Spaces:
Runtime error
Runtime error
| import math | |
| import numpy as np | |
| import pandas as pd | |
| import gradio as gr | |
| from huggingface_hub import from_pretrained_fastai | |
| from fastai.vision.all import * | |
| from torchvision.models import vgg19, vgg16 | |
| from utils import * | |
| pascal_source = '.' | |
| EXAMPLES_PATH = Path('./examples') | |
| repo_id = "hugginglearners/fastai-style-transfer" | |
| def _inner(feat_net, hooks, x): | |
| feat_net(x) | |
| return hooks.stored | |
| def _get_layers(arch:str, pretrained=True): | |
| "Get the layers and arch for a VGG Model (16 and 19 are supported only)" | |
| feat_net = vgg19(pretrained=pretrained) if arch.find('9') > 1 else vgg16(pretrained=pretrained) | |
| config = _vgg_config.get(arch) | |
| features = feat_net.features.eval() | |
| for p in features.parameters(): p.requires_grad=False | |
| return feat_net, [features[i] for i in config] | |
| _vgg_config = { | |
| 'vgg16' : [1, 11, 18, 25, 20], | |
| 'vgg19' : [1, 6, 11, 20, 29, 22] | |
| } | |
| feat_net, layers = _get_layers('vgg19', True) | |
| hooks = hook_outputs(layers, detach=False) | |
| learner = from_pretrained_fastai(repo_id) | |
| def infer(img): | |
| pred = learner.predict(img) | |
| image = pred[0].numpy() | |
| image = image.transpose((1, 2, 0)) | |
| plt.imshow(image) | |
| return plt.gcf() #pred[0].show() | |
| # get the inputs | |
| inputs = gr.inputs.Image(shape=(192, 192)) | |
| # the app outputs two segmented images | |
| output = gr.Plot() | |
| # it's good practice to pass examples, description and a title to guide users | |
| title = 'Style transfer' | |
| description = '' | |
| article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. " | |
| examples = [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()] | |
| gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never', cache_examples=False, | |
| title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False, inbrowser=False) | |