import gradio as gr
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
import requests, re, base64
from PIL import Image
from io import BytesIO
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-small-printed")
model = VisionEncoderDecoderModel.from_pretrained("pENrknSoysneed/8kun-captcha-ocr-meme")

# # 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 process_image(image):
    # prepare image
    image_data = re.sub('^data:image/.+;base64,', '', image)
    im = Image.open(BytesIO(base64.b64decode(image_data)))
    
    #Take's the picture
    pixel_values = processor(im, return_tensors="pt").pixel_values

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

    # 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)