import gradio as gr


def greet(name):
    return "Hello " + name + "!!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")

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(
            label="Initial text",
            lines=3,
            value="The quick brown fox jumped over the lazy dogs.",
        ),
        gr.Textbox(
            label="Text to compare",
            lines=3,
            value="The fast brown fox jumps over lazy dogs.",
        ),
    ],
    gr.HighlightedText(label="Diff", combine_adjacent=True),
)
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(2, 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],
    ],
)

demo.launch()

import gradio as gr


def tax_calculator(income, marital_status, assets):
    tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]
    total_deductible = sum(assets["Cost"])
    taxable_income = income - total_deductible

    total_tax = 0
    for bracket, rate in tax_brackets:
        if taxable_income > bracket:
            total_tax += (taxable_income - bracket) * rate / 100

    if marital_status == "Married":
        total_tax *= 0.75
    elif marital_status == "Divorced":
        total_tax *= 0.8

    return round(total_tax)


demo = gr.Interface(
    tax_calculator,
    [
        "number",
        gr.Radio(["Single", "Married", "Divorced"]),
        gr.Dataframe(
            headers=["Item", "Cost"],
            datatype=["str", "number"],
            label="Assets Purchased this Year",
        ),
    ],
    "number",
    examples=[
        [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]],
        [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]],
    ],
)

demo.launch()

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import gradio as gr

current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))


def encode_age(df):
    df.Age = df.Age.fillna(-0.5)
    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
    categories = pd.cut(df.Age, bins, labels=False)
    df.Age = categories
    return df


def encode_fare(df):
    df.Fare = df.Fare.fillna(-0.5)
    bins = (-1, 0, 8, 15, 31, 1000)
    categories = pd.cut(df.Fare, bins, labels=False)
    df.Fare = categories
    return df


def encode_df(df):
    df = encode_age(df)
    df = encode_fare(df)
    sex_mapping = {"male": 0, "female": 1}
    df = df.replace({"Sex": sex_mapping})
    embark_mapping = {"S": 1, "C": 2, "Q": 3}
    df = df.replace({"Embarked": embark_mapping})
    df.Embarked = df.Embarked.fillna(0)
    df["Company"] = 0
    df.loc[(df["SibSp"] > 0), "Company"] = 1
    df.loc[(df["Parch"] > 0), "Company"] = 2
    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
    df = df[
        [
            "PassengerId",
            "Pclass",
            "Sex",
            "Age",
            "Fare",
            "Embarked",
            "Company",
            "Survived",
        ]
    ]
    return df


train = encode_df(data)

X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]

num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
    X_all, y_all, test_size=num_test, random_state=23
)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)


def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
    if passenger_class is None or embark_point is None:
        return None
    df = pd.DataFrame.from_dict(
        {
            "Pclass": [passenger_class + 1],
            "Sex": [0 if is_male else 1],
            "Age": [age],
            "Company": [
                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
            ],
            "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(0, 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,
)

demo.launch()

import gradio as gr


def update(name):
    return f"Welcome to Gradio, {name}!"

demo = gr.Blocks()

with demo:
    gr.Markdown(
    """
    # Hello World!
    Start typing below to see the output.
    """)
    inp = gr.Textbox(placeholder="What is your name?")
    out = gr.Textbox()
    
    inp.change(fn=update, 
               inputs=inp, 
               outputs=out)

demo.launch()

import numpy as np

import gradio as gr

notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]


def generate_tone(note, octave, duration):
    sr = 48000
    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
    frequency = a4_freq * 2 ** (tones_from_a4 / 12)
    duration = int(duration)
    audio = np.linspace(0, duration, duration * sr)
    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
    return sr, audio


demo = gr.Interface(
    generate_tone,
    [
        gr.Dropdown(notes, type="index"),
        gr.Slider(4, 6, step=1),
        gr.Textbox(value=1, type="number", label="Duration in seconds"),
    ],
    "audio",
)

demo.launch()

import gradio as gr


def change_textbox(choice):
    if choice == "short":
        return gr.Textbox.update(lines=2, visible=True)
    elif choice == "long":
        return gr.Textbox.update(lines=8, visible=True)
    else:
        return gr.Textbox.update(visible=False)


with gr.Blocks() as demo:
    radio = gr.Radio(
        ["short", "long", "none"], label="What kind of essay would you like to write?"
    )
    text = gr.Textbox(lines=2, interactive=True)

    radio.change(fn=change_textbox, inputs=radio, outputs=text)

demo.launch()

import gradio as gr


def image_mod(image):
    return image.rotate(45)


demo = gr.Interface(image_mod, gr.inputs.Image(type="pil"), "image")

demo.launch()

import gradio as gr
import random
import time


def xray_model(diseases, img):
    time.sleep(4)
    return [{disease: random.random() for disease in diseases}]


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") as x_tab:
            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")
            ct_progress = gr.StatusTracker(cover_container=True)
            ct_run.click(
                ct_model,
                inputs=[disease, ct_scan],
                outputs=ct_results,
                status_tracker=ct_progress,
            )

    upload_btn = gr.Button("Upload Results")
    upload_btn.click(
        lambda ct, xr: time.sleep(5),
        inputs=[ct_results, xray_results],
        outputs=[],
        status_tracker=gr.StatusTracker(),
    )

demo.launch()

import gradio as gr


def video_flip(video):
    return video


demo = gr.Interface(video_flip, gr.Video(), "playable_video")

demo.launch()

from math import log2, pow
import os

import numpy as np
from scipy.fftpack import fft

import gradio as gr

A4 = 440
C0 = A4 * pow(2, -4.75)
name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]


def get_pitch(freq):
    h = round(12 * log2(freq / C0))
    n = h % 12
    return name[n]


def main_note(audio):
    rate, y = audio
    if len(y.shape) == 2:
        y = y.T[0]
    N = len(y)
    T = 1.0 / rate
    x = np.linspace(0.0, N * T, N)
    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=[
        [os.path.join(os.path.dirname(__file__),"audio/recording1.wav")],
        [os.path.join(os.path.dirname(__file__),"audio/cantina.wav")],
    ],
    interpretation="default",
)

demo.launch()

import os

import numpy as np

import gradio as gr


def reverse_audio(audio):
    sr, data = audio
    return (sr, np.flipud(data))


demo = gr.Interface(fn=reverse_audio, 
                    inputs="microphone", 
                    outputs="audio", 
                    examples=os.path.join(os.path.dirname(__file__), "audio"))

demo.launch()

from zipfile import ZipFile

import gradio as gr


def zip_to_json(file_obj):
    files = []
    with ZipFile(file_obj.name) as zfile:
        for zinfo in zfile.infolist():
            files.append(
                {
                    "name": zinfo.filename,
                    "file_size": zinfo.file_size,
                    "compressed_size": zinfo.compress_size,
                }
            )
    return files


demo = gr.Interface(zip_to_json, "file", "json")

demo.launch()

import os
from zipfile import ZipFile

import gradio as gr


def zip_two_files(file1, file2):
    with ZipFile("tmp.zip", "w") as zipObj:
        zipObj.write(file1.name, "file1")
        zipObj.write(file2.name, "file2")
    return "tmp.zip"


demo = gr.Interface(
    zip_two_files,
    ["file", "file"],
    "file",
    examples=[
        [os.path.join(os.path.dirname(__file__),"files/titanic.csv"), 
         os.path.join(os.path.dirname(__file__),"files/titanic.csv")],
    ],
)

demo.launch()

import gradio as gr


def filter_records(records, gender):
    return records[records["gender"] == gender]


demo = gr.Interface(
    filter_records,
    [
        gr.Dataframe(
            headers=["name", "age", "gender"],
            datatype=["str", "number", "str"],
            row_count=5,
            col_count=(3, "fixed")
        ),
        gr.Dropdown(["M", "F", "O"]),
    ],
    "dataframe",
    description="Enter gender as 'M', 'F', or 'O' for other.",
)

demo.launch()

import numpy as np

import gradio as gr


def transpose(matrix):
    return matrix.T


demo = gr.Interface(
    transpose,
    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
    "numpy",
    examples=[
        [np.zeros((3, 3)).tolist()],
        [np.ones((2, 2)).tolist()],
        [np.random.randint(0, 10, (3, 10)).tolist()],
        [np.random.randint(0, 10, (10, 3)).tolist()],
        [np.random.randint(0, 10, (10, 10)).tolist()],
    ],
)

demo.launch()

import random
import os
import gradio as gr


def fraud_detector(card_activity, categories, sensitivity):
    activity_range = random.randint(0, 100)
    drop_columns = [
        column for column in ["retail", "food", "other"] 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"], value=["retail", "food", "other"]
        ),
        gr.Slider(1, 3),
    ],
    [
        "dataframe",
        gr.Timeseries(x="time", y=["retail", "food", "other"]),
        gr.Label(label="Fraud Level"),
    ],
    examples=[
        [os.path.join(os.path.dirname(__file__), "fraud.csv"), ["retail", "food", "other"], 1.0],
    ],
)
demo.launch()

import spacy
from spacy import displacy

import gradio as gr

nlp = spacy.load("en_core_web_sm")


def text_analysis(text):
    doc = nlp(text)
    html = displacy.render(doc, style="dep", page=True)
    html = (
        "
"
        + html
        + "
"
    )
    pos_count = {
        "char_count": len(text),
        "token_count": 0,
    }
    pos_tokens = []

    for token in doc:
        pos_tokens.extend([(token.text, token.pos_), (" ", None)])

    return pos_tokens, pos_count, html


demo = gr.Interface(
    text_analysis,
    gr.Textbox(placeholder="Enter sentence here..."),
    ["highlight", "json", "html"],
    examples=[
        ["What a beautiful morning for a walk!"],
        ["It was the best of times, it was the worst of times."],
    ],
)

demo.launch()

# This demo needs to be run from the repo folder.
# python demo/fake_gan/run.py
import os
import random
import time

import gradio as gr


def fake_gan(count, *args):
    time.sleep(1)
    images = [
        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",
            ]
        )
        for _ in range(int(count))
    ]
    return images


cheetah = os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg")

demo = gr.Interface(
    fn=fake_gan,
    inputs=[
        gr.Number(label="Generation Count"),
        gr.Image(label="Initial Image (optional)"),
        gr.Slider(0, 50, 25, label="TV_scale (for smoothness)"),
        gr.Slider(0, 50, 25, label="Range_Scale (out of range RBG)"),
        gr.Number(label="Seed"),
        gr.Number(label="Respacing"),
    ],
    outputs=gr.Gallery(label="Generated Images"),
    title="FD-GAN",
    description="This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.",
    examples=[
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
        [2, cheetah, 12, 12, 4, 4],
    ],
)

demo.launch()

import random

import gradio as gr


def chat(message, history):
    history = history or []
    if message.startswith("How many"):
        response = random.randint(1, 10)
    elif message.startswith("How"):
        response = random.choice(["Great", "Good", "Okay", "Bad"])
    elif message.startswith("Where"):
        response = random.choice(["Here", "There", "Somewhere"])
    else:
        response = "I don't know"
    history.append((message, response))
    return history, history

chatbot = gr.Chatbot(color_map=("green", "gray"))
demo = gr.Interface(
    chat,
    ["text", "state"],
    [chatbot, "state"],
    allow_screenshot=False,
    allow_flagging="never",
)
demo.launch()

import time
import gradio as gr
import os


def load_mesh(mesh_file_name):
    time.sleep(2)
    return mesh_file_name


inputs = gr.Model3D()
outputs = gr.Model3D(clear_color=[0.8, 0.2, 0.2, 1.0])

demo = gr.Interface(
    fn=load_mesh,
    inputs=inputs,
    outputs=outputs,
    examples=[
        [os.path.join(os.path.dirname(__file__), "files/Bunny.obj")],
        [os.path.join(os.path.dirname(__file__), "files/Duck.glb")],
        [os.path.join(os.path.dirname(__file__), "files/Fox.gltf")],
        [os.path.join(os.path.dirname(__file__), "files/face.obj")],
    ],
    cache_examples=True,
)

demo.launch()

from math import sqrt

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
import pandas as pd
import bokeh.plotting as bk
from bokeh.models import ColumnDataSource
from bokeh.embed import json_item

import gradio as gr


def outbreak(plot_type, r, month, countries, social_distancing):
    months = ["January", "February", "March", "April", "May"]
    m = months.index(month)
    start_day = 30 * m
    final_day = 30 * (m + 1)
    x = np.arange(start_day, final_day + 1)
    pop_count = {"USA": 350, "Canada": 40, "Mexico": 300, "UK": 120}
    if social_distancing:
        r = sqrt(r)
    df = pd.DataFrame({'day': x})
    for country in countries:
        df[country] = ( x ** (r) * (pop_count[country] + 1))
        

    if plot_type == "Matplotlib":
        fig = plt.figure()
        plt.plot(df['day'], df[countries].to_numpy())
        plt.title("Outbreak in " + month)
        plt.ylabel("Cases")
        plt.xlabel("Days since Day 0")
        plt.legend(countries)
        return fig
    elif plot_type == "Plotly":
        fig = px.line(df, x='day', y=countries)
        fig.update_layout(title="Outbreak in " + month,
                   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(1, 4, 3.2, label="R"),
        gr.Dropdown(["January", "February", "March", "April", "May"], label="Month"),
        gr.CheckboxGroup(["USA", "Canada", "Mexico", "UK"], label="Countries", 
                         value=["USA", "Canada"]),
        gr.Checkbox(label="Social Distancing?"),
    ]
outputs = gr.Plot()

demo = gr.Interface(fn=outbreak, inputs=inputs, outputs=outputs)


demo.launch()

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np

import gradio as gr


def plot(v, a):
    g = 9.81
    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(1, 30, 25, label="Speed")
        angle = gr.Slider(0, 90, 45, label="Angle")
    output = gr.Plot()
    btn = gr.Button("Run")
    btn.click(plot, [speed, angle], output)

demo.launch()

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np

import gradio as gr


def plot_forecast(final_year, companies, noise, show_legend, point_style):
    start_year = 2020
    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)
    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(1, 100, label="Noise Level"),
        gr.Checkbox(label="Show Legend"),
        gr.Dropdown(["cross", "line", "circle"], label="Style"),
    ],
    gr.Plot(label="forecast"),
)

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", interactive=False)
    btn = gr.Button("Submit")
    btn.click(lambda a: a, inputs=[txt], outputs=[txt_2])

demo.launch()

import gradio as gr

demo = gr.Blocks(css="#btn {color: red}")

with demo:
    num = gr.Variable(value=0)
    squared = gr.Number(value=0).style(text_color="blue", container_bg_color="yellow")
    btn = gr.Button("Next Square", elem_id="btn").style(rounded=False, bg_color="purple")

    def increase(var):
        var += 1
        return var, var**2

    btn.click(increase, [num], [num, squared])

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"],
)
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, max_lines=6, label="Input Text"),
    title=title,
    examples=examples,
)

demo.launch()

import numpy as np
import gradio as gr

demo = gr.Blocks()


def flip_text(x):
    return x[::-1]

def flip_image(x):
    return np.fliplr(x)


with demo:
    gr.Markdown("Flip text or image files using this demo.")
    with gr.Tabs():
        with gr.TabItem("Flip Text"):
            text_input = gr.Textbox()
            text_output = gr.Textbox()
            text_button = gr.Button("Flip")
        with gr.TabItem("Flip Image"):
            with gr.Row():
                image_input = gr.Image()
                image_output = gr.Image()
            image_button = gr.Button("Flip")
    
    text_button.click(flip_text, inputs=text_input, outputs=text_output)
    image_button.click(flip_image, inputs=image_input, outputs=image_output)
    
demo.launch()

from transformers import pipeline

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:
    audio_file = gr.Audio(type="filepath")
    text = gr.Textbox()
    label = gr.Label()

    b1 = gr.Button("Recognize Speech")
    b2 = gr.Button("Classify Sentiment")

    b1.click(speech_to_text, inputs=audio_file, outputs=text)
    b2.click(text_to_sentiment, inputs=text, outputs=label)

demo.launch()

import gradio as gr

title = "GPT-J-6B"

tts_examples = [
    "I love learning machine learning",
    "How do you do?",
]

tts_demo = gr.Interface.load(
    "huggingface/facebook/fastspeech2-en-ljspeech",
    title=None,
    examples=tts_examples,
    description="Give me something to say!",
)

stt_demo = gr.Interface.load(
    "huggingface/facebook/wav2vec2-base-960h",
    title=None,
    inputs="mic",
    description="Let me try to guess what you're saying!",
)

demo = gr.TabbedInterface([tts_demo, stt_demo], ["Text-to-speech", "Speech-to-text"])

demo.launch()