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,
    ).style(color_map={"+": "red", "-": "green"}),
)
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(2, 20, value=4),
        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()

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, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        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()

import gradio as gr

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

with demo:
    default_json = {"a": "a"}

    num = gr.Variable(value=0)
    squared = gr.Number(value=0)
    btn = gr.Button("Next Square", elem_id="btn").style(rounded=False)

    stats = gr.Variable(value=default_json)
    table = gr.JSON()

    def increase(var, stats_history):
        var += 1
        stats_history[str(var)] = var**2
        return var, var**2, stats_history, stats_history

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

if __name__ == "__main__":
    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",
)
if __name__ == "__main__":
    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, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        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()

import gradio as gr


def func(slider_1, slider_2):
    return slider_1 + slider_2 * 5


demo = gr.Interface(
    func,
    [
        gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label="Random Big Range"),
        gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label="Random only multiple of 0.05 allowed"),
        gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label="Random only multiples of 0.25 allowed"),
        gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label="Random between -100 and 100 step 3"),
        gr.Slider(minimum=-100, maximum=100, randomize=True, label="Random between -100 and 100"),
        gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),
    ],
    "number",
    interpretation="default"
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr


def func(slider_1, slider_2):
    return slider_1 * 5 + slider_2


with gr.Blocks() as demo:
    slider = gr.Slider(minimum=-10.2, maximum=15, label="Random Slider (Static)", randomize=True)
    slider_1 = gr.Slider(minimum=100, maximum=200, label="Random Slider (Input 1)", randomize=True)
    slider_2 = gr.Slider(minimum=10, maximum=23.2, label="Random Slider (Input 2)", randomize=True)
    slider_3 = gr.Slider(value=3, label="Non random slider")
    btn = gr.Button("Run")
    btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())

if __name__ == "__main__":
    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, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        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()

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, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        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()

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, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        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()

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)

if __name__ == "__main__":
    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, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        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()

import gradio as gr
import os


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


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

demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image",
    flagging_options=["blurry", "incorrect", "other"], examples=[
        os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
        os.path.join(os.path.dirname(__file__), "images/logo.png")
        ])

if __name__ == "__main__":
    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",
)
if __name__ == "__main__":
    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/cantina.wav"),
                    os.path.join(os.path.dirname(__file__), "audio/recording1.wav")
        ], cache_examples=True)

if __name__ == "__main__":
    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")],
    ],
)

if __name__ == "__main__":
    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()],
    ],
)

if __name__ == "__main__":
    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]]],
    ],
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr

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

with demo:
    default_json = {"a": "a"}

    num = gr.Variable(value=0)
    squared = gr.Number(value=0)
    btn = gr.Button("Next Square", elem_id="btn").style(rounded=False)

    stats = gr.Variable(value=default_json)
    table = gr.JSON()

    def increase(var, stats_history):
        var += 1
        stats_history[str(var)] = var**2
        return var, var**2, stats_history, stats_history

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

if __name__ == "__main__":
    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(value="Run")
    btn.click(plot, [speed, angle], output)

if __name__ == "__main__":
    demo.launch()

import gradio as gr
import cv2
import numpy as np
import random


# Convert decimal color to hexadecimal color
def RGB_to_Hex(rgb):
    color = "#"
    for i in rgb:
        num = int(i)
        color += str(hex(num))[-2:].replace("x", "0").upper()
    return color


# Randomly generate light or dark colors
def random_color(is_light=True):
    return (
        random.randint(0, 127) + int(is_light) * 128,
        random.randint(0, 127) + int(is_light) * 128,
        random.randint(0, 127) + int(is_light) * 128,
    )


def switch_color(color_style):
    if color_style == "light":
        is_light = True
    elif color_style == "dark":
        is_light = False
    back_color_ = random_color(is_light)  # Randomly generate colors
    back_color = RGB_to_Hex(back_color_)  # Convert to hexadecimal

    # Draw color pictures.
    w, h = 50, 50
    img = np.zeros((h, w, 3), np.uint8)
    cv2.rectangle(img, (0, 0), (w, h), back_color_, thickness=-1)

    return back_color, back_color, img


inputs = [gr.Radio(["light", "dark"], value="light")]

outputs = [
    gr.ColorPicker(label="color"),
    gr.Textbox(label="hexadecimal color"),
    gr.Image(type="numpy", label="color picture"),
]

title = "Color Generator"
description = (
    "Click the Submit button, and a dark or light color will be randomly generated."
)

demo = gr.Interface(
    fn=switch_color,
    inputs=inputs,
    outputs=outputs,
    title=title,
    description=description,
)

if __name__ == "__main__":
    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, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        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()

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."],
    ],
)

if __name__ == "__main__":
    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,
                api_name="xray_model"
            )

        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,
                api_name="ct_model"
            )

    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(),
    )

if __name__ == "__main__":
    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(value="Run")
    btn.click(plot, [speed, angle], output)

if __name__ == "__main__":
    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"),
)

if __name__ == "__main__":
    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(value="Run")
    btn.click(plot, [speed, angle], output)

if __name__ == "__main__":
    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, None, None, None],
        [1, cheetah, 2, None, None, None],
        [4, cheetah, 42, None, None, None],
        [5, cheetah, 23, None, None, None],
        [4, cheetah, 11, None, None, None],
        [3, cheetah, 1, None, None, None],
    ],
)

if __name__ == "__main__":
    demo.launch()