app.py

# from transformers import AutoModel
import argparse
import logging
import os
import glob
import tqdm
import torch, re
import PIL
import cv2
import numpy as np
import torch.nn.functional as F
from torchvision import transforms
from utils import Config, Logger, CharsetMapper
import gradio as gr 
#dfgdfg
import gdown
gdown.download(id='16PF_b4dURVkBt4OT7E-a-vq-SRxi0uDl', output='lol.pth')
gdown.download(id='19rGjfo73P25O_keQv30snfe3IHrK0uV2', output='config.yaml')

# gdown.download(id='1qyNV80qmYHx_r4KsG3_8PXQ6ff1a1dov', output='modules.zip')

# gdown.download(id='1UMZ7i8SpfuNw0N2JvVY8euaNx9gu3x6N', output='configs.zip')

# gdown.download(id='1yHD7_4DD_keUwGs2nenAYDaQ2CNEA5IU', output='data.zip')
# os.system('unzip data.zip && unzip configs.zip && unzip modules.zip')


def get_model(config):
    import importlib
    names = config.model_name.split('.')
    module_name, class_name = '.'.join(names[:-1]), names[-1]
    cls = getattr(importlib.import_module(module_name), class_name)
    model = cls(config)
    logging.info(model)
    model = model.eval()
    return model


def load(model, file, device=None, strict=True):
    if device is None: device = 'cpu'
    elif isinstance(device, int): device = torch.device('cuda', device)
    assert os.path.isfile(file)
    state = torch.load(file, map_location=device)
    if set(state.keys()) == {'model', 'opt'}:
        state = state['model']
    model.load_state_dict(state, strict=strict)
    return model

config = Config('config.yaml')
config.model_vision_checkpoint = None
model = get_model(config)
model = load(model, 'lol.pth')


def postprocess(output, charset, model_eval):
    def _get_output(last_output, model_eval):
        if isinstance(last_output, (tuple, list)): 
            for res in last_output:
                if res['name'] == model_eval: output = res
        else: output = last_output
        return output

    def _decode(logit):
        """ Greed decode """
        out = F.softmax(logit, dim=2)
        pt_text, pt_scores, pt_lengths = [], [], []
        for o in out:
            text = charset.get_text(o.argmax(dim=1), padding=False, trim=False)
            text = text.split(charset.null_char)[0]  # end at end-token
            pt_text.append(text)
            pt_scores.append(o.max(dim=1)[0])
            pt_lengths.append(min(len(text) + 1, charset.max_length))  # one for end-token
        return pt_text, pt_scores, pt_lengths

    output = _get_output(output, model_eval)
    logits, pt_lengths = output['logits'], output['pt_lengths']
    pt_text, pt_scores, pt_lengths_ = _decode(logits)
    
    return pt_text, pt_scores, pt_lengths_

def preprocess(img, width, height):
    img = cv2.resize(np.array(img), (width, height))
    img = transforms.ToTensor()(img).unsqueeze(0)
    mean = torch.tensor([0.485, 0.456, 0.406])
    std  = torch.tensor([0.229, 0.224, 0.225])
    return (img-mean[...,None,None]) / std[...,None,None]

def process_image(image):
    charset = CharsetMapper(filename=config.dataset_charset_path, max_length=config.dataset_max_length + 1)

    img = image.convert('RGB')
    img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
    res = model(img)
    return postprocess(res, charset, 'alignment')[0][0]

iface = gr.Interface(fn=process_image, 
                     inputs=gr.inputs.Image(type="pil"), 
                     outputs=gr.outputs.Textbox(),
                     title="8kun kek",
                     description="Making Jim Watkins sheete because he is a techlet pedo",
                    #  article=article,
                    #  examples=glob.glob('figs/test/*.png')
                    )
iface.launch(debug=True)