import gradio as gr
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import requests, re, base64, string, random
from PIL import Image, ImageEnhance
from io import BytesIO
import os
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-small-printed")
model = VisionEncoderDecoderModel.from_pretrained("jonahgoldberg/bk_wht_8kun")

def random_string(string_length):
  input = string.ascii_lowercase + string.digits
  return ''.join(random.choice(input) for i in range(string_length))


# # load image examples
# urls = [
#     'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/nfcb5.png',
#     'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/p57fn.png',
#     'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/w2yp7.png',
#     'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/pme86.png',
#     'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/w4nfx.png',
#     'https://storage.googleapis.com/trocr-captcha.appspot.com/captcha_images_v2/nf8b8.png'
# ]
# for idx, url in enumerate(urls):
#   image = Image.open(requests.get(url, stream=True).raw)
#   image.save(f"image_{idx}.png")
def execit(command):
    return os.system(command)
###git add *.txt && git add *.py && git commit -m "lol" && git push
###git add *.txt && git add *.py && git commit -m "lol" && git push
###git add *.txt && git add *.py && git commit -m "lol" && git push
def process_image(image):
    # prepare image
    image_data = re.sub('^data:image/.+;base64,', '', image)
    im = Image.open(BytesIO(base64.b64decode(image_data))).convert("RGB")

    filter = ImageEnhance.Color(im)
    im = filter.enhance(0)

    # input_location = f"{random_string(9)}.png"
    # outputfile_tmp = f"{random_string(9)}.png"
    # outputfile_usable = f"{random_string(9)}.png"
    # execit("input_location="+input_location)
    # execit("outputfile_tmp="+outputfile_tmp)
    # execit("outputfile_usable="+outputfile_usable)
    # im.save(input_location, "png")
    # execit('''gegl -x "<?xml version='1.0' encoding='UTF-8'?> <gegl> <node operation='gegl:ripple'> <params> <param name='amplitude'>9.9</param> <param name='period'>125.0</param> <param name='sampler-type'>nearest</param> <param name='abyss-policy'>none</param> </params> </node> <node operation='gegl:brightness-contrast'> <params> <param name='contrast'>5</param> <param name='brightness'>-1.0</param> </params> </node> <node operation='gegl:c2g'/> <node operation='gegl:load'> <params> <param name='path'>"$input_location"</param> </params> </node> </gegl>"           -o $outputfile_tmp''')
    # execit('convert $outputfile_tmp -background white -alpha remove -alpha off $outputfile_usable')


    
    #Take's the picture
    pixel_values = processor(im, return_tensors="pt").pixel_values

    # generate (no beam search)
    generated_ids = model.generate(pixel_values)

    # os.remove(input_location)
    # os.remove(outputfile_tmp)
    # os.remove(outputfile_usable)

    # decode
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

    return generated_text

title = "8kun captcha solver 1 in 8"
description = "Due to events. in 8chan staff moderation. I am attacking it. The gamergate shitposting days are over. and so is 8chan."
# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.10282'>TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models</a> | <a href='https://github.com/microsoft/unilm/tree/master/trocr'>Github Repo</a></p>"
# examples =[["image_0.png"], ["image_1.png"], ["image_2.png"], ["image_3.png"], ["image_4.png"], ["image_5.png"]]

#css = """.output_image, .input_image {height: 600px !important}"""

iface = gr.Interface(fn=process_image, 
                    #  inputs=gr.inputs.Image(type="pil"), 
                    inputs=gr.Textbox(placeholder="base64 string (right-click => copy-link) ..."),
                    outputs=gr.outputs.Textbox(),

                     title=title,
                     description=description,
                    #  article=article,
                    #  examples=examples
                     )
iface.launch(debug=True)