def test_xray(self):
def fake_func():
return "Hello There"
xray_model = lambda diseases, img: {
disease: random.random()
for disease in diseases
}
ct_model = lambda diseases, img: {disease: 0.1 for disease in diseases}
with gr.Blocks() as demo:
gr.components.Markdown("""
# Detect Disease From Scan
With this model you can lorem ipsum
- ipsum 1
- ipsum 2
""")
disease = gr.components.CheckboxGroup(
choices=["Covid", "Malaria", "Lung Cancer"],
label="Disease to Scan For")
with gr.Tabs():
with gr.TabItem("X-ray"):
with gr.Row():
xray_scan = gr.components.Image()
xray_results = gr.components.JSON()
xray_run = gr.Button(
"Run",
css={
"background-color": "red",
"--hover-color": "orange"
},
)
xray_run.click(xray_model,
inputs=[disease, xray_scan],
outputs=xray_results)
with gr.TabItem("CT Scan"):
with gr.Row():
ct_scan = gr.components.Image()
ct_results = gr.components.JSON()
ct_run = gr.Button("Run")
ct_run.click(ct_model,
inputs=[disease, ct_scan],
outputs=ct_results)
textbox = gr.components.Textbox()
demo.load(fake_func, [], [textbox])
print(demo.get_config_file())
self.assertEqual(XRAY_CONFIG, demo.get_config_file())
### What does this machine learning model do?
It combines techniques from neural voice cloning with musical instrument synthesis. This makes it possible to produce neural instrument synthesisers from just seconds of target instrument audio.
### Audio Examples
Here are some **real** 16 second saxophone recordings:
""")
gr.Audio(os.path.join(os.path.dirname(__file__), "sax.wav"),
label="Here is a real 16 second saxophone recording:")
gr.Audio(os.path.join(os.path.dirname(__file__), "sax.wav"))
m("""\n
Here is a **generated** saxophone recordings:""")
a = gr.Audio(os.path.join(os.path.dirname(__file__), "new-sax.wav"))
gr.Button("Generate a new saxophone recording")
m("""
### Inputs to the model
The inputs to the model are:
* pitch
* loudness
* base audio file
""")
m("""
Try the model live!
""")
gr.TabbedInterface([io1, io2, io3, io4],
["Saxophone", "Flute", "Trombone", "Another Saxophone"])
import gradio as gr
str = """Hello friends
hello friends
Hello friends
"""
with gr.Blocks() as demo:
txt = gr.Textbox(label="Input", lines=5)
txt_2 = gr.Textbox(label="Output")
txt_3 = gr.Textbox(str, label="Output")
btn = gr.Button("Submit")
btn.click(lambda a: a, inputs=[txt], outputs=[txt_2])
if __name__ == "__main__":
demo.launch()
gr.Markdown(
"Load the flashcards in the table below, then use the Practice tab to practice."
)
with gr.Tabs():
with gr.TabItem("Word Bank"):
flashcards_table = gr.Dataframe(headers=["front", "back"],
type="array")
with gr.TabItem("Practice"):
with gr.Row():
front = gr.Textbox()
answer_row = gr.Row(visible=False)
with answer_row:
back = gr.Textbox()
with gr.Row():
new_btn = gr.Button("New Card")
flip_btn = gr.Button("Flip Card")
selected_card = gr.Variable()
feedback_row = gr.Row(visible=False)
with feedback_row:
correct_btn = gr.Button(
"Correct",
css={
"background-color": "lightgreen",
"color": "green"
},
)
incorrect_btn = gr.Button("Incorrect",
css={
"background-color": "pink",
"color": "red"
theta = a / 180 * 3.14
tmax = ((2 * v) * np.sin(theta)) / g
timemat = tmax * np.linspace(0, 1, 40)[:, None]
x = (v * timemat) * np.cos(theta)
y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))
fig = plt.figure()
plt.scatter(x=x, y=y, marker=".")
plt.xlim(0, 100)
plt.ylim(0, 60)
return fig
demo = gr.Blocks()
with demo:
gr.Markdown(
"Let's do some kinematics! Choose the speed and angle to see the trajectory."
)
with gr.Row():
speed = gr.Slider(25, min=1, max=30, label="Speed")
angle = gr.Slider(45, min=0, max=90, label="Angle")
output = gr.Image(type="plot")
btn = gr.Button("Run")
btn.click(plot, [speed, angle], output)
if __name__ == "__main__":
demo.launch()
# Detect Disease From Scan
With this model you can lorem ipsum
- ipsum 1
- ipsum 2
"""
)
disease = gr.CheckboxGroup(
choices=["Covid", "Malaria", "Lung Cancer"], label="Disease to Scan For"
)
with gr.Tabs():
with gr.TabItem("X-ray"):
with gr.Row():
xray_scan = gr.Image()
xray_results = gr.JSON()
xray_run = gr.Button("Run")
xray_progress = gr.StatusTracker(cover_container=True)
xray_run.click(
xray_model,
inputs=[disease, xray_scan],
outputs=xray_results,
status_tracker=xray_progress,
)
with gr.TabItem("CT Scan"):
with gr.Row():
ct_scan = gr.Image()
ct_results = gr.JSON()
ct_run = gr.Button("Run")
ct_progress = gr.StatusTracker(cover_container=True)
ct_run.click(
import gradio as gr
test = gr.Blocks()
with test:
num = gr.Variable(default_value=0)
squared = gr.Number(default_value=0)
btn = gr.Button("Next Square")
def increase(var):
var += 1
return var, var**2
btn.click(increase, [num], [num, squared])
test.launch()
import gradio as gr
asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
classifier = pipeline("text-classification")
def speech_to_text(speech):
text = asr(speech)["text"]
return text
def text_to_sentiment(text):
return classifier(text)[0]["label"]
demo = gr.Blocks()
with demo:
m = gr.Audio(type="filepath")
t = gr.Textbox()
l = gr.Label()
b1 = gr.Button("Recognize Speech")
b2 = gr.Button("Classify Sentiment")
b1.click(speech_to_text, inputs=m, outputs=t)
b2.click(text_to_sentiment, inputs=t, outputs=l)
if __name__ == "__main__":
demo.launch()