import glob
import os
import io
import ffmpeg
import requests
from PIL import Image
import gradio as gr
import shutil
import concurrent.futures

def process_image(mask_data, image_path):
    image = Image.open(image_path)
    image_data = io.BytesIO()
    image.save(image_data, format=image.format)
    image_data = image_data.getvalue()

    # Prepare form data
    form_data = {
            'ldmSteps': 25,
            'ldmSampler': 'plms',
            'zitsWireframe': True,
            'hdStrategy': 'Original',
            'hdStrategyCropMargin': 196,
            'hdStrategyCropTrigerSize': 1280,
            'hdStrategyResizeLimit': 2048,
            'prompt': '',
            'negativePrompt': '',
            'croperX': -24,
            'croperY': -23,
            'croperHeight': 512,
            'croperWidth': 512,
            'useCroper': False,
            'sdMaskBlur': 5,
            'sdStrength': 0.75,
            'sdSteps': 50,
            'sdGuidanceScale': 7.5,
            'sdSampler': 'pndm',
            'sdSeed': 42,
            'sdMatchHistograms': False,
            'sdScale': 1,
            'cv2Radius': 5,
            'cv2Flag': 'INPAINT_NS',
            'paintByExampleSteps': 50,
            'paintByExampleGuidanceScale': 7.5,
            'paintByExampleSeed': 42,
            'paintByExampleMaskBlur': 5,
            'paintByExampleMatchHistograms': False,
            'sizeLimit': 1024,
        }

    files_data = {
        'image': (os.path.basename(image_path), image_data),
        'mask': ('mask.png', mask_data)
    }

    response = requests.post('https://ahmedghani-lama-cleaner-lama.hf.space/inpaint', data=form_data, files=files_data)

    if response.headers['Content-Type'] == 'image/jpeg' or response.headers['Content-Type'] == 'image/png':
        output_image_path = os.path.join('output_images', os.path.splitext(os.path.basename(image_path))[0] + '_inpainted' + os.path.splitext(image_path)[1])
        with open(output_image_path, 'wb') as output_image_file:
            output_image_file.write(response.content)
    else:
        print(f"Error processing {image_path}: {response.text}")

def remove_watermark(sketch, images_path='frames', output_path='output_images'):
    if os.path.exists('output_images'):
        shutil.rmtree('output_images')
    os.makedirs('output_images')

    mask_data = io.BytesIO()
    sketch["mask"].save(mask_data, format=sketch["mask"].format)
    mask_data = mask_data.getvalue()

    image_paths = glob.glob(f'{images_path}/*.*')

    with concurrent.futures.ThreadPoolExecutor() as executor:
        executor.map(lambda image_path: process_image(mask_data, image_path), image_paths)

    return gr.Video.update(value=convert_frames_to_video('output_images'), visible=True), gr.Button.update(value='Done!')

def convert_video_to_frames(video):
    print(f" input video is : {video}")
    if os.path.exists('input_video.mp4'):
        os.remove('input_video.mp4')

    ffmpeg.input(video).output('input_video.mp4').run()
    video_path = 'input_video.mp4'
    
    if os.path.exists('frames'):
        shutil.rmtree('frames')
    os.makedirs('frames')

    video_name = os.path.splitext(os.path.basename(video_path))[0]  
    ffmpeg.input(video_path).output(f'frames/{video_name}_%d.jpg', qscale=2).run()
    return gr.Image.update(value=f"{os.getcwd()}/frames/{video_name}_1.jpg", interactive=True), gr.Button.update(interactive=True)

def convert_frames_to_video(frames_path):
    if os.path.exists('output_video.mp4'):
        os.remove('output_video.mp4')
    
    (
        ffmpeg
        .input(f'{frames_path}/*.jpg', pattern_type='glob', framerate=25)
        .output('output_video.mp4')
        .run()
    )
    return gr.Video.update(value='output_video.mp4', visible=True, interactive=True), gr.Button.update(interactive=False)

css = """
    #remove_btn {
        background: linear-gradient(#201d18, #2bbbc3);
        font-weight: bold;
        font-size: 18px;
        color:white;
    }
    #remove_btn:hover {
        background: linear-gradient(#2bbbc3, #201d18);
    }
    footer {
        display: none !important;
    }
"""
demo = gr.Blocks(css=css, title="Video Watermark Remover")
with demo:
    gr.Markdown("""
    # <center>🎥 Video Watermark Remover</center>
    """)
    with gr.Row():
        with gr.Column():
            input_video = gr.Video(label="Upload a Video")
        with gr.Column():
            mask = gr.Image(label="Create a mask for the image", tool="sketch", type="pil", interactive=False)
    with gr.Row():
        with gr.Column():
            pass
        with gr.Column():
            remove_btn = gr.Button("Remove Watermark", interactive=False, elem_id="remove_btn")
        with gr.Column():
            pass
    
    output_video = gr.Video(label="Output Video", interactive=False)
    input_video.change(convert_video_to_frames, inputs=[input_video], outputs=[mask, remove_btn])
    remove_btn.click(remove_watermark, inputs=[mask], outputs=[output_video, remove_btn])

    #position:fixed;bottom:0;left:0;right:0;
    gr.Markdown("""## <center style="margin:20px;">Developed by Muhammad Ahmed<img src="https://avatars.githubusercontent.com/u/63394104?v=4" style="height:50px;width:50px;border-radius:50%;margin:5px;"></img></center>
    """)
demo.launch(show_api=False)