app7.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-5.4.1: Correção do Wrapper de Geração) ---

# --- 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")

VIDEO_FPS = 24
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-2.5-flash'); img = Image.open(initial_image_path)
    print("Gerando roteiro com análise de visão integrada...")
    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 ao criar o roteiro: {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-2.5-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, ref_images_tasks, progress=gr.Progress()):
    if not storyboard: raise gr.Error("Nenhum roteiro para gerar keyframes.")
    initial_ref_image_path = ref_images_tasks[0]['image']
    if not initial_ref_image_path or not os.path.exists(initial_ref_image_path): raise gr.Error("A imagem de referência principal (à esquerda) é obrigatória.")

    log_history = ""; generated_images_for_gallery = [] 
    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)
            
            reference_items = []
            fixed_references_basenames = [os.path.basename(item['image']) for item in ref_images_tasks if item['image']]
            
            for item in ref_images_tasks:
                if item['image']:
                    reference_items.append({'image_np': np.array(Image.open(item['image']).convert("RGB")), 'task': item['task']})
            
            dynamic_references_paths = keyframe_paths[-3:]
            for ref_path in dynamic_references_paths:
                if os.path.basename(ref_path) not in fixed_references_basenames:
                    reference_items.append({'image_np': np.array(Image.open(ref_path).convert("RGB")), 'task': 'ip'})

            log_history += f"  - Roteiro: '{scene_description}'\n  - Usando {len(reference_items)} referências visuais.\n  - 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=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: str, start_image_path: str, destination_image_path: str, dest_scene_desc: str):
    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-2.5-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-2.5-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(
    video_duration_seconds, video_fps, cut_frames_value, use_attention_slicing,
    mid_cond_frame, mid_cond_strength, end_cond_frame_offset, num_inference_steps,
    prompt_geral, keyframe_images_state, scene_storyboard, cfg, 
    progress=gr.Progress()
):
    video_total_frames = int(video_duration_seconds * video_fps)
    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 Controles Manuais...\n"
    yield {production_log_output: log_history, video_gallery_glitch: []}
    
    end_cond_frame = video_total_frames - end_cond_frame_offset
    seed = int(time.time())
    target_device = 'cuda' if torch.cuda.is_available() else 'cpu'
    try:
        pipeline_instance.to(target_device)
        video_fragments, story_history = [], ""; kinetic_memory_path = None
        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:
                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, int(0), 1.0), (destination_path, int(end_cond_frame), 1.0)]
            else:
                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 Cinética: {os.path.basename(memory_path)}\n  - Caminho: {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, int(0), 1.0), (path_path, int(mid_cond_frame), mid_cond_strength), (destination_path, int(end_cond_frame), 1.0)]

            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, progress=progress,
                video_total_frames=video_total_frames, video_fps=video_fps,
                use_attention_slicing=use_attention_slicing, num_inference_steps=num_inference_steps
            )
            
            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:
                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(cut_frames_value))
                eco_output_path = os.path.join(WORKSPACE_DIR, f"eco_from_frag_{fragment_num}.png")
                kinetic_memory_path = extract_last_frame_as_image(final_fragment_path, eco_output_path)
                log_history += f"  - Gerado e cortado. Novo Eco Dinâmico criado: {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, progress,
    video_total_frames, video_fps, use_attention_slicing, num_inference_steps
):
    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_attention_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)
        
        first_pass_config = PIPELINE_CONFIG_YAML.get("first_pass", {}).copy()
        first_pass_config['num_inference_steps'] = int(num_inference_steps)

        kwargs = {"prompt": motion_prompt, "negative_prompt": "blurry, distorted, bad quality, artifacts", "height": padded_h, "width": padded_w, "num_frames": actual_num_frames, "frame_rate": video_fps, "generator": torch.Generator(device=target_device).manual_seed(int(seed) + current_fragment_index), "output_type": "pt", "guidance_scale": float(cfg), "timesteps": first_pass_config.get("timesteps"), "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, "num_inference_steps": int(num_inference_steps)}
        
        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=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_attention_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("# NOVIM-5.4 (Painel de Controle do Diretor)\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)
    
    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("")

    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, 5, 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("Forneça referências para guiar a IA. A Principal é obrigatória. A Secundária é opcional (ex: para estilo ou uma segunda pessoa).")
            with gr.Row():
                with gr.Column():
                    ref1_image = gr.Image(label="Referência Principal (Conteúdo/ID)", type="filepath")
                    ref1_task = gr.Dropdown(choices=["ip", "id", "style"], value="ip", label="Tarefa da Ref. Principal")
                with gr.Column():
                    ref2_image = gr.Image(label="Referência Secundária (Opcional)", type="filepath")
                    ref2_task = gr.Dropdown(choices=["ip", "id", "style"], value="style", label="Tarefa da Ref. Secundária")
            photographer_button = gr.Button("▶️ 2. Pintar Imagens-Chave em Cadeia", variant="primary")
        with gr.Column(scale=1):
            keyframe_log_output = gr.Textbox(label="Diário de Bordo do Pintor", lines=15, interactive=False)
            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):
            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=6.0, step=0.5)
                video_fps_slider = gr.Slider(label="FPS do Vídeo", minimum=12, maximum=30, value=30, step=1)
                num_inference_steps_slider = gr.Slider(label="Etapas de Inferência", minimum=10, maximum=50, value=30, step=1)
                cut_frames_slider = gr.Slider(label="Ponto de Corte do Eco (Frames)", minimum=30, maximum=300, value=150, step=1)
                slicing_checkbox = gr.Checkbox(label="Usar Attention Slicing (Economiza VRAM)", value=True)
                gr.Markdown("---"); gr.Markdown("#### Controles de Condicionamento")
                mid_cond_frame_slider = gr.Slider(label="Frame do 'Caminho'", minimum=1, maximum=300, value=54, step=1)
                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 de Convergência do 'Destino' (frames do fim)", minimum=1, maximum=48, value=8, step=1)
            gr.Markdown(
                """
                **Instruções:**
                - **Etapas de Inferência:** Menos etapas = mais rápido, mas pode ter menos detalhes. Mais etapas = mais lento, mas com maior refinamento. O padrão (30) é um bom equilíbrio.
                - **Attention Slicing:** **Mantenha ativado** para evitar erros de memória, especialmente com cenas longas. Desative apenas se tiver muita VRAM e quiser a máxima "aderência" visual.
                """
            )
            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 (Editor)")
    editor_button = gr.Button("▶️ 4. Montar Vídeo Final", variant="primary")
    final_video_output = gr.Video(label="A Obra-Prima Final", width=TARGET_RESOLUTION)

    gr.Markdown(
        """
        ---
        ### A Arquitetura: Handoff Cinético & Big Bang
        A geração começa com um "Big Bang": a primeira transição de vídeo é entre o **Keyframe 1 e o Keyframe 2**. A imagem de referência original é usada apenas para criar o primeiro keyframe e depois é descartada do processo de vídeo.
        *   **O Bastão (O `Eco`):** Após a primeira transição, o último frame do clipe cortado (`Eco`) carrega a "energia cinética" da cena.
        *   **O Handoff (A Geração):** Os fragmentos seguintes começam a partir deste `Eco` dinâmico, herdando a "física" do movimento e da iluminação.
        *   **A Sincronização (Cineasta de IA):** Para cada Handoff, o Cineasta de IA (`Γ`) analisa o (`Eco`), o (`Keyframe` do caminho) e o (`Keyframe` do destino) para criar uma instrução de movimento precisa.
        """
    )
    
    # --- Ato 6: A Regência (Lógica de Conexão dos Botões) ---
    def process_and_update_storyboard(num_fragments, prompt, image_path):
        processed_path = process_image_to_square(image_path)
        if not processed_path: raise gr.Error("A imagem de referência é inválida ou não foi fornecida.")
        storyboard = run_storyboard_generation(num_fragments, prompt, processed_path)
        return storyboard, prompt, processed_path

    director_button.click(
        fn=process_and_update_storyboard, 
        inputs=[num_fragments_input, prompt_input, image_input], 
        outputs=[scene_storyboard_state, prompt_geral_state, processed_ref_path_state]
    ).success(
        fn=lambda s, p: (s, p),
        inputs=[scene_storyboard_state, processed_ref_path_state],
        outputs=[storyboard_to_show, ref1_image]
    )
    
    @photographer_button.click(
        inputs=[scene_storyboard_state, ref1_image, ref1_task, ref2_image, ref2_task],
        outputs=[keyframe_log_output, keyframe_gallery_output, keyframe_images_state]
    )
    def run_keyframe_generation_wrapper(storyboard, ref1_img, ref1_tsk, ref2_img, ref2_tsk, progress=gr.Progress()):
        ref_data = [{'image': ref1_img, 'task': ref1_tsk}, {'image': ref2_img, 'task': ref2_tsk}]
        yield from run_keyframe_generation(storyboard, ref_data, progress)

    animator_button.click(
        fn=run_video_production,
        inputs=[
            video_duration_slider, video_fps_slider, cut_frames_slider, slicing_checkbox,
            mid_cond_frame_slider, mid_cond_strength_slider, end_cond_offset_slider,
            num_inference_steps_slider,
            prompt_geral_state, keyframe_images_state, scene_storyboard_state, cfg_slider
        ],
        outputs=[production_log_output, video_gallery_glitch, fragment_list_state]
    )
    
    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)