app.py

import gradio as gr
import spotipy


###########

from vega_datasets import data

iris = data.iris()


def scatter_plot_fn(dataset):
    return gr.ScatterPlot(
        value=iris
    )


##########
def get_started():
    # redirects to spotify and comes back
    # then generates plots
    return

with gr.Blocks() as demo:
    gr.Markdown(" ## Spotify Analyzer 🥳🎉")
    gr.Markdown("This app analyzes how cool your music taste is. We dare you to take this challenge!")
    with gr.Row():
        get_started_btn = gr.Button("Get Started")
    with gr.Row():
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    plot = gr.ScatterPlot(show_label=False).style(container=True)
                with gr.Column():
                    plot = gr.ScatterPlot(show_label=False).style(container=True)
            with gr.Row():
                with gr.Column():
                    plot = gr.ScatterPlot(show_label=False).style(container=True)
                with gr.Column():
                    plot = gr.ScatterPlot(show_label=False).style(container=True)
    with gr.Row():
        gr.Markdown(" ### We have recommendations for you!")
    with gr.Row():
        gr.Dataframe(
            headers=["Song", "Album", "Artist"],
            datatype=["str", "str", "str"],
            label="Reccomended Songs",
            value=[["something", "something", "something"], ["something", "something", "something"]]  # TODO: replace with actual reccomendations once get_started() is implemeted.
        )
    demo.load(fn=scatter_plot_fn, outputs=plot)


demo.launch()