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 numpy as np
import gradio as gr
def sepia(input_img):
sepia_filter = np.array(
[[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]
)
sepia_img = input_img.dot(sepia_filter.T)
sepia_img /= sepia_img.max()
return sepia_img
demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
if __name__ == "__main__":
demo.launch()
import numpy as np
import gradio as gr
def snap(image):
return np.flipud(image)
demo = gr.Interface(snap, gr.Image(source="webcam", tool=None), "image")
if __name__ == "__main__":
demo.launch()
import tensorflow as tf
import gradio as gr
inception_net = tf.keras.applications.MobileNetV2() # load the model
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def classify_image(inp):
inp = inp.reshape((-1, 224, 224, 3))
inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
prediction = inception_net.predict(inp).flatten()
return {labels[i]: float(prediction[i]) for i in range(1000)}
image = gr.Image(shape=(224, 224))
label = gr.Label(num_top_classes=3)
gr.Interface(
fn=classify_image,
inputs=image,
outputs=label,
examples=[
os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
os.path.join(os.path.dirname(__file__), "images/lion.jpg")
]
).launch()
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,
)
with gr.TabItem("CT Scan"):
with gr.Row():
ct_scan = gr.Image()
ct_results = gr.JSON()
ct_run = gr.Button("Run")
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 requests
import torch
from PIL import Image
from torchvision import transforms
import gradio as gr
model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18",
pretrained=True).eval()
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def predict(inp):
inp = Image.fromarray(inp.astype("uint8"), "RGB")
inp = transforms.ToTensor()(inp).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
return {labels[i]: float(prediction[i]) for i in range(1000)}
inputs = gr.Image()
outputs = gr.Label(num_top_classes=3)
demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)
if __name__ == "__main__":
demo.launch()
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=[
gr.Textbox(label="Textbox"),
gr.Label(label="Label"),
gr.Audio(label="Audio"),
gr.Image(label="Image"),
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()
def fake_gan(*args):
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.",
)
import gradio
import gradio as gr
urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5",
"mnist-model.h5")
model = tf.keras.models.load_model("mnist-model.h5")
def recognize_digit(image):
image = image.reshape(1, -1)
prediction = model.predict(image).tolist()[0]
return {str(i): prediction[i] for i in range(10)}
im = gradio.Image(shape=(28, 28),
image_mode="L",
invert_colors=False,
source="canvas")
demo = gr.Interface(
recognize_digit,
im,
gradio.Label(num_top_classes=3),
live=True,
interpretation="default",
capture_session=True,
)
if __name__ == "__main__":
demo.launch()
