app.py

import gradio as gr
import cv2
import numpy as np
import os
import tempfile
import subprocess
import traceback
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
from gfpgan import GFPGANer
from basicsr.utils.download_util import load_file_from_url

# --- Model Loading (Unchanged) ---
model_cache = {}

def get_upsampler(model_name='realesr-general-x4v3'):
    if model_name in model_cache:
        return model_cache[model_name]
    if model_name == 'RealESRGAN_x4plus_anime_6B':
        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
        netscale = 4
        file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth'
    else: # realesr-general-x4v3
        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
        netscale = 4
        file_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
    model_path = load_file_from_url(url=file_url, model_dir='weights', progress=True)
    upsampler = RealESRGANer(
        scale=netscale, model_path=model_path, model=model,
        tile=64, tile_pad=10, pre_pad=10, half=True, gpu_id=None
    )
    model_cache[model_name] = upsampler
    return upsampler

def get_face_enhancer(upsampler, outscale):
    key = f'face_enhancer_{outscale}'
    if key in model_cache:
        return model_cache[key]
    face_enhancer = GFPGANer(
        model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
        upscale=outscale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler
    )
    model_cache[key] = face_enhancer
    return face_enhancer

# --- Core Video Processing Function (Unchanged) ---
def enhance_video(video_path, model_name, outscale, face_enhance, progress=gr.Progress(track_tqdm=True)):
    if not video_path:
        raise gr.Error("Please upload a video to enhance.")
    try:
        upsampler = get_upsampler(model_name)
        face_enhancer = None
        if face_enhance:
            face_enhancer = get_face_enhancer(upsampler, outscale)
        cap = cv2.VideoCapture(video_path)
        fps = cap.get(cv2.CAP_PROP_FPS)
        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        temp_dir = tempfile.mkdtemp()
        enhanced_video_path = os.path.join(temp_dir, "enhanced_video.mp4")
        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
        writer = cv2.VideoWriter(enhanced_video_path, fourcc, fps, (width * outscale, height * outscale))
        for _ in progress.tqdm(range(total_frames), desc="Enhancing Frames..."):
            ret, frame = cap.read()
            if not ret: break
            if face_enhancer:
                _, _, enhanced_frame = face_enhancer.enhance(frame, has_aligned=False, only_center_face=False, paste_back=True)
            else:
                enhanced_frame, _ = upsampler.enhance(frame, outscale=outscale)
            writer.write(enhanced_frame)
        cap.release()
        writer.release()
        final_output_path = os.path.join(temp_dir, "final_output_with_audio.mp4")
        audio_merge_cmd = f'ffmpeg -y -i "{enhanced_video_path}" -i "{video_path}" -c:v libx264 -crf 23 -preset fast -c:a aac -b:a 128k -map 0:v:0 -map 1:a:0 -shortest "{final_output_path}"'
        subprocess.call(audio_merge_cmd, shell=True, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL)
        return final_output_path
    except Exception as e:
        print(traceback.format_exc())
        raise gr.Error(f"An error occurred: {e}")

# --- Gradio UI with Corrected Layout ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet"), title="🎥 AI Video Enhancer") as demo:
    gr.Markdown(
        """
        Improve video quality, upscale resolution, and restore faces with cutting-edge AI.
        """
    )
    
    # Main two-column layout
    with gr.Row(variant="panel"):
        # --- Input Column on the Left ---
        with gr.Column(scale=1):
            video_input = gr.Video(label="🎬 Upload Your Video")

            # Accordion for less frequently used settings
            with gr.Accordion("Advanced Options", open=False):
                model_name = gr.Dropdown(
                    choices=["realesr-general-x4v3", "RealESRGAN_x4plus_anime_6B"], 
                    value="realesr-general-x4v3", 
                    label="Model Type (General or Anime)"
                )
                outscale = gr.Slider(1, 4, value=2, step=1, label="Upscale Factor")
        
        # --- Output Column on the Right ---
        with gr.Column(scale=1):
            video_output = gr.Video(label="🌟 Enhanced Result")
            
            # ✅ FIX: Controls are now placed directly under the output video
            face_enhance = gr.Checkbox(label="✨ Restore Faces (GFPGAN)", value=False, elem_id="face-enhance-checkbox")
            enhance_btn = gr.Button("🚀 Enhance Video", variant="primary")
            download_file = gr.File(label="⬇️ Download Enhanced Video", visible=False)

    # --- Event Logic (Unchanged) ---
    def on_submit(video, model, scale, face):
        output_path = enhance_video(video, model, scale, face)
        return output_path, gr.update(value=output_path, visible=True)

    enhance_btn.click(
        fn=on_submit, 
        inputs=[video_input, model_name, outscale, face_enhance], 
        outputs=[video_output, download_file]
    )

if __name__ == '__main__':
    demo.launch()