app5.5.6.py

# Euia-AducSdr: Uma implementação aberta e funcional da arquitetura ADUC-SDR para geração de vídeo coerente.
# Copyright (C) 4 de Agosto de 2025  Carlos Rodrigues dos Santos
#
# Contato:
# Carlos Rodrigues dos Santos
# carlex22@gmail.com
# Rua Eduardo Carlos Pereira, 4125, B1 Ap32, Curitiba, PR, Brazil, CEP 8102025
#
# Repositórios e Projetos Relacionados:
# GitHub: https://github.com/carlex22/Aduc-sdr
# Hugging Face: https://huggingface.co/spaces/Carlexx/Ltx-SuperTime-60Secondos/
# Hugging Face: https://huggingface.co/spaces/Carlexxx/Novinho/
#
# Este programa é software livre: você pode redistribuí-lo e/ou modificá-lo
# sob os termos da Licença Pública Geral Affero da GNU como publicada pela
# Free Software Foundation, seja a versão 3 da Licença, ou
# (a seu critério) qualquer versão posterior.
#
# Este programa é distribuído na esperança de que seja útil,
# mas SEM QUALQUER GARANTIA; sem mesmo a garantia implícita de
# COMERCIALIZAÇÃO ou ADEQUAÇÃO A UM DETERMINADO FIM. Consulte a
# Licença Pública Geral Affero da GNU para mais detalhes.
#
# Você deve ter recebido uma cópia da Licença Pública Geral Affero da GNU
# junto com este programa. Se não, veja <https://www.gnu.org/licenses/>.

# --- app.py (NOVINHO-5.3-DEJAVU: Lógica de Handoff com "Eco Fantasma") ---

# --- Ato 1: A Convocação da Orquestra (Importações) ---
import gradio as gr
import torch
import os
import yaml
from PIL import Image, ImageOps, ExifTags
import shutil
import gc
import subprocess
import google.generativeai as genai
import numpy as np
import imageio
from pathlib import Path
import huggingface_hub
import json
import time

from inference import create_ltx_video_pipeline, load_image_to_tensor_with_resize_and_crop, ConditioningItem, calculate_padding
from dreamo_helpers import dreamo_generator_singleton

# --- Ato 2: A Preparação do Palco (Configurações) ---
config_file_path = "configs/ltxv-13b-0.9.8-distilled.yaml"
with open(config_file_path, "r") as file: PIPELINE_CONFIG_YAML = yaml.safe_load(file)

LTX_REPO = "Lightricks/LTX-Video"
models_dir = "downloaded_models_gradio"
Path(models_dir).mkdir(parents=True, exist_ok=True)
WORKSPACE_DIR = "aduc_workspace"
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")

# Valores padrão que agora podem ser sobrescritos pela UI
VIDEO_FPS_DEFAULT = 24
VIDEO_DURATION_SECONDS_DEFAULT = 8.0
TARGET_RESOLUTION = 420

print("Criando pipelines LTX na CPU (estado de repouso)...")
distilled_model_actual_path = huggingface_hub.hf_hub_download(repo_id=LTX_REPO, filename=PIPELINE_CONFIG_YAML["checkpoint_path"], local_dir=models_dir, local_dir_use_symlinks=False)
pipeline_instance = create_ltx_video_pipeline(
    ckpt_path=distilled_model_actual_path,
    precision=PIPELINE_CONFIG_YAML["precision"],
    text_encoder_model_name_or_path=PIPELINE_CONFIG_YAML["text_encoder_model_name_or_path"],
    sampler=PIPELINE_CONFIG_YAML["sampler"],
    device='cpu'
)
print("Modelos LTX prontos (na CPU).")


# --- Ato 3: As Partituras dos Músicos (Funções de Geração e Análise) ---

# --- Funções da ETAPA 1 (Roteiro) ---
def robust_json_parser(raw_text: str) -> dict:
    try:
        start_index = raw_text.find('{'); end_index = raw_text.rfind('}')
        if start_index != -1 and end_index != -1 and end_index > start_index:
            json_str = raw_text[start_index : end_index + 1]
            return json.loads(json_str)
        else: raise ValueError("Nenhum objeto JSON válido encontrado na resposta da IA.")
    except json.JSONDecodeError as e: raise ValueError(f"Falha ao decodificar JSON: {e}")

def extract_image_exif(image_path: str) -> str:
    try:
        img = Image.open(image_path); exif_data = img._getexif()
        if not exif_data: return "No EXIF metadata found."
        exif = { ExifTags.TAGS[k]: v for k, v in exif_data.items() if k in ExifTags.TAGS }
        relevant_tags = ['DateTimeOriginal', 'Model', 'LensModel', 'FNumber', 'ExposureTime', 'ISOSpeedRatings', 'FocalLength']
        metadata_str = ", ".join(f"{key}: {exif[key]}" for key in relevant_tags if key in exif)
        return metadata_str if metadata_str else "No relevant EXIF metadata found."
    except Exception: return "Could not read EXIF data."

def run_storyboard_generation(num_fragments: int, prompt: str, initial_image_path: str):
    if not initial_image_path: raise gr.Error("Por favor, forneça uma imagem de referência inicial.")
    if not GEMINI_API_KEY: raise gr.Error("Chave da API Gemini não configurada!")
    exif_metadata = extract_image_exif(initial_image_path)
    prompt_file = "prompts/unified_storyboard_prompt.txt"
    with open(os.path.join(os.path.dirname(__file__), prompt_file), "r", encoding="utf-8") as f: template = f.read()
    director_prompt = template.format(user_prompt=prompt, num_fragments=int(num_fragments), image_metadata=exif_metadata)
    genai.configure(api_key=GEMINI_API_KEY)
    model = genai.GenerativeModel('gemini-2.0-flash'); img = Image.open(initial_image_path)
    response = model.generate_content([director_prompt, img])
    try:
        storyboard_data = robust_json_parser(response.text)
        storyboard = storyboard_data.get("scene_storyboard", [])
        if not storyboard or len(storyboard) != int(num_fragments): raise ValueError(f"A IA não gerou o número correto de cenas. Esperado: {num_fragments}, Recebido: {len(storyboard)}")
        return storyboard
    except Exception as e: raise gr.Error(f"O Roteirista (Gemini) falhou: {e}. Resposta recebida: {response.text}")

# --- Funções da ETAPA 2 (Keyframes) ---
def get_dreamo_prompt_for_transition(previous_image_path: str, target_scene_description: str) -> str:
    genai.configure(api_key=GEMINI_API_KEY)
    prompt_file = "prompts/img2img_evolution_prompt.txt"
    with open(os.path.join(os.path.dirname(__file__), prompt_file), "r", encoding="utf-8") as f: template = f.read()
    director_prompt = template.format(target_scene_description=target_scene_description)
    model = genai.GenerativeModel('gemini-2.0-flash'); img = Image.open(previous_image_path)
    response = model.generate_content([director_prompt, "Previous Image:", img])
    return response.text.strip().replace("\"", "")

def run_keyframe_generation(storyboard, initial_ref_image_path, sequential_ref_task, *additional_refs_and_tasks, progress=gr.Progress()):
    if not storyboard: raise gr.Error("Nenhum roteiro para gerar keyframes.")
    if not initial_ref_image_path: raise gr.Error("A imagem de referência principal é obrigatória.")
    log_history = ""; generated_images_for_gallery = []
    base_reference_items = []
    num_pairs = len(additional_refs_and_tasks) // 2
    for i in range(num_pairs):
        img_path, task = additional_refs_and_tasks[i * 2], additional_refs_and_tasks[i * 2 + 1]
        if img_path: base_reference_items.append({'image_np': np.array(Image.open(img_path).convert("RGB")), 'task': task})
    try:
        pipeline_instance.to('cpu'); gc.collect(); torch.cuda.empty_cache()
        dreamo_generator_singleton.to_gpu()
        with Image.open(initial_ref_image_path) as img: width, height = (img.width // 32) * 32, (img.height // 32) * 32
        keyframe_paths, current_ref_image_path = [initial_ref_image_path], initial_ref_image_path
        for i, scene_description in enumerate(storyboard):
            progress(i / len(storyboard), desc=f"Pintando Keyframe {i+1}/{len(storyboard)}")
            log_history += f"\n--- PINTANDO KEYFRAME {i+1}/{len(storyboard)} ---\n"
            dreamo_prompt = get_dreamo_prompt_for_transition(current_ref_image_path, scene_description)
            recent_references_paths = keyframe_paths[-3:]
            sequential_reference_items = [{'image_np': np.array(Image.open(ref_path).convert("RGB")), 'task': sequential_ref_task} for ref_path in recent_references_paths]
            all_reference_items = base_reference_items + sequential_reference_items
            log_history += f"  - Roteiro: '{scene_description}'\n  - Usando {len(all_reference_items)} refs. Prompt do D.A.: \"{dreamo_prompt}\"\n"
            yield {keyframe_log_output: gr.update(value=log_history), keyframe_gallery_output: gr.update(value=generated_images_for_gallery)}
            output_path = os.path.join(WORKSPACE_DIR, f"keyframe_{i+1}.png")
            image = dreamo_generator_singleton.generate_image_with_gpu_management(reference_items=all_reference_items, prompt=dreamo_prompt, width=width, height=height)
            image.save(output_path)
            keyframe_paths.append(output_path); generated_images_for_gallery.append(output_path); current_ref_image_path = output_path
            yield {keyframe_log_output: gr.update(value=log_history), keyframe_gallery_output: gr.update(value=generated_images_for_gallery)}
    except Exception as e: raise gr.Error(f"O Pintor (DreamO) ou Diretor de Arte (Gemini) falhou: {e}")
    finally: dreamo_generator_singleton.to_cpu(); gc.collect(); torch.cuda.empty_cache()
    log_history += "\nPintura de todos os keyframes concluída.\n"
    yield {keyframe_log_output: gr.update(value=log_history), keyframe_gallery_output: gr.update(value=generated_images_for_gallery), keyframe_images_state: keyframe_paths}

# --- Funções da ETAPA 3 (Produção de Vídeo) ---
def get_initial_motion_prompt(user_prompt, start_image_path, destination_image_path, dest_scene_desc):
    if not GEMINI_API_KEY: raise gr.Error("Chave da API Gemini não configurada!")
    try:
        genai.configure(api_key=GEMINI_API_KEY)
        model = genai.GenerativeModel('gemini-2.0-flash')
        prompt_file = "prompts/initial_motion_prompt.txt"
        with open(os.path.join(os.path.dirname(__file__), prompt_file), "r", encoding="utf-8") as f: template = f.read()
        cinematographer_prompt = template.format(user_prompt=user_prompt, destination_scene_description=dest_scene_desc)
        start_img, dest_img = Image.open(start_image_path), Image.open(destination_image_path)
        model_contents = ["START Image:", start_img, "DESTINATION Image:", dest_img, cinematographer_prompt]
        response = model.generate_content(model_contents)
        return response.text.strip()
    except Exception as e: raise gr.Error(f"O Cineasta de IA (Inicial) falhou: {e}. Resposta: {getattr(e, 'text', 'No text available.')}")

def get_dynamic_motion_prompt(user_prompt, story_history, memory_image_path, path_image_path, destination_image_path, path_scene_desc, dest_scene_desc):
    if not GEMINI_API_KEY: raise gr.Error("Chave da API Gemini não configurada!")
    try:
        genai.configure(api_key=GEMINI_API_KEY)
        model = genai.GenerativeModel('gemini-2.0-flash')
        prompt_file = "prompts/dynamic_motion_prompt.txt"
        with open(os.path.join(os.path.dirname(__file__), prompt_file), "r", encoding="utf-8") as f: template = f.read()
        cinematographer_prompt = template.format(user_prompt=user_prompt, story_history=story_history, midpoint_scene_description=path_scene_desc, destination_scene_description=dest_scene_desc)
        mem_img, path_img, dest_img = Image.open(memory_image_path), Image.open(path_image_path), Image.open(destination_image_path)
        model_contents = ["START Image (Memory):", mem_img, "MIDPOINT Image (Path):", path_img, "DESTINATION Image (Destination):", dest_img, cinematographer_prompt]
        response = model.generate_content(model_contents)
        return response.text.strip()
    except Exception as e: raise gr.Error(f"O Cineasta de IA (Dinâmico) falhou: {e}. Resposta: {getattr(e, 'text', 'No text available.')}")

def run_video_production(prompt_geral, keyframe_images_state, scene_storyboard, seed, cfg,
                         video_duration, video_fps, num_inference_steps, handoff_point, use_slicing,
                         mid_cond_strength, end_cond_offset, end_cond_strength,
                         progress=gr.Progress()):
    if not keyframe_images_state or len(keyframe_images_state) < 3: raise gr.Error("Pinte pelo menos 2 keyframes para produzir uma transição.")
    log_history = "\n--- FASE 3/4: Iniciando Produção com Lógica 'Big Bang' e 'Eco Fantasma'...\n"
    yield {production_log_output: log_history, video_gallery_glitch: []}

    VIDEO_TOTAL_FRAMES = int(video_duration * video_fps)
    END_COND_FRAME = VIDEO_TOTAL_FRAMES - int(end_cond_offset)
    if int(handoff_point) >= END_COND_FRAME:
        raise gr.Error(f"Erro de timing: O 'Ponto de Handoff' ({handoff_point}) não pode ocorrer no mesmo frame ou depois do frame de 'Destino' ({END_COND_FRAME}). Aumente a duração, diminua o offset ou reduza o ponto de handoff.")

    target_device = 'cuda' if torch.cuda.is_available() else 'cpu'
    try:
        pipeline_instance.to(target_device)
        video_fragments, story_history = [], ""
        kinetic_memory_path = None # Esta será a nossa memória, o "Eco Fantasma"

        with Image.open(keyframe_images_state[1]) as img: width, height = img.size

        num_transitions = len(keyframe_images_state) - 2
        for i in range(num_transitions):
            fragment_num = i + 1
            progress(i / num_transitions, desc=f"Filmando Fragmento {fragment_num}/{num_transitions}")
            log_history += f"\n--- FRAGMENTO {fragment_num} ---\n"

            if i == 0: # Big Bang
                start_path, destination_path = keyframe_images_state[1], keyframe_images_state[2]
                dest_scene_desc = scene_storyboard[1]
                log_history += f"  - Início (Big Bang): {os.path.basename(start_path)}\n  - Destino: {os.path.basename(destination_path)}\n"
                current_motion_prompt = get_initial_motion_prompt(prompt_geral, start_path, destination_path, dest_scene_desc)
                conditioning_items_data = [(start_path, 0, 1.0), (destination_path, END_COND_FRAME, float(end_cond_strength))]
            else: # Handoff Cinético com "Eco Fantasma"
                memory_path, path_path, destination_path = kinetic_memory_path, keyframe_images_state[i+1], keyframe_images_state[i+2]
                path_scene_desc, dest_scene_desc = scene_storyboard[i], scene_storyboard[i+1]
                log_history += f"  - Memória (Eco Fantasma): {os.path.basename(memory_path)}\n  - Caminho (Déjà Vu): {os.path.basename(path_path)}\n  - Destino: {os.path.basename(destination_path)}\n"
                current_motion_prompt = get_dynamic_motion_prompt(prompt_geral, story_history, memory_path, path_path, destination_path, path_scene_desc, dest_scene_desc)
                conditioning_items_data = [(memory_path, 0, 1.0), (path_path, int(handoff_point), float(mid_cond_strength)), (destination_path, END_COND_FRAME, float(end_cond_strength))]

            story_history += f"\n- Ato {fragment_num + 1}: {current_motion_prompt}"
            log_history += f"  - Instrução do Cineasta: '{current_motion_prompt}'\n"; yield {production_log_output: log_history}

            full_fragment_path, _ = run_ltx_animation(
                current_fragment_index=fragment_num, motion_prompt=current_motion_prompt, conditioning_items_data=conditioning_items_data,
                width=width, height=height, seed=seed, cfg=cfg, video_total_frames=VIDEO_TOTAL_FRAMES, video_fps=video_fps,
                num_inference_steps=num_inference_steps, use_slicing=use_slicing, progress=progress
            )

            # *** LÓGICA DO ECO FANTASMA IMPLEMENTADA AQUI ***
            is_last_fragment = (i == num_transitions - 1)
            if is_last_fragment:
                final_fragment_path = full_fragment_path
                log_history += "  - Último fragmento gerado, mantendo a duração total para um final limpo.\n"
            else:
                # 1. Extrai o "Eco Fantasma" do vídeo COMPLETO para a PRÓXIMA geração
                eco_output_path = os.path.join(WORKSPACE_DIR, f"eco_fantasma_from_frag_{fragment_num}.png")
                kinetic_memory_path = extract_last_frame_as_image(full_fragment_path, eco_output_path)
                
                # 2. Corta o vídeo para a montagem FINAL
                final_fragment_path = os.path.join(WORKSPACE_DIR, f"fragment_{fragment_num}_trimmed.mp4")
                trim_video_to_frames(full_fragment_path, final_fragment_path, int(handoff_point))
                
                log_history += f"  - Gerado e cortado em {handoff_point} frames.\n  - Novo Eco Fantasma (Déjà Vu) criado para o próximo fragmento: {os.path.basename(kinetic_memory_path)}\n"

            video_fragments.append(final_fragment_path)
            yield {production_log_output: log_history, video_gallery_glitch: video_fragments}

        progress(1.0, desc="Produção Concluída.")
        yield {production_log_output: log_history, video_gallery_glitch: video_fragments, fragment_list_state: video_fragments}
    finally:
        pipeline_instance.to('cpu'); gc.collect(); torch.cuda.empty_cache()


# --- Funções Utilitárias e de Pós-Produção ---
def process_image_to_square(image_path: str, size: int = TARGET_RESOLUTION) -> str:
    if not image_path: return None
    try:
        img = Image.open(image_path).convert("RGB"); img_square = ImageOps.fit(img, (size, size), Image.Resampling.LANCZOS)
        output_path = os.path.join(WORKSPACE_DIR, f"initial_ref_{size}x{size}.png"); img_square.save(output_path)
        return output_path
    except Exception as e: raise gr.Error(f"Falha ao processar a imagem de referência: {e}")

def load_conditioning_tensor(media_path: str, height: int, width: int) -> torch.Tensor:
    return load_image_to_tensor_with_resize_and_crop(media_path, height, width)

def run_ltx_animation(current_fragment_index, motion_prompt, conditioning_items_data, width, height, seed, cfg,
                      video_total_frames, video_fps, num_inference_steps, use_slicing, progress=gr.Progress()):
    progress(0, desc=f"[Câmera LTX] Filmando Cena {current_fragment_index}...");
    output_path = os.path.join(WORKSPACE_DIR, f"fragment_{current_fragment_index}_full.mp4"); target_device = pipeline_instance.device
    try:
        if use_slicing: pipeline_instance.enable_attention_slicing()
        conditioning_items = [ConditioningItem(load_conditioning_tensor(p, height, width).to(target_device), s, t) for p, s, t in conditioning_items_data]
        actual_num_frames = int(round((float(video_total_frames) - 1.0) / 8.0) * 8 + 1)
        padded_h, padded_w = ((height - 1) // 32 + 1) * 32, ((width - 1) // 32 + 1) * 32
        padding_vals = calculate_padding(height, width, padded_h, padded_w)
        for item in conditioning_items: item.media_item = torch.nn.functional.pad(item.media_item, padding_vals)
        kwargs = {
            "prompt": motion_prompt, "negative_prompt": "blurry, distorted, bad quality, artifacts",
            "height": padded_h, "width": padded_w, "num_frames": actual_num_frames, "frame_rate": int(video_fps),
            "generator": torch.Generator(device=target_device).manual_seed(int(seed) + current_fragment_index),
            "output_type": "pt", "guidance_scale": float(cfg), "timesteps": int(num_inference_steps),
            "conditioning_items": conditioning_items, "decode_timestep": PIPELINE_CONFIG_YAML.get("decode_timestep"),
            "decode_noise_scale": PIPELINE_CONFIG_YAML.get("decode_noise_scale"), "stochastic_sampling": PIPELINE_CONFIG_YAML.get("stochastic_sampling"),
            "image_cond_noise_scale": 0.15, "is_video": True, "vae_per_channel_normalize": True,
            "mixed_precision": (PIPELINE_CONFIG_YAML.get("precision") == "mixed_precision"), "enhance_prompt": False, "decode_every": 4
        }
        result_tensor = pipeline_instance(**kwargs).images
        pad_l, pad_r, pad_t, pad_b = map(int, padding_vals); slice_h = -pad_b if pad_b > 0 else None; slice_w = -pad_r if pad_r > 0 else None
        cropped_tensor = result_tensor[:, :, :video_total_frames, pad_t:slice_h, pad_l:slice_w]
        video_np = (cropped_tensor[0].permute(1, 2, 3, 0).cpu().float().numpy() * 255).astype(np.uint8)
        with imageio.get_writer(output_path, fps=int(video_fps), codec='libx264', quality=8) as writer:
            for i, frame in enumerate(video_np): writer.append_data(frame)
        return output_path, actual_num_frames
    finally:
        if use_slicing: pipeline_instance.disable_attention_slicing()

def trim_video_to_frames(input_path: str, output_path: str, frames_to_keep: int) -> str:
    try:
        subprocess.run(f"ffmpeg -y -v error -i \"{input_path}\" -vf \"select='lt(n,{frames_to_keep})'\" -an \"{output_path}\"", shell=True, check=True, text=True)
        return output_path
    except subprocess.CalledProcessError as e: raise gr.Error(f"FFmpeg falhou ao cortar vídeo: {e.stderr}")

def extract_last_frame_as_image(video_path: str, output_image_path: str) -> str:
    try:
        subprocess.run(f"ffmpeg -y -v error -sseof -1 -i \"{video_path}\" -update 1 -q:v 1 \"{output_image_path}\"", shell=True, check=True, text=True)
        return output_image_path
    except subprocess.CalledProcessError as e: raise gr.Error(f"FFmpeg falhou ao extrair último frame: {e.stderr}")

def concatenate_and_trim_masterpiece(fragment_paths: list, progress=gr.Progress()):
    if not fragment_paths: raise gr.Error("Nenhum fragmento de vídeo para concatenar.")
    progress(0.5, desc="Montando a obra-prima final...");
    try:
        list_file_path = os.path.join(WORKSPACE_DIR, "concat_list.txt"); final_output_path = os.path.join(WORKSPACE_DIR, "masterpiece_final.mp4")
        with open(list_file_path, "w") as f:
            for p in fragment_paths: f.write(f"file '{os.path.abspath(p)}'\n")
        subprocess.run(f"ffmpeg -y -v error -f concat -safe 0 -i \"{list_file_path}\" -c copy \"{final_output_path}\"", shell=True, check=True, text=True)
        progress(1.0, desc="Montagem concluída!")
        return final_output_path
    except subprocess.CalledProcessError as e: raise gr.Error(f"FFmpeg falhou na concatenação final: {e.stderr}")

# --- Ato 5: A Interface com o Mundo (UI) ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# NOVINHO-5.3 (Déjà Vu)\n*By Carlex & Gemini & DreamO*")

    if os.path.exists(WORKSPACE_DIR): shutil.rmtree(WORKSPACE_DIR)
    os.makedirs(WORKSPACE_DIR); Path("prompts").mkdir(exist_ok=True)

    # State variables
    scene_storyboard_state, keyframe_images_state, fragment_list_state = gr.State([]), gr.State([]), gr.State([])
    prompt_geral_state, processed_ref_path_state = gr.State(""), gr.State("")
    MAX_ADDITIONAL_REFS = 4

    gr.Markdown("--- \n ## ETAPA 1: O ROTEIRO (IA Roteirista)")
    with gr.Row():
        with gr.Column(scale=1):
            prompt_input = gr.Textbox(label="Ideia Geral (Prompt)")
            num_fragments_input = gr.Slider(2, 10, 4, step=1, label="Número de Atos (Keyframes)")
            image_input = gr.Image(type="filepath", label=f"Imagem de Referência Principal (será {TARGET_RESOLUTION}x{TARGET_RESOLUTION})")
            director_button = gr.Button("▶️ 1. Gerar Roteiro", variant="primary")
        with gr.Column(scale=2):
            storyboard_to_show = gr.JSON(label="Roteiro de Cenas Gerado (em Inglês)")

    gr.Markdown("--- \n ## ETAPA 2: OS KEYFRAMES (IA Pintor & Diretor de Arte)")
    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("O Pintor usará as referências abaixo + as **3 últimas imagens** geradas para criar a próxima.")
            with gr.Group():
                ref1_image = gr.Image(label="Referência Principal (Automática da Etapa 1)", type="filepath", interactive=False)
                ref1_task = gr.Dropdown(choices=["ip", "id", "style"], value="ip", label="Tarefa das Referências em Cadeia")
            additional_ref_images, additional_ref_tasks = [], []
            with gr.Accordion("Referências Adicionais do Pintor (Opcional)", open=False):
                with gr.Tabs():
                    for i in range(MAX_ADDITIONAL_REFS):
                        with gr.TabItem(f"Ref. Extra {i+1}"):
                            with gr.Column():
                                ref_img = gr.Image(label=f"Imagem de Referência Extra {i+1}", type="filepath", scale=2)
                                ref_task_dd = gr.Dropdown(choices=["ip", "id", "style"], value="style", label=f"Tarefa da Ref. Extra {i+1}")
                                additional_ref_images.append(ref_img)
                                additional_ref_tasks.append(ref_task_dd)
            photographer_button = gr.Button("▶️ 2. Pintar Imagens-Chave em Cadeia", variant="primary")
            keyframe_log_output = gr.Textbox(label="Diário de Bordo do Pintor", lines=10, interactive=False)
        with gr.Column(scale=1):
            keyframe_gallery_output = gr.Gallery(label="Imagens-Chave Pintadas", object_fit="contain", height="auto", type="filepath")

    gr.Markdown("--- \n ## ETAPA 3: A PRODUÇÃO (IA Cineasta & Câmera)")
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Row():
                seed_number = gr.Number(42, label="Seed")
                cfg_slider = gr.Slider(1.0, 10.0, 2.5, step=0.1, label="CFG")
            with gr.Accordion("Controles Avançados de Timing e Performance", open=False):
                video_duration_slider = gr.Slider(label="Duração da Cena (segundos)", minimum=2.0, maximum=10.0, value=VIDEO_DURATION_SECONDS_DEFAULT, step=0.5)
                video_fps_slider = gr.Slider(label="FPS do Vídeo", minimum=12, maximum=30, value=VIDEO_FPS_DEFAULT, step=1)
                num_inference_steps_slider = gr.Slider(label="Etapas de Inferência", minimum=10, maximum=50, value=30, step=1)
                handoff_point_slider = gr.Slider(label="Ponto de Handoff (Frames)", minimum=30, maximum=300, value=150, step=1, info="Define o corte do vídeo para a montagem final.")
                slicing_checkbox = gr.Checkbox(label="Usar Attention Slicing (Economiza VRAM)", value=True)
                gr.Markdown("---"); gr.Markdown("#### Controles de Condicionamento")
                mid_cond_strength_slider = gr.Slider(label="Força do 'Caminho'", minimum=0.1, maximum=1.0, value=0.5, step=0.05)
                end_cond_offset_slider = gr.Slider(label="Offset do 'Destino' (frames do fim)", minimum=1, maximum=48, value=8, step=1, info="Define quão cedo o vídeo converge para o destino e qual frame será o 'Eco Fantasma'.")
                end_cond_strength_slider = gr.Slider(label="Força do 'Destino'", minimum=0.1, maximum=1.0, value=1.0, step=0.05)
            gr.Markdown(
                """
                **Instruções (Lógica 'Eco Fantasma'):**
                - O `Eco Fantasma` (a memória do futuro) é extraído do último frame do vídeo *completo*, antes do corte.
                - Este `Eco` se torna o ponto de partida para o próximo fragmento, garantindo máxima continuidade.
                - O `Ponto de Handoff` define o frame de corte para a montagem e onde o `Keyframe` seguinte ('Caminho') será posicionado no tempo.
                """
            )
            animator_button = gr.Button("▶️ 3. Produzir Cenas (Handoff Cinético)", variant="primary")
            production_log_output = gr.Textbox(label="Diário de Bordo da Produção", lines=15, interactive=False)
        with gr.Column(scale=1):
            video_gallery_glitch = gr.Gallery(label="Fragmentos Gerados", object_fit="contain", height="auto", type="video")

    gr.Markdown(f"--- \n ## ETAPA 4: PÓS-PRODUÇÃO (IA Editor)")
    editor_button = gr.Button("▶️ 4. Montar Vídeo Final", variant="primary")
    final_video_output = gr.Video(label="A Obra-Prima Final", width=TARGET_RESOLUTION)

    # --- Event Handlers ---
    director_button.click(fn=run_storyboard_generation, inputs=[num_fragments_input, prompt_input, image_input], outputs=[scene_storyboard_state]).success(fn=lambda s, p: (s, p), inputs=[scene_storyboard_state, prompt_input], outputs=[storyboard_to_show, prompt_geral_state]).success(fn=process_image_to_square, inputs=[image_input], outputs=[processed_ref_path_state]).success(fn=lambda p: p, inputs=[processed_ref_path_state], outputs=[ref1_image])

    photographer_button_inputs = [scene_storyboard_state, ref1_image, ref1_task]
    for i in range(MAX_ADDITIONAL_REFS):
        photographer_button_inputs.append(additional_ref_images[i])
        photographer_button_inputs.append(additional_ref_tasks[i])
    photographer_button.click(fn=run_keyframe_generation, inputs=photographer_button_inputs, outputs=[keyframe_log_output, keyframe_gallery_output, keyframe_images_state])

    animator_button_inputs = [prompt_geral_state, keyframe_images_state, scene_storyboard_state, seed_number, cfg_slider,
                              video_duration_slider, video_fps_slider, num_inference_steps_slider, handoff_point_slider, slicing_checkbox,
                              mid_cond_strength_slider, end_cond_offset_slider, end_cond_strength_slider]
    animator_button_outputs = [production_log_output, video_gallery_glitch, fragment_list_state]
    animator_button.click(fn=run_video_production, inputs=animator_button_inputs, outputs=animator_button_outputs)

    editor_button.click(fn=concatenate_and_trim_masterpiece, inputs=[fragment_list_state], outputs=[final_video_output])

if __name__ == "__main__":
    demo.queue().launch(server_name="0.0.0.0", share=True)