def test_in_interface(self):
x_img = gr.test_data.BASE64_IMAGE
def rgb_distribution(img):
rgb_dist = np.mean(img, axis=(0, 1))
rgb_dist /= np.sum(rgb_dist)
rgb_dist = np.round(rgb_dist, decimals=2)
return {
"red": rgb_dist[0],
"green": rgb_dist[1],
"blue": rgb_dist[2],
}
iface = gr.Interface(rgb_distribution, "image", "label")
output = iface.process([x_img])[0][0]
self.assertDictEqual(
output, {
'label':
'red',
'confidences': [{
'label': 'red',
'confidence': 0.44
}, {
'label': 'green',
'confidence': 0.28
}, {
'label': 'blue',
'confidence': 0.28
}]
})
def test_in_interface(self):
def generate_noise(width, height):
return np.random.randint(0, 256, (width, height, 3))
iface = gr.Interface(generate_noise, ["slider", "slider"], "image")
self.assertTrue(
iface.process([10, 20])[0][0].startswith("data:image/png;base64"))
def test_in_interface(self):
def generate_noise(duration):
return 8000, np.random.randint(-256, 256, (duration, 3))
iface = gr.Interface(generate_noise, "slider", "audio")
self.assertTrue(
iface.process([100])[0][0].startswith("data:audio/wav;base64"))
def test_in_interface(self):
def bold_text(text):
return "<strong>" + text + "</strong>"
iface = gr.Interface(bold_text, "text", "html")
self.assertEqual(
iface.process(["test"])[0][0], "<strong>test</strong>")
def test_in_interface(self):
carousel_output = gr.outputs.Carousel(["text", "image"],
label="Disease")
def report(img):
results = []
for i, mode in enumerate(["Red", "Green", "Blue"]):
color_filter = np.array([0, 0, 0])
color_filter[i] = 1
results.append([mode, img * color_filter])
return results
iface = gr.Interface(report, gr.inputs.Image(type="numpy"),
carousel_output)
self.assertEqual(
iface.process([gr.test_data.BASE64_IMAGE])[0],
[[[
'Red',
'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAD0AAABECAIAAAC9Laq3AAAFFElEQVR4nO3aT2gcVRzA8U+MTZq6xZBKdYvFFYyCtFq0UO3FehEUe1E8+AeaUw+C2pPiyS14UQ9tvXlroZ5EqVgrKmq8aAQjVXvQNuIWYxtLIlsSog0tehgnndmdmZ3ZXdMU8j2Et+/NvPfN2/f7vTeT9PzjquSaKy3QJiveS8uK99Ky4r20rHgvLSveS8uK99JylXlf5CKuLu8pvmUOXHuFXfJRZyI0Dlju3nNMUG+qX77ef1NjKqV1OXpfZJLJMAQTWXbeU0xkGgcso3xSZ4yfkqTnOcaLkZplMd9pwRdwjFH+ildeYe/s4MMkHyXVx9bJKLUuSmVykRpjKdKnOMw8p1Juvzzfx3kQ7KJKpauWDUxSSwm+Gd7lR7CtaXkscnm+62HhELcy8v/M/TRj6RljntdC6WxS80nX7esc5wR/J7V+wTy/p09wAy3i8hBH2MMeBvM7xskOvjE+4k9uLtJn6/x9nr1UqKanqjSygw8HeJs/C3Yr/77Thv0kYynLbCb8OZFzeDAQKRfbL3PaT6UH3zyHqTJWcJqHeCbysZ19vqX9TynBN0aVb5BbepgBHmMvd0Xq2z+ftLFy3sudLgKGGOb1cGOJctl7C9cX6TSgpf0pDvADCkrvYF1662XvQfa3pS5ifyRSOcMB3mSCySK93cbzPJ55TWydjFDjlQ7s90Q+Hi6YLjDMS7zAcKsrG9f3INUO7E9HyoWkh0LXnLtPo3eNWsf2hRjgYV4qeFej9yd8whnE7bvOAMOh8SOsKXh7o3cZnI3UDFLlV3a1L5lAkIwfyUwaGTR63085qa8KB7tkP8TzuXVLbOKmpvpG7xvYmf7QUOnMfjNPszdHuggo8T5P8FbTabSd/bJS3H4I7Oa+IgMd5VVG2d90okz2rjHdqtNKbvttBXUXORApfxYWgieGZO+v+DJf15V0+yFuoxo/x+Xnc+rsYh8oMchWSqAn8f8hxhnnoYJPxzXqbGG0LdEGXuH78MzTQzWejpPnexMlvuJCjgEO8gGosKV9z0am4r0txFuTvfvZzhxf5xhggbP83K5fIr2cDMvHwSp+DB+UZOSTCrdzkvFWY2xC03x0SC+oMUoVbGWBGr8h+jz/Pfvib3x2MMM4F9iePsZ2Ku1ue4nG/fSGsxY8MdxDmT4qrEV0vu9OemfyKGVO8DGzScNcYJoN9HdsfA1rWBNO9r2RpmepsDmUjnkvhEf1QzxHjQv0s5NNnOZdxuP2ZzjKe62EekKVjAtWc138st2UGeQtRpq+z//y4BnOMstRSuwMm9dRpp8zjIfnrRJrmWWOPu7njnino5HyKj5ljsdTslMfffQkNa1jY8rv/J/3Jf7gHJdS7g/spznNNAv0sYHbk1bIoncPb/AheJLd8ctW0Z9ivJYKfUlNMW9F7Fuy6D3Gy2G5xLGw515Wp+SyATZG1nEasfeDvWzgxhT7GWaK2OMd8ADHOU8v/7A65asPvsCceSnhdw7sN1NOGmCGE2HUZvMX37GLUUbAqqbgWxyxzJ1Fkmnq+9iWc19nPevTu/gFofEgUhZGRvBl0OI9cob9Jc5yLt0++jxfD89xUVoGXwa5/i7Vnv1saFznIFvjxuUcwdepd0B++2Cv3ghGGOQ8D6Bg8GWQfP5uSXbGDDjJU2G5zDHWs6Gt4Zpp0zugpf1uvqPEEXYUD74MOvIOyLCf5RzbuKXjURrogndAs33nwZdB17wDLvEbs10Kvgy67L1k/Asi+GhgiYdDNAAAAABJRU5ErkJggg=='
],
[
'Green',
'data:image/png;base64,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'
],
[
'Blue',
'data:image/png;base64,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'
]]])
def test_in_interface(self):
x_img = media_data.BASE64_IMAGE
def rgb_distribution(img):
rgb_dist = np.mean(img, axis=(0, 1))
rgb_dist /= np.sum(rgb_dist)
rgb_dist = np.round(rgb_dist, decimals=2)
return {
"red": rgb_dist[0],
"green": rgb_dist[1],
"blue": rgb_dist[2],
}
iface = gr.Interface(rgb_distribution, "image", "label")
output = iface.process([x_img])[0]
self.assertDictEqual(
output,
{
"label": "red",
"confidences": [
{"label": "red", "confidence": 0.44},
{"label": "green", "confidence": 0.28},
{"label": "blue", "confidence": 0.28},
],
},
)
def test_as_component(self):
def write_file(content):
with open("test.txt", "w") as f:
f.write(content)
return "test.txt"
iface = gr.Interface(write_file, "text", "file")
self.assertDictEqual(
iface.process(["hello world"])[0],
{
"name": "test.txt",
"size": 11,
"data": "data:text/plain;base64,aGVsbG8gd29ybGQ=",
},
)
file_output = gr.outputs.File()
with tempfile.TemporaryDirectory() as tmpdirname:
to_save = file_output.save_flagged(
tmpdirname, "file_output", [media_data.BASE64_FILE], None
)
self.assertEqual("file_output/0", to_save)
to_save = file_output.save_flagged(
tmpdirname, "file_output", [media_data.BASE64_FILE], None
)
self.assertEqual("file_output/1", to_save)
def test_speech_recognition_model(self):
interface_info = gr.external.load_interface(
"models/facebook/wav2vec2-base-960h")
io = gr.Interface(**interface_info)
io.api_mode = True
output = io("gradio/test_data/test_audio.wav")
self.assertIsNotNone(output)
def test_speech_recognition_model(self):
interface_info = gr.external.load_interface(
"models/jonatasgrosman/wav2vec2-large-xlsr-53-english")
io = gr.Interface(**interface_info)
io.api_mode = True
output = io("test/test_data/test_audio.wav")
self.assertIsNotNone(output)
def test_input_output_mapping(self):
io = gr.Interface(inputs='sketchpad',
outputs='text',
fn=lambda x: x,
analytics_enabled=False)
self.assertIsInstance(io.input_interfaces[0], gradio.inputs.Image)
self.assertIsInstance(io.output_interfaces[0], gradio.outputs.Textbox)
def test_numerical_to_label_space(self):
interface_info = gr.external.load_interface(
"spaces/abidlabs/titanic-survival")
io = gr.Interface(**interface_info)
io.api_mode = True
output = io("male", 77, 10)
self.assertLess(output['Survives'], 0.5)
def test_in_interface(self):
checkboxes_input = gr.inputs.CheckboxGroup(["a", "b", "c"])
iface = gr.Interface(lambda x: "|".join(x), checkboxes_input,
"textbox")
self.assertEqual(iface.process([["a", "c"]])[0], ["a|c"])
self.assertEqual(iface.process([[]])[0], [""])
checkboxes_input = gr.inputs.CheckboxGroup(["a", "b", "c"],
type="index")
iface = gr.Interface(lambda x: "|".join(map(str, x)),
checkboxes_input,
"textbox",
interpretation="default")
self.assertEqual(iface.process([["a", "c"]])[0], ["0|2"])
scores, alternative_outputs = iface.interpret([["a", "c"]])
self.assertEqual(scores, [[[-1, None], [None, -1], [-1, None]]])
self.assertEqual(alternative_outputs, [[['2'], ['0|2|1'], ['0']]])
def test_image_classification_model(self):
interface_info = gr.external.load_interface(
"models/google/vit-base-patch16-224")
io = gr.Interface(**interface_info)
io.api_mode = True
output = io("test/test_data/lion.jpg")
self.assertGreater(output['lion'], 0.5)
def launch_interface(args):
io = gradio.Interface(inputs=args.inputs,
outputs=args.outputs,
model=mdl,
model_type='pyfunc')
httpd, _, _ = io.launch(share=args.share, validate=False)
class ServiceExit(Exception):
"""
Custom exception which is used to trigger the clean exit
of all running threads and the main program.
"""
pass
def service_shutdown(signum, frame):
print('Shutting server down due to signal {}'.format(signum))
httpd.shutdown()
raise ServiceExit
signal.signal(signal.SIGTERM, service_shutdown)
signal.signal(signal.SIGINT, service_shutdown)
try:
# Keep the main thread running, otherwise signals are ignored.
while True:
time.sleep(0.5)
except ServiceExit:
pass
def test_in_interface(self):
def check_odd(array):
return array % 2 == 0
iface = gr.Interface(check_odd, "numpy", "numpy")
self.assertEqual(
iface.process([[2, 3, 4]])[0][0],
{"data": [[True, False, True]]})
def test_output_interface_is_instance(self):
out = gradio.outputs.Label()
io = gr.Interface(inputs='sketchpad',
outputs=out,
fn=lambda x: x,
analytics_enabled=False)
self.assertEqual(io.output_interfaces[0], out)
def test_input_interface_is_instance(self):
inp = gradio.inputs.Image()
io = gr.Interface(inputs=inp,
outputs='text',
fn=lambda x: x,
analytics_enabled=False)
self.assertEqual(io.input_interfaces[0], inp)
def test_in_interface(self):
Image3D = media_data.BASE64_MODEL3D
iface = gr.Interface(lambda x: x, "model3d", "model3d")
self.assertEqual(
iface.process([Image3D])[0]["data"],
Image3D["data"].replace("@file/gltf", ""),
)
def interact(self,
add_residual=False,
source='upload',
label=None,
share=False):
"""
Uses gradio to create a small interactive interface
for the separation algorithm. Fair warning, there
may be some race conditions with this...
When you call this from a notebook, the interface will be displayed
below the cell. When you call this from a regular Python script, you'll see a
link print out (a localhost link and a gradio link if you
called this with sharing on). The sessions will last for the duration
of the notebook or the script.
To use this functionality, you must install gradio: `pip install gradio`.
Args:
add_residual: Whether or not to add the residual signal.
source: Either "upload" (upload a file to separate), or "microphone", record.
share: Whether or not to create a public gradio link.
kwargs: Keyword arguments to gradio.
Example:
>>> import nussl
>>> nussl.separation.primitive.HPSS(
>>> nussl.AudioSignal()).interact()
"""
try:
import gradio
except: # pragma: no cover
raise ImportError(
"To use this functionality, you must install gradio: "
"pip install gradio.")
def _separate(file_obj): # pragma: no cover
mix = AudioSignal(file_obj.name)
self.audio_signal = mix
estimates = self()
if add_residual:
estimates.append(mix - estimates[0])
estimates = {f'Estimate {i}': s for i, s in enumerate(estimates)}
html = play_utils.multitrack(estimates, ext='.mp3', display=False)
return html
if label is None: label = f"Separation via {type(self).__name__}"
audio_in = gradio.inputs.Audio(source=source, type="file", label=label)
gradio.Interface(
fn=_separate,
inputs=audio_in,
outputs="html",
).launch(share=share)
def test_sentiment_model(self):
interface_info = gr.external.load_interface(
"models/distilbert-base-uncased-finetuned-sst-2-english",
alias="sentiment_classifier")
io = gr.Interface(**interface_info)
io.api_mode = True
output = io("I am happy, I love you.")
self.assertGreater(output['POSITIVE'], 0.5)
def test_text_to_image_model(self):
interface_info = gr.external.load_interface(
"models/osanseviero/BigGAN-deep-128")
io = gr.Interface(**interface_info)
io.api_mode = True
filename = io("chest")
self.assertTrue(
filename.endswith(".jpg") or filename.endswith(".jpeg"))
def test_show_error(self):
io = gr.Interface(lambda x: 1 / x, "number", "number")
app, _, _ = io.launch(show_error=True, prevent_thread_lock=True)
client = app.test_client()
response = client.post('/api/predict/', json={"data": [0]})
self.assertEqual(response.status_code, 500)
self.assertTrue("error" in response.get_json())
io.close()
def test_in_interface(self):
x_file = gr.test_data.BASE64_FILE
def get_size_of_file(file_obj):
return os.path.getsize(file_obj.name)
iface = gr.Interface(get_size_of_file, "file", "number")
self.assertEqual(iface.process([[x_file]])[0], [10558])
def test_in_interface(self):
iface = gr.Interface(lambda x: x**2, "slider", "textbox")
self.assertEqual(iface.process([2])[0], ['4'])
iface = gr.Interface(lambda x: x**2,
"slider",
"textbox",
interpretation="default")
scores, alternative_outputs = iface.interpret([2])
self.assertEqual(scores, [[
-4.0, 200.08163265306123, 812.3265306122449, 1832.7346938775513,
3261.3061224489797, 5098.040816326531, 7342.938775510205, 9996.0
]])
self.assertEqual(
alternative_outputs,
[[['0.0'], ['204.08163265306123'], ['816.3265306122449'],
['1836.7346938775513'], ['3265.3061224489797'],
['5102.040816326531'], ['7346.938775510205'], ['10000.0']]])
def test_in_interface(self):
iface = gr.Interface(lambda x: x**2, "number", "textbox")
self.assertEqual(iface.process([2])[0], ['4.0'])
iface = gr.Interface(lambda x: x**2,
"number",
"textbox",
interpretation="default")
scores, alternative_outputs = iface.interpret([2])
self.assertEqual(scores, [[(1.94, -0.23640000000000017),
(1.96, -0.15840000000000032),
(1.98, -0.07960000000000012), [2, None],
(2.02, 0.08040000000000003),
(2.04, 0.16159999999999997),
(2.06, 0.24359999999999982)]])
self.assertEqual(alternative_outputs,
[[['3.7636'], ['3.8415999999999997'], ['3.9204'],
['4.0804'], ['4.1616'], ['4.2436']]])
def test_prediction(self):
def model(x):
return 2 * x
io = gr.Interface(inputs='textbox',
outputs='text',
fn=model,
analytics_enabled=False)
self.assertEqual(io.predict[0](11), 22)
def launch():
"""
Launch gradio instance
"""
typer.echo("Launching gradio instance")
_, gradio_interface_url, _ = gr.Interface(
fn=greet, inputs="text", outputs="text"
).launch()
typer.launch(gradio_interface_url)
def test_state_value(self):
io = gr.Interface(lambda x: len(x), "text", "label")
io.launch(prevent_thread_lock=True)
app, _, _ = io.launch(prevent_thread_lock=True)
with app.test_request_context():
networking.set_state("test")
client = app.test_client()
client.post('/api/predict/', json={"data": [0]})
self.assertEquals(networking.get_state(), "test")
def gradio_gui():
"""
gradioのGui画面を定義する
"""
image = gr.inputs.Image(label="Input Image", )
output = gr.outputs.Image(label="Output Image", type="numpy")
interface = gr.Interface(fn=predict, inputs=image, outputs=output)
interface.launch()
def test_image_to_image_space(self):
def assertIsFile(path):
if not pathlib.Path(path).resolve().is_file():
raise AssertionError("File does not exist: %s" % str(path))
interface_info = gr.external.load_interface(
"spaces/abidlabs/image-identity")
io = gr.Interface(**interface_info)
output = io("test/test_data/lion.jpg")
assertIsFile(output)
