.
#              Provides a user-friendly, step-by-step workflow for creating videos.

import gradio as gr
import traceback
import sys

# ==============================================================================
# --- BACKEND SERVICES IMPORT ---
# ==============================================================================

# Encapsulate imports in a try-except block for robust error handling at startup.
try:
    # This assumes the backend file is named 'ltx_server_refactored_complete.py'
    # and is in a reachable path (e.g., 'api/').
    from api.ltx_server_refactored_complete import video_generation_service
    
    # Placeholder for SeedVR server.
    # from api.seedvr_server import SeedVRServer
    # seedvr_inference_server = SeedVRServer()
    seedvr_inference_server = None 
    print("Backend services imported successfully.")
except ImportError as e:
    print(f"FATAL ERROR: Could not import backend services. Ensure the backend file is accessible. Details: {e}")
    sys.exit(1)
except Exception as e:
    print(f"FATAL ERROR: An unexpected error occurred during backend initialization. Details: {e}")
    sys.exit(1)

# ==============================================================================
# --- UI WRAPPER FUNCTIONS ---
# These functions act as a bridge between the Gradio UI and the backend service.
# They handle data conversion, error catching, and UI updates.
# ==============================================================================

def run_generate_base_video(
    generation_mode: str, prompt: str, neg_prompt: str, start_img: str, 
    height: int, width: int, duration: float, seed: int, randomize_seed: bool,
    fp_guidance_preset: str, fp_guidance_scale_list: str, fp_stg_scale_list: str,
    progress=gr.Progress(track_tqdm=True)
) -> tuple:
    """
    Wrapper to call the backend for generating the initial low-resolution video.
    It decides whether to use the 'narrative' or 'single' generation mode.
    """
    try:
        print(f"[UI] Request received for base video generation. Mode: {generation_mode}")
        
        initial_conditions = []
        if start_img:
            # Estimate total frames for conditioning context
            num_frames_estimate = int(duration * 24)
            items_list = [[start_img, 0, 1.0]] # [[media, frame, weight]]
            initial_conditions = video_generation_service.prepare_condition_items(
                items_list, height, width, num_frames_estimate
            )

        # Package advanced LTX settings for the backend
        ltx_configs = {
            "guidance_preset": fp_guidance_preset,
            "guidance_scale_list": fp_guidance_scale_list,
            "stg_scale_list": fp_stg_scale_list,
        }

        # Select the appropriate backend function based on UI mode
        if generation_mode == "Narrativa (Múltiplos Prompts)":
            func_to_call = video_generation_service.generate_narrative_low
        else:
            func_to_call = video_generation_service.generate_single_low

        video_path, tensor_path, final_seed = func_to_call(
            prompt=prompt, negative_prompt=neg_prompt,
            height=height, width=width, duration=duration,
            seed=None if randomize_seed else int(seed),
            initial_conditions=initial_conditions,
            ltx_configs_override=ltx_configs
        )
        
        if not video_path:
            raise RuntimeError("Backend failed to return a valid video path.")

        # Update the session state with the results
        new_state = {"low_res_video": video_path, "low_res_latents": tensor_path, "used_seed": final_seed}
        
        print(f"[UI] Base video generation successful. 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}"
        print(f"{error_message}\nDetails: {traceback.format_exc()}")
        raise gr.Error(error_message)


def run_ltx_refinement(
    state: dict, prompt: str, neg_prompt: str,
    progress=gr.Progress(track_tqdm=True)
) -> tuple:
    """Wrapper to call the LTX texture refinement and upscaling backend 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:
        print("[UI] Request received for LTX refinement.")
        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"]
        )
        # Update state with refined assets
        state["refined_video_ltx"] = video_path
        state["refined_latents_ltx"] = tensor_path
        print(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}"
        print(f"{error_message}\nDetails: {traceback.format_exc()}")
        raise gr.Error(error_message)


def run_seedvr_upscaling(
    state: dict, seed: int, resolution: int, batch_size: int, fps: int,
    progress=gr.Progress(track_tqdm=True)
) -> tuple:
    """Wrapper to call the SeedVR upscaling backend 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:
        print("[UI] Request received for SeedVR upscaling.")
        def progress_wrapper(p, desc=""): progress(p, desc=desc)
        
        output_filepath = seedvr_inference_server.run_inference(
            file_path=state["low_res_video"], seed=seed, resolution=resolution,
            batch_size=batch_size, fps=fps, progress=progress_wrapper
        )
        
        status_message = f"✅ Upscaling complete!\nSaved to: {output_filepath}"
        print(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}"
        print(f"{error_message}\nDetails: {traceback.format_exc()}")
        return None, gr.update(value=error_message)


# ==============================================================================
# --- UI BUILDER ---
# Functions dedicated to creating parts of the Gradio interface.
# ==============================================================================

def build_ui():
    """Constructs the entire Gradio application UI."""
    
    with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
        # App state persists across interactions within a session
        app_state = gr.State(value={"low_res_video": None, "low_res_latents": None, "used_seed": None})
        
        gr.Markdown("# LTX Video - Geração e Pós-Produção por Etapas", elem_id="main-title")
        
        ui_components = {} # Dictionary to hold all key UI components

        with gr.Row():
            with gr.Column(scale=1):
                # Build the main generation controls (Step 1)
                _build_generation_controls(ui_components)
            
            with gr.Column(scale=1):
                gr.Markdown("### Vídeo Base Gerado")
                ui_components['low_res_video_output'] = gr.Video(
                    label="O resultado da Etapa 1 aparecerá aqui", interactive=False
                )
        
        # Build the post-production section (Step 2), initially hidden
        _build_postprod_controls(ui_components)
        
        # Connect all UI events to their corresponding functions
        _register_event_handlers(app_state, ui_components)

    return demo

def _build_generation_controls(ui: dict):
    """Builds the UI components for Step 1: Base Video Generation."""
    gr.Markdown("### Etapa 1: 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 de cenas (uma 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=16, minimum=256, maximum=1024)
            ui['width'] = gr.Slider(label="Width", value=768, step=16, minimum=256, maximum=1024)
        with gr.Row():
            ui['seed'] = gr.Number(label="Seed", value=42, precision=0)
            ui['randomize_seed'] = gr.Checkbox(label="Randomize Seed", value=True)

    with gr.Accordion("Opções Avançadas de Guiagem (First Pass)", open=False):
        ui['fp_guidance_preset'] = gr.Dropdown(
            label="Preset de Guiagem",
            choices=["Padrão (Recomendado)", "Agressivo", "Suave", "Customizado"],
            value="Padrão (Recomendado)", 
            info="Controla como a guiagem de texto se comporta ao longo da 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):
    """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", elem_id="postprod-title")
        
        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("🔴 *O serviço SeedVR não está disponível nesta instância.*")
                
                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=1440, value=1072, step=8, label="Resolução Vertical")
                        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", value="Aguardando...", lines=3, interactive=False)


# ==============================================================================
# --- EVENT HANDLERS ---
# Connects UI component events (like clicks) to the wrapper functions.
# ==============================================================================

def _register_event_handlers(app_state: gr.State, ui: dict):
    """Registers all Gradio event handlers."""
    
    # --- Handler for custom guidance visibility ---
    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']
    )

    # --- Handler for the main "Generate" button ---
    gen_inputs = [
        ui['generation_mode'], ui['prompt'], ui['neg_prompt'], ui['start_image'],
        ui['height'], ui['width'], ui['duration'], ui['seed'], ui['randomize_seed'],
        ui['fp_guidance_preset'], ui['fp_guidance_scale_list'], ui['fp_stg_scale_list']
    ]
    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)

    # --- Handler for the LTX Refine button ---
    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)
    
    # --- Handler for the SeedVR Upscale button ---
    if 'run_seedvr_btn' in ui:
        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)


# ==============================================================================
# --- APPLICATION ENTRY POINT ---
# ==============================================================================

if __name__ == "__main__":
    print("Building Gradio UI...")
    gradio_app = build_ui()
    print("Launching Gradio app...")
    gradio_app.queue().launch(
        server_name="0.0.0.0", 
        server_port=7860, 
        debug=True, 
        show_error=True
    )