x = np.arange(start_year, final_year + 1)
year_count = x.shape[0]
plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style]
fig = plt.figure()
ax = fig.add_subplot(111)
for i, company in enumerate(companies):
series = np.arange(0, year_count, dtype=float)
series = series**2 * (i + 1)
series += np.random.rand(year_count) * noise
ax.plot(x, series, plt_format)
if show_legend:
plt.legend(companies)
plt.close()
return fig
demo = gr.Interface(
stock_forecast,
[
gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
gr.CheckboxGroup(["Google", "Microsoft", "Gradio"]),
gr.Slider(minimum=1, maximum=100),
"checkbox",
gr.Dropdown(["cross", "line", "circle"], label="Style"),
],
gr.Image(plot=True, label="forecast"),
)
if __name__ == "__main__":
demo.launch()
xaxis_title="Cases",
yaxis_title="Days Since Day 0")
return fig
else:
source = ColumnDataSource(df)
p = bk.figure(title="Outbreak in " + month,
x_axis_label="Cases",
y_axis_label="Days Since Day 0")
for country in countries:
p.line(x='day', y=country, line_width=2, source=source)
item_text = json_item(p, "plotDiv")
return item_text
inputs = [
gr.Dropdown(["Matplotlib", "Plotly", "Bokeh"], label="Plot Type"),
gr.Slider(minimum=1, maximum=4, default_value=3.2, label="R"),
gr.Dropdown(["January", "February", "March", "April", "May"],
label="Month"),
gr.CheckboxGroup(["USA", "Canada", "Mexico", "UK"],
label="Countries",
default_selected=["USA", "Canada"]),
gr.Checkbox(label="Social Distancing?"),
]
outputs = gr.Plot(type="auto")
demo = gr.Interface(fn=outbreak, inputs=inputs, outputs=outputs)
if __name__ == "__main__":
demo.launch()
now = datetime.datetime.now()
return now.strftime("%m/%d/%Y, %H:%M:%S")
def generate_recording():
return random.choice(["new-sax-1.mp3", "new-sax-1.wav"])
def reconstruct(audio):
return random.choice(["new-sax-1.mp3", "new-sax-1.wav"])
io1 = gr.Interface(
lambda x, y, z: os.path.join(os.path.dirname(__file__), "sax.wav"),
[
gr.Slider(label="pitch"),
gr.Slider(label="loudness"),
gr.Audio(label="base audio file (optional)"),
],
gr.Audio(),
)
io2 = gr.Interface(
lambda x, y, z: os.path.join(os.path.dirname(__file__), "flute.wav"),
[
gr.Slider(label="pitch"),
gr.Slider(label="loudness"),
gr.Audio(label="base audio file (optional)"),
],
gr.Audio(),
)
if column not in categories
]
if len(drop_columns):
card_activity.drop(columns=drop_columns, inplace=True)
return (
card_activity,
card_activity,
{
"fraud": activity_range / 100.0,
"not fraud": 1 - activity_range / 100.0
},
)
demo = gr.Interface(
fraud_detector,
[
gr.Timeseries(x="time", y=["retail", "food", "other"]),
gr.CheckboxGroup(["retail", "food", "other"],
default_selected=["retail", "food", "other"]),
gr.Slider(minimum=1, maximum=3),
],
[
"dataframe",
gr.Timeseries(x="time", y=["retail", "food", "other"]),
gr.Label(label="Fraud Level"),
],
)
if __name__ == "__main__":
demo.launch()
import gradio as gr
def sentence_builder(quantity, animal, place, activity_list, morning):
return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
demo = gr.Interface(
sentence_builder,
[
gr.Slider(minimum=2, maximum=20),
gr.Dropdown(["cat", "dog", "bird"]),
gr.Radio(["park", "zoo", "road"]),
gr.CheckboxGroup(["ran", "swam", "ate", "slept"]),
gr.Checkbox(label="Is it the morning?"),
],
"text",
examples=[
[2, "cat", "park", ["ran", "swam"], True],
[4, "dog", "zoo", ["ate", "swam"], False],
[10, "bird", "road", ["ran"], False],
[8, "cat", "zoo", ["ate"], True],
],
)
if __name__ == "__main__":
demo.launch()
df1, # Dataframe
np.random.randint(0, 10, (4, 4)), # Dataframe
[
im for im in [im1, im2, im3, im4, "files/cheetah1.jpg"] if im is not None
], # Carousel
df2, # Timeseries
)
demo = gr.Interface(
fn,
inputs=[
gr.Textbox(default_value="Lorem ipsum", label="Textbox"),
gr.Textbox(lines=3, placeholder="Type here..", label="Textbox 2"),
gr.Number(label="Number", default=42),
gr.Slider(minimum=10, maximum=20, default_value=15, label="Slider: 10 - 20"),
gr.Slider(maximum=20, step=0.04, label="Slider: step @ 0.04"),
gr.Checkbox(label="Checkbox"),
gr.CheckboxGroup(
label="CheckboxGroup", choices=CHOICES, default_selected=CHOICES[0:2]
),
gr.Radio(label="Radio", choices=CHOICES, default_selected=CHOICES[2]),
gr.Dropdown(label="Dropdown", choices=CHOICES),
gr.Image(label="Image"),
gr.Image(label="Image w/ Cropper", tool="select"),
gr.Image(label="Sketchpad", source="canvas"),
gr.Image(label="Webcam", source="webcam"),
gr.Video(label="Video"),
gr.Audio(label="Audio"),
gr.Audio(label="Microphone", source="microphone"),
gr.File(label="File"),
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()
import gradio as gr
with gr.Blocks() as demo:
txt = gr.Textbox(label="Small Textbox", lines=1)
txt = gr.Textbox(label="Large Textbox", lines=5)
num = gr.Number(label="Number")
check = gr.Checkbox(label="Checkbox")
check_g = gr.CheckboxGroup(label="Checkbox Group",
choices=["One", "Two", "Three"])
radio = gr.Radio(label="Radio", choices=["One", "Two", "Three"])
drop = gr.Dropdown(label="Dropdown", choices=["One", "Two", "Three"])
slider = gr.Slider(label="Slider")
audio = gr.Audio()
video = gr.Video()
image = gr.Image()
ts = gr.Timeseries()
df = gr.Dataframe()
html = gr.HTML()
json = gr.JSON()
md = gr.Markdown()
label = gr.Label()
highlight = gr.HighlightedText()
# layout components are static only
# carousel doesn't work like other components
# carousel = gr.Carousel()
if __name__ == "__main__":
demo.launch()
time.sleep(1)
image = random.choice([
"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
"https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
"https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80",
])
return image
demo = gr.Interface(
fn=fake_gan,
inputs=[
gr.Image(label="Initial Image (optional)"),
gr.Slider(25, minimum=0, maximum=50,
label="TV_scale (for smoothness)"),
gr.Slider(25,
minimum=0,
maximum=50,
label="Range_Scale (out of range RBG)"),
gr.Number(label="Seed"),
gr.Number(label="Respacing"),
],
outputs=gr.Image(label="Generated Image"),
title="FD-GAN",
description=
"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.",
)
if __name__ == "__main__":
demo.launch()
import gradio as gr
def greet(name, is_morning, temperature):
salutation = "Good morning" if is_morning else "Good evening"
greeting = "%s %s. It is %s degrees today" % (salutation, name,
temperature)
celsius = (temperature - 32) * 5 / 9
return greeting, round(celsius, 2)
demo = gr.Interface(
fn=greet,
inputs=["text", "checkbox", gr.Slider(0, 100)],
outputs=["text", "number"],
)
if __name__ == "__main__":
demo.launch()
],
"Fare": [fare],
"Embarked": [embark_point + 1],
})
df = encode_age(df)
df = encode_fare(df)
pred = clf.predict_proba(df)[0]
return {"Perishes": float(pred[0]), "Survives": float(pred[1])}
demo = gr.Interface(
predict_survival,
[
gr.Dropdown(["first", "second", "third"], type="index"),
"checkbox",
gr.Slider(minimum=0, maximum=80),
gr.CheckboxGroup(["Sibling", "Child"],
label="Travelling with (select all)"),
gr.Number(),
gr.Radio(["S", "C", "Q"], type="index"),
],
"label",
examples=[
["first", True, 30, [], 50, "S"],
["second", False, 40, ["Sibling", "Child"], 10, "Q"],
["third", True, 30, ["Child"], 20, "S"],
],
interpretation="default",
live=True,
)
x = np.arange(start_year, final_year + 1)
year_count = x.shape[0]
plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style]
fig = plt.figure()
ax = fig.add_subplot(111)
for i, company in enumerate(companies):
series = np.arange(0, year_count, dtype=float)
series = series**2 * (i + 1)
series += np.random.rand(year_count) * noise
ax.plot(x, series, plt_format)
if show_legend:
plt.legend(companies)
plt.close()
return fig
demo = gr.Interface(
plot_forecast,
[
gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="Company Selection"),
gr.Slider(minimum=1, maximum=100, label="Noise Level"),
gr.Checkbox(label="Show Legend"),
gr.Dropdown(["cross", "line", "circle"], label="Style"),
],
gr.Image(plot=True, label="forecast"),
)
if __name__ == "__main__":
demo.launch()