import os

import gradio as gr
import numpy as np
import torch
from PIL import Image
from bisnet import BiSeNet
from huggingface_hub import snapshot_download

from utils import vis_parsing_maps, decode_segmentation_masks, image_to_tensor

os.system("pip freeze")

REPO_ID = "leonelhs/faceparser"
MODEL_NAME = "79999_iter.pth"

model = BiSeNet(n_classes=19)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
snapshot_folder = snapshot_download(repo_id=REPO_ID)
model_path = os.path.join(snapshot_folder, MODEL_NAME)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()


def makeOverlay(image, mask):
    prediction_mask = np.asarray(mask)
    image = image.resize((512, 512), Image.BILINEAR)
    dark_map, overlay = vis_parsing_maps(image, prediction_mask)
    colormap = decode_segmentation_masks(dark_map)
    return overlay, colormap


def makeMask(image):
    with torch.no_grad():
        image = image.resize((512, 512), Image.BILINEAR)
        input_tensor = image_to_tensor(image)
        input_tensor = torch.unsqueeze(input_tensor, 0)
        if torch.cuda.is_available():
            input_tensor = input_tensor.cuda()
        output = model(input_tensor)[0]
        return output.squeeze(0).cpu().numpy().argmax(0)


def predict(image):
    mask = makeMask(image)
    overlay, colormap = makeOverlay(image, mask)
    return overlay


title = "Face Parser"
description = r"""
## Image face parser for research

This is an implementation of <a href='https://github.com/zllrunning/face-parsing.PyTorch' target='_blank'>face-parsing.PyTorch</a>.
It has no any particular purpose than start research on AI models.

"""

article = r"""
Questions, doubts, comments, please email 📧 `leonelhs@gmail.com`

This demo is running on a CPU, if you like this project please make us a donation to run on a GPU or just give us a <a href='https://github.com/leonelhs/zeroscratches/' target='_blank'>Github ⭐</a>

<a href="https://www.buymeacoffee.com/leonelhs"><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=leonelhs&button_colour=FFDD00&font_colour=000000&font_family=Cookie&outline_colour=000000&coffee_colour=ffffff" /></a>

<center><img src='https://visitor-badge.glitch.me/badge?page_id=zeroscratches.visitor-badge' alt='visitor badge'></center>
"""

demo = gr.Interface(
    predict, [
        gr.Image(type="pil", label="Input"),
    ], [
        gr.Image(type="numpy", label="Image face parsed")
    ],
    title=title,
    description=description,
    article=article)

demo.queue().launch()