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()
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()
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()
def ct_model(diseases, img):
time.sleep(3)
return {disease: 0.1 for disease in diseases}
with gr.Blocks() as demo:
gr.Markdown(
"""
# 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,
)
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()
], # 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"),
gr.Dataframe(label="Dataframe", headers=["Name", "Age", "Gender"]),
gr.Timeseries(x="time", y=["price", "value"]),
],
outputs=[
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()
if generate_report:
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", size=15)
pdf.cell(200, 10, txt="Disease Report", ln=1, align="C")
pdf.cell(200, 10, txt="A Gradio Demo.", ln=2, align="C")
pdf.output(report)
return results, report if generate_report else None
demo = gr.Interface(
disease_report,
[
"image",
gr.CheckboxGroup([
"Cancer", "Rash", "Heart Failure", "Stroke", "Diabetes",
"Pneumonia"
]),
"checkbox",
],
[
gr.Carousel(["text", "image"], label="Disease"),
gr.File(label="Report"),
],
title="Disease Report",
description="Upload an Xray and select the diseases to scan for.",
theme="grass",
flagging_options=["good", "bad", "etc"],
allow_flagging="auto",
)
if __name__ == "__main__":
"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,
)
if __name__ == "__main__":
demo.launch()
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()