app_seedvr.py

# app_seedvr.py

import os
import sys
from pathlib import Path
from typing import Optional
import gradio as gr
import cv2

# --- SERVER LOGIC INTEGRATION ---
try:
    from api.seedvr_server import SeedVRServer
except ImportError as e:
    print(f"FATAL ERROR: Could not import SeedVRServer. Details: {e}")
    raise

# --- INITIALIZATION ---
server = SeedVRServer()

# --- HELPER FUNCTIONS ---

def _is_video(path: str) -> bool:
    """Checks if a file path corresponds to a video type."""
    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]:
    """Extracts the first frame from a video and saves it as a JPG image."""
    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(): return None
        success, image = vid_cap.read()
        vid_cap.release()
        if not success: 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"Error extracting first frame: {e}")
        return None

def on_file_upload(file_obj):
    """Callback triggered when a user uploads a file."""
    if file_obj is None:
        return 1
    if _is_video(file_obj.name):
        return gr.update(value=4, interactive=True)
    else:
        return gr.update(value=1, interactive=False)

# --- CORE INFERENCE FUNCTION ---

def run_inference_ui(
    input_file_path: Optional[str],
    resolution: str,
    sp_size: int,
    fps: float,
    progress=gr.Progress(track_tqdm=True)
):
    """
    The main callback function for Gradio, using generators (`yield`)
    for real-time UI updates.
    """
    # 1. Initial State & Validation
    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="Waiting for logs...", 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"]
    yield gr.update(), None, None, None, ''.join(log_buffer)

    # CORREÇÃO APLICADA AQUI
    def progress_callback(step: float, desc: str):
        """A simple callback to append messages to our log buffer."""
        log_buffer.append(f"⏳ [{int(step*100)}%] {desc}\n")
        # A chamada correta para a API de progresso do Gradio
        progress(step, desc=desc)

    was_input_video = _is_video(input_file_path)

    try:
        # 2. Execute Inference
        progress_callback(0.1, "Calling backend engine...")
        yield gr.update(), None, None, None, ''.join(log_buffer)

        video_result_path = server.run_inference_direct(
            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,
        )
        
        progress_callback(1.0, "Inference complete! Processing final output...")
        yield gr.update(), None, None, None, ''.join(log_buffer)
        
        # 3. Process and Display Results
        final_image, final_video = None, None
        if was_input_video:
            final_video = video_result_path
            log_buffer.append(f"✅ Video result is ready.\n")
        else:
            final_image = _extract_first_frame(video_result_path)
            final_video = video_result_path
            log_buffer.append(f"✅ 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)
        print(error_message)
        import traceback
        traceback.print_exc()
        
        yield (
            gr.update(interactive=True, value="Restore Media"),
            None, None, None,
            gr.update(value=f"{''.join(log_buffer)}\n{error_message}", visible=True)
        )


# --- GRADIO UI LAYOUT ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="SeedVR Media Restoration") as demo:
    # Header
    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")
            gr.Markdown("### 2. Configure Settings")
            with gr.Accordion("Generation Parameters", open=True):
                resolution_select = gr.Dropdown(
                    label="Resolution (Short Edge)",
                    choices=["480", "560", "720", "960", "1024"],
                    value="480",
                    info="The output height and width will be set to this value."
                )
                sp_size_slider = gr.Slider(
                    label="Sequence Parallelism (sp_size)",
                    minimum=1, maximum=16, step=1, value=4,
                    info="For multi-GPU videos. This will be 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])
    
    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__":
    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
    )