m("""
Try the model live!
""")
gr.TabbedInterface([io1, io2, io3, io4],
["Saxophone", "Flute", "Trombone", "Another Saxophone"])
m("""
### Using the model for cloning
You can also use this model a different way, to simply clone the audio file and reconstruct it
using machine learning. Here, we'll show a demo of that below:
""")
a2 = gr.Audio()
a2.change(reconstruct, a2, a2)
m("""
Thanks for reading this! As you may have realized, all of the "models" in this demo are fake. They are just designed to show you what is possible using Blocks 🤗.
For details of the model, read the [original report here](https://erlj.notion.site/Neural-Instrument-Cloning-from-very-few-samples-2cf41d8b630842ee8c7eb55036a1bfd6).
*Details for nerds*: this report was "launched" on:
""")
t = gr.Textbox(label="timestamp")
demo.load(get_time, [], t)
if __name__ == "__main__":
demo.launch()
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()
import gradio as gr
def greet(name):
return "Hello " + name + "!"
demo = gr.Interface(
fn=greet,
inputs=gr.Textbox(lines=2, placeholder="Name Here..."),
outputs="text",
)
if __name__ == "__main__":
app, local_url, share_url = demo.launch()
import gradio as gr
demo = gr.Blocks()
with demo:
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"
},
import gradio as gr
def image_mod(text):
return text[::-1]
demo = gr.Blocks()
with demo:
text = gr.Textbox(label="Input-Output")
btn = gr.Button("Run")
btn.click(image_mod, text, text)
print(demo.get_config_file())
if __name__ == "__main__":
demo.launch()
import gradio as gr
demo = gr.Blocks()
with demo:
gr.Textbox("Hello")
gr.Number(5)
if __name__ == "__main__":
demo.launch()
from difflib import Differ
import gradio as gr
def diff_texts(text1, text2):
d = Differ()
return [(token[2:], token[0] if token[0] != " " else None)
for token in d.compare(text1, text2)]
demo = gr.Interface(
diff_texts,
[
gr.Textbox(
lines=3,
default_value="The quick brown fox jumped over the lazy dogs."),
gr.Textbox(lines=3,
default_value="The fast brown fox jumps over lazy dogs."),
],
gr.HighlightedText(),
)
if __name__ == "__main__":
demo.launch()
demo = gr.Interface(
fn=fake_gan,
inputs=[
gr.Image(label="Initial Image (optional)"),
gr.Markdown("**Parameters**"),
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="Respacing"),
gr.Markdown("**Parameters Two**"),
gr.Slider(25,
minimum=0,
maximum=50,
label="Range_Scale (out of range RBG)"),
gr.Number(label="Respacing"),
gr.Markdown("**Parameters Three**"),
gr.Textbox(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()
+ radio
+ "</button>", # HTML
"files/titanic.csv",
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"),
import gradio as gr
def print_message(n):
return "Welcome! This page has loaded for " + n
with gr.Blocks() as demo:
t = gr.Textbox("Frank", label="Name")
t2 = gr.Textbox(label="Output")
demo.load(print_message, t, t2)
if __name__ == "__main__":
demo.launch()
import gradio as gr
def greet(name: str, repeat: int):
return "Hello " + name * repeat + "!!"
demo = gr.Interface(fn=greet,
inputs=[gr.Textbox(lines=2, max_lines=4),
gr.Number()],
outputs=gr.component("textarea"))
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()
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()
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import gradio as gr
nltk.download("vader_lexicon")
sid = SentimentIntensityAnalyzer()
def sentiment_analysis(text):
scores = sid.polarity_scores(text)
del scores["compound"]
return scores
demo = gr.Interface(
sentiment_analysis,
gr.Textbox(placeholder="Enter a positive or negative sentence here..."),
"label",
interpretation="default")
if __name__ == "__main__":
demo.launch()
import gradio as gr
title = "GPT-J-6B"
examples = [
["The tower is 324 metres (1,063 ft) tall,"],
["The Moon's orbit around Earth has"],
["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
]
demo = gr.Interface.load(
"huggingface/EleutherAI/gpt-j-6B",
inputs=gr.Textbox(lines=5, label="Input Text"),
title=title,
examples=examples,
)
if __name__ == "__main__":
demo.launch()
import gradio as gr
male_words, female_words = ["he", "his", "him"], ["she", "hers", "her"]
def gender_of_sentence(sentence):
male_count = len([word for word in sentence.split() if word.lower() in male_words])
female_count = len(
[word for word in sentence.split() if word.lower() in female_words]
)
total = max(male_count + female_count, 1)
return {"male": male_count / total, "female": female_count / total}
demo = gr.Interface(
fn=gender_of_sentence,
inputs=gr.Textbox(default_value="She went to his house to get her keys."),
outputs="label",
interpretation="default",
)
if __name__ == "__main__":
demo.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()