app_cuda.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-6.0: Eco + Déjà Vu) ---

# --- 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) ---

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-1.5-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}")

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-1.5-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}

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-1.5-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_media_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-1.5-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)
        
        with imageio.get_reader(memory_media_path) as reader:
            mem_img = Image.fromarray(reader.get_data(0))
            
        path_img, dest_img = Image.open(path_image_path), Image.open(destination_image_path)
        model_contents = ["START Image (from Kinetic Echo):", 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, eco_video_frames, use_attention_slicing,
    fragment_duration_frames, mid_cond_strength, 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.")
    if int(fragment_duration_frames) > video_total_frames:
        raise gr.Error(f"A 'Duração de Cada Fragmento' ({fragment_duration_frames} frames) não pode ser maior que a 'Duração da Geração Bruta' ({video_total_frames} frames).")

    log_history = "\n--- FASE 3/4: Iniciando Produção (Eco + Déjà Vu)...\n"
    yield {
        production_log_output: log_history, 
        video_gallery_glitch: [],
        prod_media_start_output: gr.update(value=None),
        prod_media_mid_output: gr.update(value=None, visible=False),
        prod_media_end_output: gr.update(value=None),
    }
    
    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"Preparando Fragmento {fragment_num}...")
            log_history += f"\n--- FRAGMENTO {fragment_num}/{num_transitions} ---\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, 0, 1.0), (destination_path, int(video_total_frames), 1.0)]
                
                yield {
                    production_log_output: gr.update(value=log_history),
                    prod_media_start_output: gr.update(value=start_path),
                    prod_media_mid_output: gr.update(value=None, visible=False),
                    prod_media_end_output: gr.update(value=destination_path),
                }
            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 (Vídeo): {os.path.basename(memory_path)}\n  - Caminho: {os.path.basename(path_path)}\n  - Destino: {os.path.basename(destination_path)}\n"
                
                mid_cond_frame_calculated = int(video_total_frames - fragment_duration_frames + eco_video_frames)
                log_history += f"  - Frame de Condicionamento do 'Caminho' calculado: {mid_cond_frame_calculated}\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, mid_cond_frame_calculated, mid_cond_strength), (destination_path, int(video_total_frames), 1.0)]

                yield {
                    production_log_output: gr.update(value=log_history),
                    prod_media_start_output: gr.update(value=memory_path),
                    prod_media_mid_output: gr.update(value=path_path, visible=True),
                    prod_media_end_output: gr.update(value=destination_path),
                }

            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}
            
            progress(i / num_transitions, desc=f"Filmando Fragmento {fragment_num}...")
            full_fragment_path, actual_frames_generated = 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
            )
            
            log_history += f"  - LOG: Gerei o fragmento_{fragment_num} bruto com {actual_frames_generated} frames.\n"
            yield {production_log_output: log_history}
            
            trimmed_fragment_path = os.path.join(WORKSPACE_DIR, f"fragment_{fragment_num}_trimmed.mp4")
            trim_video_to_frames(full_fragment_path, trimmed_fragment_path, int(fragment_duration_frames))
            
            log_history += f"  - LOG: Reduzi o fragmento_{fragment_num} para {int(fragment_duration_frames)} frames.\n"
            yield {production_log_output: log_history}
            
            is_last_fragment = (i == num_transitions - 1)
            if not is_last_fragment:
                eco_output_path = os.path.join(WORKSPACE_DIR, f"eco_from_frag_{fragment_num}.mp4")
                kinetic_memory_path = extract_last_n_frames_as_video(trimmed_fragment_path, eco_output_path, int(eco_video_frames))
                log_history += f"  - LOG: Gerei o eco com {int(eco_video_frames)} frames a partir do final do fragmento reduzido.\n"
                log_history += f"  - Novo Eco Cinético (Vídeo) criado: {os.path.basename(kinetic_memory_path)}\n"
            else:
                 log_history += f"  - Este é o último fragmento, não é necessário gerar um eco.\n"

            video_fragments.append(trimmed_fragment_path)
            yield {production_log_output: log_history, video_gallery_glitch: video_fragments}
            
        progress(1.0, desc="Produção Concluída.")
        log_history += "\nProdução de todos os fragmentos concluída.\n"
        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()

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:
    if media_path.lower().endswith(('.mp4', '.mov', '.avi')):
        with imageio.get_reader(media_path) as reader:
            first_frame_np = reader.get_data(0)
        temp_img_path = os.path.join(WORKSPACE_DIR, f"temp_frame_from_{os.path.basename(media_path)}.png")
        Image.fromarray(first_frame_np).save(temp_img_path)
        return load_image_to_tensor_with_resize_and_crop(temp_img_path, height, width)
    else:
        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 = 'cuda' if torch.cuda.is_available() else 'cpu'
    try:
        pipeline_instance.to(target_device)
        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[:, :, :actual_num_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()
        pipeline_instance.to('cpu')

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_n_frames_as_video(input_path: str, output_path: str, n_frames: int) -> str:
    try:
        cmd_probe = f"ffprobe -v error -select_streams v:0 -count_frames -show_entries stream=nb_read_frames -of default=nokey=1:noprint_wrappers=1 \"{input_path}\""
        result = subprocess.run(cmd_probe, shell=True, check=True, text=True, capture_output=True)
        total_frames = int(result.stdout.strip())
        
        if n_frames >= total_frames:
             shutil.copyfile(input_path, output_path)
             return output_path

        start_frame = total_frames - n_frames
        cmd_ffmpeg = f"ffmpeg -y -v error -i \"{input_path}\" -vf \"select='gte(n,{start_frame})'\" -vframes {n_frames} -an \"{output_path}\""
        subprocess.run(cmd_ffmpeg, shell=True, check=True, text=True)
        return output_path
    except (subprocess.CalledProcessError, ValueError) as e:
        raise gr.Error(f"FFmpeg falhou ao extrair os últimos {n_frames} frames: {getattr(e, 'stderr', str(e))}")

def concatenate_and_trim_masterpiece(fragment_paths: list, fragment_duration_frames: int, eco_video_frames: int, progress=gr.Progress()):
    if not fragment_paths: raise gr.Error("Nenhum fragmento de vídeo para concatenar.")
    progress(0.1, desc="Preparando fragmentos para montagem final...");
    
    try:
        list_file_path = os.path.join(WORKSPACE_DIR, "concat_list.txt")
        final_output_path = os.path.join(WORKSPACE_DIR, "masterpiece_final.mp4")
        temp_files_for_concat = []
        
        final_clip_len = int(fragment_duration_frames - eco_video_frames)

        for i, p in enumerate(fragment_paths):
            if i == len(fragment_paths) - 1:
                temp_files_for_concat.append(os.path.abspath(p))
                progress(0.1 + (i / len(fragment_paths)) * 0.8, desc=f"Mantendo último fragmento: {os.path.basename(p)}")
            else:
                temp_path = os.path.join(WORKSPACE_DIR, f"temp_concat_{i}.mp4")
                progress(0.1 + (i / len(fragment_paths)) * 0.8, desc=f"Cortando {os.path.basename(p)} para {final_clip_len} frames")
                trim_video_to_frames(p, temp_path, final_clip_len)
                temp_files_for_concat.append(temp_path)
        
        progress(0.9, desc="Concatenando clipes...")
        with open(list_file_path, "w") as f:
            for p_temp in temp_files_for_concat:
                f.write(f"file '{p_temp}'\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-6.0 (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 Geração Bruta (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)
                slicing_checkbox = gr.Checkbox(label="Usar Attention Slicing (Economiza VRAM)", value=True)
                gr.Markdown("---"); gr.Markdown("#### Controles de Duração (Arquitetura Eco + Déjà Vu)")
                fragment_duration_slider = gr.Slider(label="Duração de Cada Fragmento (Frames)", minimum=30, maximum=300, value=90, step=1)
                eco_frames_slider = gr.Slider(label="Tamanho do Eco Cinético (Frames)", minimum=4, maximum=48, value=8, step=1)
                mid_cond_strength_slider = gr.Slider(label="Força do 'Caminho'", minimum=0.1, maximum=1.0, value=0.5, step=0.05)
            gr.Markdown(
                """
                **Instruções (Nova Arquitetura):**
                - **Duração da Geração Bruta:** Tempo total que a IA tem para criar a transição. Deve ser MAIOR que a Duração do Fragmento.
                - **Duração de Cada Fragmento:** O comprimento final de cada clipe de vídeo que será gerado.
                - **Tamanho do Eco Cinético:** Quantos frames do *final* de um fragmento serão passados para o próximo para garantir continuidade.
                - **Força do Caminho:** Define o quão forte a imagem-chave intermediária ('Caminho') influencia a transição.
                """
            )
            animator_button = gr.Button("▶️ 3. Produzir Cenas (Handoff Cinético)", variant="primary")
            with gr.Accordion("Visualização das Mídias de Condicionamento (Ao Vivo)", open=True):
                with gr.Row():
                    prod_media_start_output = gr.Video(label="Mídia Inicial (Eco/K1)", interactive=False)
                    prod_media_mid_output = gr.Image(label="Mídia do Caminho (K_i-1)", interactive=False, visible=False)
                    prod_media_end_output = gr.Image(label="Mídia de Destino (K_i)", interactive=False)
            production_log_output = gr.Textbox(label="Diário de Bordo da Produção", lines=10, interactive=False)
        with gr.Column(scale=1): video_gallery_glitch = gr.Gallery(label="Fragmentos Gerados (Versões Cortadas)", object_fit="contain", height="auto", type="video")
    
    fragment_duration_state = gr.State()
    eco_frames_state = gr.State()
    
    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: Eco + Déjà Vu
        A geração começa com um "Big Bang" entre os dois primeiros keyframes. A partir daí, a mágica acontece.
        *   **O Eco (A Memória Física):** No final de cada cena, os últimos frames são capturados e salvos como um pequeno vídeo, o `Eco`. Ele carrega a "energia cinética" do movimento, iluminação e atmosfera da cena que acabou.
        *   **O Déjà Vu (A Memória Conceitual):** Para criar a próxima cena, o Cineasta de IA (Gemini) assiste ao `Eco`, olha para o keyframe do "caminho" e o keyframe do "destino". Com essa visão tripla, ele tem um "déjà vu", uma memória do que acabou de acontecer que o inspira a escrever uma instrução de câmera precisa para conectar o passado ao futuro de forma fluida e coerente.
        """
    )
    
    # --- 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=lambda frag_dur, eco_dur: (frag_dur, eco_dur),
        inputs=[fragment_duration_slider, eco_frames_slider],
        outputs=[fragment_duration_state, eco_frames_state]
    ).then(
        fn=run_video_production,
        inputs=[
            video_duration_slider, video_fps_slider, eco_frames_slider, slicing_checkbox,
            fragment_duration_slider, mid_cond_strength_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,
            prod_media_start_output, prod_media_mid_output, prod_media_end_output
        ]
    )
    
    editor_button.click(
        fn=concatenate_and_trim_masterpiece, 
        inputs=[fragment_list_state, fragment_duration_state, eco_frames_state], 
        outputs=[final_video_output]
    )

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