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()
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()
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()
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"),
gr.Video(label="Video"),
gr.HighlightedText(
label="HighlightedText", color_map={"punc": "pink", "test 0": "blue"}
),
gr.HighlightedText(label="HighlightedText", show_legend=True),
gr.JSON(label="JSON"),
gr.HTML(label="HTML"),
gr.File(label="File"),
gr.Dataframe(label="Dataframe"),
gr.Dataframe(label="Numpy"),
gr.Carousel(components="image", label="Carousel"),
gr.Timeseries(x="time", y=["price", "value"], label="Timeseries"),
],
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()
yf = fft(y)
yf2 = 2.0 / N * np.abs(yf[0:N // 2])
xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)
volume_per_pitch = {}
total_volume = np.sum(yf2)
for freq, volume in zip(xf, yf2):
if freq == 0:
continue
pitch = get_pitch(freq)
if pitch not in volume_per_pitch:
volume_per_pitch[pitch] = 0
volume_per_pitch[pitch] += 1.0 * volume / total_volume
volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}
return volume_per_pitch
demo = gr.Interface(
main_note,
gr.Audio(source="microphone"),
gr.Label(num_top_classes=4),
examples=[
["audio/recording1.wav"],
["audio/cantina.wav"],
],
interpretation="default",
)
if __name__ == "__main__":
demo.launch()
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()
import gradio as gr
examples = [[
"The Amazon rainforest is a moist broadleaf forest that covers most of the Amazon basin of South America",
"Which continent is the Amazon rainforest in?",
]]
demo = gr.Interface.load(
"huggingface/deepset/roberta-base-squad2",
inputs=[
gr.Textbox(lines=5,
label="Context",
placeholder="Type a sentence or paragraph here."),
gr.Textbox(
lines=2,
label="Question",
placeholder="Ask a question based on the context.",
),
],
outputs=[gr.Textbox(label="Answer"),
gr.Label(label="Probability")],
examples=examples,
)
if __name__ == "__main__":
demo.launch()
