app.py

import logging
import os

import gradio as gr
import numpy as np
from PIL import Image
from huggingface_hub import hf_hub_url, cached_download

from inference.face_detector import StatRetinaFaceDetector
from inference.model_pipeline import VSNetModelPipeline
from inference.onnx_model import ONNXModel

logging.basicConfig(
    format='%(asctime)s %(levelname)-8s %(message)s',
    level=logging.INFO,
    datefmt='%Y-%m-%d %H:%M:%S')

MODEL_IMG_SIZE = 256
def load_model():
    REPO_ID = "Podtekatel/ARCNEGAN"
    FILENAME = "arcane_exp_203_ep_281.onnx"

    global model
    global pipeline

    model_path = cached_download(
        hf_hub_url(REPO_ID, FILENAME), use_auth_token=os.getenv('HF_TOKEN')
    )
    model = ONNXModel(model_path)

    pipeline = VSNetModelPipeline(model, StatRetinaFaceDetector(MODEL_IMG_SIZE), background_resize=1024, no_detected_resize=1024)
    return model

load_model()

def inference(img):
    img = np.array(img)
    out_img = pipeline(img)
    out_img = Image.fromarray(out_img)
    return out_img


title = "ARCNStyleTransfer"
description = "Gradio Demo for Arcane Season 1 style transfer. To use it, simply upload your image, or click one of the examples to load them."
article = "This is one of my successful experiments on style transfer. I've built my own pipeline, generator model and private dataset to train this model<br>" \
          "" \
          "" \
          "" \
          "Model pipeline which used in project is improved CartoonGAN.<br>" \
          "This model was trained on RTX 2080 Ti 1.5 days with batch size 7.<br>" \
          "Model weights 64 MB in ONNX fp32 format, infers 25 ms on GPU and 150 ms on CPU at 256x256 resolution.<br>" \
          "If you want to use this app or integrate this model into yours, please contact me at email '[email protected]'."

imgs_folder = 'demo'
examples = [[os.path.join(imgs_folder, img_filename)] for img_filename in sorted(os.listdir(imgs_folder))]

demo = gr.Interface(
    fn=inference,
    inputs=[gr.inputs.Image(type="pil")],
    outputs=gr.outputs.Image(type="pil"),
    title=title,
    description=description,
    article=article,
    examples=examples)

demo.launch()