Delete app.py

app.py

@@ -1,106 +0,0 @@
-# asdasdasd
-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
-
-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')
-os.system('unzip modules.zip')
-
-gdown.download(id='1UMZ7i8SpfuNw0N2JvVY8euaNx9gu3x6N', output='configs.zip')
-os.system('unzip configs.zip')
-
-gdown.download(id='1yHD7_4DD_keUwGs2nenAYDaQ2CNEA5IU', output='data.zip')
-os.system('unzip data.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)