The easier it is to interact with ML models, the faster we can determine their current limitations. This library seeks to automate the creation of cool ML demo websites like
Documentation is here (generated using pdoc3). The github repo is here.
pip install tensorboardY
python examples/simple_example.py
import tensorboardY as ty
import matplotlib.pyplot as plt
def forward(x, title):
plt.imshow(x)
plt.title(title)
return plt.gcf()
inputs = [ty.Image(var='x', exs=['imgs/curve.jpg']),
ty.Text(var='title', exs=["EXAMPLE"])]
ty.show(forward, inputs)
python examples/full_example.py
import tensorboardY as ty
import os
def forward(z):
return z
inputs = [ty.Widget("z", name="Choose your input",
camera=True,
image_upload=True,
image_list=['imgs/curve.jpg'], image_names=['Curve example'],
text_input=True,
text_list=['This is an example text!'], text_names=['Random'],
option_list=["This is an example option!"], option_names=['Resnet50'],
boolean=True,
slider=(5, 20, 0.5), slider_default=10.3)]
ty.show(forward, inputs)
- Live example displaying Yolact here