import gradio as gr
import torch
from model.models import UNet
from scripts.test_functions import process_image, process_video

window_size = 512
stride = 256
steps = 18
frame_count = 0

def get_model():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    unet_model = UNet().to(device)
    unet_model.load_state_dict(torch.load("model/best_unet_model.pth", map_location=device))
    unet_model.eval()
    return unet_model

unet_model = get_model()

def block_img(image, source_age, target_age):
    from PIL import Image as PILImage
    import numpy as np
    if isinstance(image, str):
        image = PILImage.open(image).convert('RGB')
    elif isinstance(image, np.ndarray) and image.dtype == object:
        image = image.astype(np.uint8)
    return process_image(unet_model, image, video=False, source_age=source_age,
                         target_age=target_age, window_size=window_size, stride=stride)

def block_img_vid(image, source_age):
    from PIL import Image as PILImage
    import numpy as np
    if isinstance(image, str):
        image = PILImage.open(image).convert('RGB')
    elif isinstance(image, np.ndarray) and image.dtype == object:
        image = image.astype(np.uint8)
    return process_image(unet_model, image, video=True, source_age=source_age,
                         target_age=0, window_size=window_size, stride=stride, steps=steps)

def block_vid(video_path, source_age, target_age):
    return process_video(unet_model, video_path, source_age, target_age,
                         window_size=window_size, stride=stride, frame_count=frame_count)

demo_img = gr.Interface(
    fn=block_img,
    inputs=[
        gr.Image(type="pil"),
        gr.Slider(10, 90, value=20, step=1, label="Current age", info="Choose your current age"),
        gr.Slider(10, 90, value=80, step=1, label="Target age", info="Choose the age you want to become")
    ],
    outputs="image",
    examples=[
        ['assets/gradio_example_images/1.png', 20, 80],
        ['assets/gradio_example_images/2.png', 75, 40],
        ['assets/gradio_example_images/3.png', 30, 70],
        ['assets/gradio_example_images/4.png', 22, 60],
        ['assets/gradio_example_images/5.png', 28, 75],
        ['assets/gradio_example_images/6.png', 35, 15]
    ],
    description="Input an image of a person and age them from the source age to the target age."
)

demo_img_vid = gr.Interface(
    fn=block_img_vid,
    inputs=[
        gr.Image(type="pil"),
        gr.Slider(10, 90, value=20, step=1, label="Current age", info="Choose your current age"),
    ],
    outputs=gr.Video(),
    examples=[
        ['assets/gradio_example_images/1.png', 20],
        ['assets/gradio_example_images/2.png', 75],
        ['assets/gradio_example_images/3.png', 30],
        ['assets/gradio_example_images/4.png', 22],
        ['assets/gradio_example_images/5.png', 28],
        ['assets/gradio_example_images/6.png', 35]
    ],
    description="Input an image of a person and a video will be returned of the person at different ages."
)

demo_vid = gr.Interface(
    fn=block_vid,
    inputs=[
        gr.Video(),
        gr.Slider(10, 90, value=20, step=1, label="Current age", info="Choose your current age"),
        gr.Slider(10, 90, value=80, step=1, label="Target age", info="Choose the age you want to become")
    ],
    outputs=gr.Video(),
    examples=[
        ['assets/gradio_example_images/orig.mp4', 35, 60],
    ],
    description="Input a video of a person, and it will be aged frame-by-frame."
)

demo = gr.TabbedInterface([demo_img, demo_img_vid, demo_vid],
                          tab_names=['Image inference demo', 'Image animation demo', 'Video inference demo'],
                          title="Face Re-Aging Demo",
                          )

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