import gradio as gr

from PIL import Image
import cv2 as cv
import torch
from RealESRGAN import RealESRGAN
import tempfile
import numpy as np
import tqdm
import ffmpeg
import spaces


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

@spaces.GPU(duration=60)
def infer_image(img: Image.Image, size_modifier: int ) -> Image.Image:
    if img is None:
        raise Exception("Image not uploaded")
    
    width, height = img.size
    
    if width >= 5000 or height >= 5000:
        raise Exception("The image is too large.")

    model = RealESRGAN(device, scale=size_modifier)
    model.load_weights(f'weights/RealESRGAN_x{size_modifier}.pth', download=False)

    result = model.predict(img.convert('RGB'))
    print(f"Image size ({device}): {size_modifier} ... OK")
    return result

@spaces.GPU(duration=120)
def infer_video(video_filepath: str, size_modifier: int) -> str:
    model = RealESRGAN(device, scale=size_modifier)
    model.load_weights(f'weights/RealESRGAN_x{size_modifier}.pth', download=False)

    cap = cv.VideoCapture(video_filepath)
    
    tmpfile = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
    vid_output = tmpfile.name
    tmpfile.close()

    # Check if the input video has an audio stream
    probe = ffmpeg.probe(video_filepath)
    has_audio = any(stream['codec_type'] == 'audio' for stream in probe['streams'])

    if has_audio:
        # Extract audio from the input video
        audio_file = video_filepath.replace(".mp4", ".wav")
        ffmpeg.input(video_filepath).output(audio_file, format='wav', ac=1).run(overwrite_output=True)

    vid_writer = cv.VideoWriter(
        vid_output,
        fourcc=cv.VideoWriter.fourcc(*'mp4v'),
        fps=cap.get(cv.CAP_PROP_FPS),
        frameSize=(int(cap.get(cv.CAP_PROP_FRAME_WIDTH)) * size_modifier, int(cap.get(cv.CAP_PROP_FRAME_HEIGHT)) * size_modifier)
    )

    n_frames = int(cap.get(cv.CAP_PROP_FRAME_COUNT))

    for _ in tqdm.tqdm(range(n_frames)):
        ret, frame = cap.read()
        if not ret:
            break

        frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
        frame = Image.fromarray(frame)

        upscaled_frame = model.predict(frame.convert('RGB'))
        
        upscaled_frame = np.array(upscaled_frame)
        upscaled_frame = cv.cvtColor(upscaled_frame, cv.COLOR_RGB2BGR)

        vid_writer.write(upscaled_frame)

    vid_writer.release()

    if has_audio:
        # Re-encode the video with the modified audio
        ffmpeg.input(vid_output).output(video_filepath.replace(".mp4", "_upscaled.mp4"), vcodec='libx264', acodec='aac', audio_bitrate='320k').run(overwrite_output=True)

        # Replace the original audio with the upscaled audio
        ffmpeg.input(audio_file).output(video_filepath.replace(".mp4", "_upscaled.mp4"), acodec='aac', audio_bitrate='320k').run(overwrite_output=True)

    print(f"Video file : {video_filepath}")

    return vid_output.replace(".mp4", "_upscaled.mp4") if has_audio else vid_output

input_image = gr.Image(type='pil', label='Input Image')
input_model_image = gr.Radio([('x2', 2), ('x4', 4), ('x8', 8)], type="value", value=4, label="Model Upscale/Enhance Type")
submit_image_button = gr.Button('Submit')
output_image = gr.Image(type="filepath", label="Output Image")

tab_img = gr.Interface(
    fn=infer_image,
    inputs=[input_image, input_model_image],
    outputs=output_image,
    title="Real-ESRGAN",
    description="Gradio UI for Real-ESRGAN Pytorch version. To use it, simply upload your image and choose the model. Read more at the links below. Please click submit only once <br>Credits: [Nick088](https://linktr.ee/Nick088), Xinntao, Tencent, Geeve George, ai-forever, daroche <br><p style='text-align: center'><a href='https://github.com/Nick088/Real-ESRGAN_Pytorch'>Github Repo</a></p>"
)

input_video = gr.Video(label='Input Video')
input_model_video = gr.Radio([('x2', 2), ('x4', 4), ('x8', 8)], type="value", value=2, label="Model Upscale/Enhance Type")
submit_video_button = gr.Button('Submit')
output_video = gr.Video(label='Output Video', autoplay = True, loop = True)

tab_vid = gr.Interface(
    fn=infer_video,
    inputs=[input_video, input_model_video],
    outputs=output_video,
    title="Real-ESRGAN",
    description="Gradio UI for Real-ESRGAN Pytorch version. To use it, simply upload your video and choose the model. Read more at the links below. Please click submit only once <br>Credits: [Nick088](https://linktr.ee/Nick088), Xinntao, Tencent, Geeve George, ai-forever, daroche <br><p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/ai-forever/Real-ESRGAN'>Github Repo</a></p>",
    examples=[
        [
            "RealESRGAN_examples/Example1.mp4",
            2
        ]
    ],
    cache_examples=True,
    cache_mode='lazy'
)

demo = gr.TabbedInterface([tab_img, tab_vid], ["Image", "Video"])

demo.launch(mcp_server=True, debug=True, show_error=True, share=True)