import re
import jaconv
import gradio as gr
from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel
from PIL import Image
import torch


tokenizer = AutoTokenizer.from_pretrained("kha-white/manga-ocr-base")

model = VisionEncoderDecoderModel.from_pretrained("kha-white/manga-ocr-base")

feature_extractor = AutoFeatureExtractor.from_pretrained("kha-white/manga-ocr-base")

examples = ["00.jpg", "01.jpg", "02.jpg", "03.jpg", "04.jpg", "05.jpg", "06.jpg", "07.jpg", "08.jpg", "09.jpg", "10.jpg", "11.jpg"]

def post_process(text):
  text = ''.join(text.split())
  text = text.replace('…', '...')
  text = re.sub('[・.]{2,}', lambda x: (x.end() - x.start()) * '.', text)
  text = jaconv.h2z(text, ascii=True, digit=True)
  return text

def manga_ocr(img):
  img = img.convert('L').convert('RGB')
  pixel_values = feature_extractor(img, return_tensors="pt").pixel_values
  output = model.generate(pixel_values)[0]
  text = tokenizer.decode(output, skip_special_tokens=True)
  text = post_process(text)
  return text

iface = gr.Interface(
    fn=manga_ocr,
    inputs=[gr.inputs.Image(label="Input", type="pil")],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="Manga OCR",
    description="Optical Character Recognization for Japanese Texts with focus on Mangas. The model is trained by kha-white with Github link: <a href=\"https://github.com/kha-white/manga-ocr\">manga-ocr</a> while the Space App is made by me.",
    allow_flagging='never',
    examples=examples,
    article = "Author: <a href=\"https://huggingface.co/gryan-galario\">Gryan Galario</a>",
)

iface.launch()