app_seedvr.py

# app_seedvr.py

import os
import sys
from pathlib import Path
from typing import Optional
import gradio as gr
import cv2
import multiprocessing as mp # <--- LINHA ADICIONADA AQUI

# --- INTEGRAÇÃO COM A LÓGICA DO SERVIDOR ---
try:
    from api.seedvr_server import SeedVRServer
except ImportError as e:
    print(f"ERRO FATAL: Não foi possível importar o SeedVRServer. Detalhes: {e}")
    raise

# --- INICIALIZAÇÃO ---
server = SeedVRServer()

# --- FUNÇÕES AUXILIARES DA UI ---

def _is_video(path: str) -> bool:
    """Verifica se um caminho de arquivo corresponde a um tipo de vídeo."""
    if not path: return False
    import mimetypes
    mime, _ = mimetypes.guess_type(path)
    return (mime or "").startswith("video")

def _extract_first_frame(video_path: str) -> Optional[str]:
    """Extrai o primeiro frame de um vídeo e o salva como uma imagem JPG."""
    if not video_path or not os.path.exists(video_path): return None
    try:
        vid_cap = cv2.VideoCapture(video_path)
        if not vid_cap.isOpened():
            print(f"Erro: Não foi possível abrir o vídeo em {video_path}")
            return None
        success, image = vid_cap.read()
        vid_cap.release()
        if not success:
            print(f"Erro: Não foi possível ler o primeiro frame de {video_path}")
            return None
        image_path = Path(video_path).with_suffix(".jpg")
        cv2.imwrite(str(image_path), image)
        return str(image_path)
    except Exception as e:
        print(f"Erro ao extrair o primeiro frame: {e}")
        return None

def on_file_upload(file_obj):
    """Callback acionado quando o usuário faz o upload de um arquivo."""
    if file_obj is None:
        return gr.update(value=1), None, None, None, gr.update(value=None, visible=False)
    
    if _is_video(file_obj.name):
        return gr.update(value=4, interactive=True), None, None, None, gr.update(value=None, visible=False)
    else:
        return gr.update(value=1, interactive=False), None, None, None, gr.update(value=None, visible=False)

# --- FUNÇÃO PRINCIPAL DE INFERÊNCIA DA UI ---

def run_inference_ui(
    input_file_path: Optional[str],
    resolution: str,
    sp_size: int,
    fps: float,
    progress=gr.Progress(track_tqdm=True)
):
    """
    A função de callback principal do Gradio.
    """
    yield (
        gr.update(interactive=False, value="Processing... 🚀"),
        gr.update(value=None, visible=False),
        gr.update(value=None, visible=False),
        gr.update(value=None, visible=False),
        gr.update(value="▶ Starting inference process...\n", visible=True)
    )

    if not input_file_path:
        gr.Warning("Please upload a media file first.")
        yield (gr.update(interactive=True, value="Restore Media"), None, None, None, gr.update(visible=False))
        return

    log_buffer = ["▶ Starting inference process...\n"]
    last_log_message = ""
    was_input_video = _is_video(input_file_path)

    try:
        def progress_callback_wrapper(step: float, desc: str):
            nonlocal last_log_message
            if desc != last_log_message:
                log_buffer.append(f"{desc}\n")
                last_log_message = desc
            progress(step, desc=desc)

        video_result_path = server.run_inference(
            file_path=input_file_path,
            seed=42,
            res_h=int(resolution),
            res_w=int(resolution),
            sp_size=int(sp_size),
            fps=float(fps) if fps and fps > 0 else None,
            progress=progress_callback_wrapper,
        )
        
        progress(1.0, desc="Complete!")
        log_buffer.append("✅ Inference complete! Processing final output...\n")
        
        final_image, final_video = None, None
        if was_input_video:
            final_video = video_result_path
            log_buffer.append("✅ Video result is ready.\n")
        else:
            final_image = _extract_first_frame(video_result_path)
            final_video = video_result_path
            log_buffer.append("✅ Image result extracted from video.\n")
        
        yield (
            gr.update(interactive=True, value="Restore Media"),
            gr.update(value=final_image, visible=final_image is not None),
            gr.update(value=final_video, visible=final_video is not None),
            gr.update(value=video_result_path, visible=video_result_path is not None),
            ''.join(log_buffer)
        )

    except Exception as e:
        error_message = f"❌ Inference failed: {e}"
        gr.Error(error_message)
        log_buffer.append(f"\n{error_message}")
        import traceback
        traceback.print_exc()
        
        yield (
            gr.update(interactive=True, value="Restore Media"),
            None, None, None,
            gr.update(value=''.join(log_buffer), visible=True)
        )

# --- LAYOUT DA INTERFACE GRÁFICA (GRADIO) ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="SeedVR Media Restoration") as demo:
    # ... (O layout da UI permanece exatamente o mesmo)
    gr.Markdown(
        """
        <div style='text-align: center; margin-bottom: 20px;'>
            <h1>📸 SeedVR - Image & Video Restoration 🚀</h1>
            <p>High-quality media upscaling powered by SeedVR-3B. Upload your file and see the magic.</p>
        </div>
        """
    )
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 1. Upload Media")
            input_media = gr.File(label="Input File (Video or Image)", type="filepath", interactive=True)
            gr.Markdown("### 2. Configure Settings")
            with gr.Accordion("Generation Parameters", open=True):
                resolution_select = gr.Dropdown(
                    label="Resolution",
                    choices=["480", "560", "720", "960", "1024"],
                    value="480",
                    info="Sets the output height and width to this value."
                )
                sp_size_slider = gr.Slider(
                    label="Frames per Batch (sp_size)",
                    minimum=1, maximum=16, step=1, value=4,
                    info="For multi-GPU videos. Automatically set to 1 for images."
                )
                fps_out = gr.Number(label="Output FPS (for Videos)", value=24, precision=0, info="Set to 0 to use the original FPS.")
            run_button = gr.Button("Restore Media", variant="primary", icon="✨")
        with gr.Column(scale=2):
            gr.Markdown("### 3. Results")
            log_window = gr.Textbox(
                label="Inference Log 📝", lines=8, max_lines=15,
                interactive=False, visible=False, autoscroll=True,
            )
            output_image = gr.Image(label="Image Result", show_download_button=True, type="filepath", visible=False)
            output_video = gr.Video(label="Video Result", visible=False)
            output_download = gr.File(label="Download Full Result (Video)", visible=False)
    gr.Markdown(
        """
        ---
        *Space and Docker were developed by Carlex.*
        *Contact: Email: Carlex22@gmail.com | GitHub: [carlex22](https://github.com/carlex22)*
        """
    )
    
    input_media.upload(
        fn=on_file_upload,
        inputs=[input_media],
        outputs=[sp_size_slider, output_image, output_video, output_download, log_window]
    )
    
    run_button.click(
        fn=run_inference_ui,
        inputs=[input_media, resolution_select, sp_size_slider, fps_out],
        outputs=[run_button, output_image, output_video, output_download, log_window],
    )

if __name__ == "__main__":
    # Garante que o start_method do multiprocessing seja 'spawn', que é mais seguro
    # e evita problemas de estado compartilhado entre processos.
    # É uma boa prática definir isso no ponto de entrada principal da aplicação.
    mp.set_start_method('spawn', force=True)
    
    demo.launch(
        server_name=os.getenv("GRADIO_SERVER_NAME", "0.0.0.0"),
        server_port=int(os.getenv("GRADIO_SERVER_PORT", "7860")),
        show_error=True
    )