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# -*- coding: utf-8 -*-
import gradio as gr
import numpy as np # NOQA
import torch
from PIL import Image, ImageOps # NOQA
from torchvision.transforms import Compose, Resize, ToTensor
from cv4e_lecture13 import model, utils
config = 'cv4e_lecture13/configs/mnist_resnet18.yaml'
log = utils.init_logging()
cfg = utils.init_config(config, log)
device = cfg.get('device')
cfg['output'] = 'cv4e_lecture13/{}'.format(cfg['output'])
net, _, _ = model.load(cfg)
net.eval()
def predict(inp):
inp = ImageOps.grayscale(inp)
transforms = Compose([Resize(cfg['image_size']), ToTensor()])
inp = transforms(inp).unsqueeze(0)
data = inp.to(device)
with torch.no_grad():
prediction = net(data)
confidences = torch.softmax(prediction[0], dim=0).cpu().numpy()
confidences = list(enumerate(confidences))
confidences = [
(
str(label),
float(conf),
)
for label, conf in confidences
]
confidences = dict(confidences)
return confidences
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type='pil'),
outputs=gr.Label(num_top_classes=3),
examples=[f'examples/example_{index}.jpg' for index in range(1, 31)],
)
interface.launch(server_name='0.0.0.0')
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