Gradio demo

gradio_1.py

def find_palindroms(text: str) -> str:
    # A placeholder for the palindromes found
    palindromes = []

    # loop through all the words in the text
    for word in text.split():
        # and check if they are palindromes
        if word == word[::-1]:
            # if they are, add them to the list
            palindromes.append(word)

    # Return the list of palindromes as a string
    return "|".join(palindromes)


# 1. Import Gradio
import gradio as gr

# 2. Create a Gradio interface with prefered input and output widgets
app = gr.Interface(find_palindroms, inputs=["text"], outputs=["text"])

# 3. Launch the app. Bingo!
app.launch()

gradio_2.py

from email.policy import default
from nltk.corpus import stopwords

# Add more inputs to the find_palindroms function.
def find_palindroms(text: str, remove_stops: bool, max_output: int, lang: str) -> str:
    palindromes = []

    if remove_stops:
        stops = set(stopwords.words(lang))
        words = [word for word in text.split() if word not in stops]
    else:
        words = text.split()

    for word in words:
        if word == word[::-1]:
            palindromes.append(word)

    if len(palindromes) > max_output:
        palindromes = palindromes[:max_output]

    return "|".join(palindromes)


import gradio as gr

# Create more widgets in the Gradio app.
app = gr.Interface(
    find_palindroms,
    inputs=[
        gr.Textbox(label="Text", value=""),
        gr.Checkbox(label="Remove stopwords", value=False),
        gr.Slider(label="Max output", value=3, minimum=1, maximum=10),
        gr.Radio(choices=["english", "french", "spanish"], label="Language"),
    ],
    outputs=["text"],
)

app.launch()

gradio_sklearn.py

import pickle
import gradio as gr

"""
# You can use this block to train and save a model.

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
# Load the data
data = pd.read_csv("data.csv")
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    data[["temp", "humidity"]], data["rainfall"], test_size=0.2, random_state=42
)
# Train the model
model = LinearRegression().fit(X_train, y_train)
# Save the model
pickle.dump(model, open("model.pkl", "wb"))
"""


def predict_rainfall(temp, humidity):
    """
    Predict the rainfall based on the temperature and humidity.
    This function uses a pickled scikitlearn model to predict the rainfall.
    The output is a string with the predicted rainfall.
    """
    # Load the model
    model = pickle.load(open("model.pkl", "rb"))
    # Predict the rainfall
    rainfall = model.predict([[temp, humidity]])
    # Return the rainfall
    return rainfall


app = gr.Interface(
    predict_rainfall,
    inputs=[
        gr.Number(value=20, label="Temperature"),
        gr.Number(value=50, label="Humidity"),
    ],
    outputs=[gr.Text(label="Rainfall")],
)

app.launch()