# Read more about micropip at https://micropip.pyodide.org/en/v0.2.2/project/api.html#micropip.install
import micropip
await micropip.install("faker")

import gradio as gr
from faker import Faker
import pandas as pd

fake = Faker()
data_types = {"Name": fake.name, "Address": fake.address, "Email": fake.email, 
              "Phone": fake.phone_number, "Job": fake.job}

def generate_data(data_type, count):
    data = [{"#": i+1, "Data": data_types[data_type]()} for i in range(count)]
    return pd.DataFrame(data)

demo = gr.Interface(
    fn=generate_data,
    inputs=[gr.Dropdown(list(data_types.keys())), gr.Slider(1, 10, value=3, step=1)],
    outputs=gr.Dataframe(wrap=True),
)
demo.launch()

# Read more about micropip at https://micropip.pyodide.org/en/v0.2.2/project/api.html#micropip.install import micropip await micropip.install("faker") import gradio as gr from faker import Faker import pandas as pd fake = Faker() data_types = {"Name": fake.name, "Address": fake.address, "Email": fake.email, "Phone": fake.phone_number, "Job": fake.job} def generate_data(data_type, count): data = [{"#": i+1, "Data": data_types[data_type]()} for i in range(count)] return pd.DataFrame(data) demo = gr.Interface( fn=generate_data, inputs=[gr.Dropdown(list(data_types.keys())), gr.Slider(1, 10, value=3, step=1)], outputs=gr.Dataframe(wrap=True), ) demo.launch()