from functools import partial

import gradio as gr
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import numpy as np
from sklearn import datasets, manifold


SEED = 0
N_COMPONENTS = 2
np.random.seed(SEED)


def get_circles(n_samples):
    X, color = datasets.make_circles(
        n_samples=n_samples,
        factor=0.5,
        noise=0.05,
        random_state=SEED
    )
    return X, color


def get_s_curve(n_samples):
    X, color = datasets.make_s_curve(n_samples=n_samples, random_state=SEED)
    X[:, 1], X[:, 2] = X[:, 2], X[:, 1].copy()
    return X, color


def get_uniform_grid(n_samples):
    x = np.linspace(0, 1, int(np.sqrt(n_samples)))
    xx, yy = np.meshgrid(x, x)
    X = np.hstack(
        [
            xx.ravel().reshape(-1, 1),
            yy.ravel().reshape(-1, 1),
        ]
    )
    color = xx.ravel()
    return X, color


DATA_MAPPING = {
    'Circles': get_circles,
    'S-curve': get_s_curve,
    'Uniform Grid': get_uniform_grid,
}



def plot_data(dataset: str, perplexity: int, n_samples: int, tsne: bool):
    if isinstance(perplexity, dict):
        perplexity = perplexity['value']
    else:
        perplexity = int(perplexity)

    X, color = DATA_MAPPING[dataset](n_samples)
    if tsne:
        tsne = manifold.TSNE(
            n_components=N_COMPONENTS,
            init="random",
            random_state=0,
            perplexity=perplexity,
            n_iter=400,
        )
        Y = tsne.fit_transform(X)
    else:
        Y = X

    fig, ax = plt.subplots(figsize=(7, 7))

    ax.scatter(Y[:, 0], Y[:, 1], c=color)
    ax.xaxis.set_major_formatter(NullFormatter())
    ax.yaxis.set_major_formatter(NullFormatter())
    ax.axis("tight")

    return fig


title = "t-SNE: The effect of various perplexity values on the shape"
description = """
t-Stochastic Neighborhood Embedding ([t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)) is a powerful technique dimensionality reduction and visualization of high dimensional datasets.

One of the key parameters in t-SNE is perplexity, which controls the number of nearest neighbors used to represent each data point in the low-dimensional space.

In this illustration, we explore the impact of various perplexity values on t-SNE visualizations using three commonly used datasets: Concentric Circles, S-curve and Uniform Grid.

By comparing the resulting visualizations, we demonstrate how changing the perplexity value affects the shape of the visualization.

Created by [@Hnabil](https://huggingface.co/Hnabil) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html)
"""



with gr.Blocks(title=title) as demo:
    gr.HTML(f"<b>{title}</b>")
    gr.Markdown(description)

    input_data = gr.Radio(
        list(DATA_MAPPING),
        value="Circles",
        label="dataset"
    )
    n_samples = gr.Slider(
        minimum=100,
        maximum=1000,
        value=150,
        step=25,
        label='Number of Samples'
    )
    perplexity = gr.Slider(
        minimum=2,
        maximum=100,
        value=5,
        step=1,
        label='Perplexity'
    )
    with gr.Row():
        with gr.Column():
            plot = gr.Plot(label="Original data")
            fn = partial(plot_data, tsne=False)
            input_data.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
            n_samples.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
            demo.load(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
        with gr.Column():
            plot = gr.Plot(label="t-SNE")
            fn = partial(plot_data, tsne=True)
            input_data.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
            perplexity.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
            n_samples.change(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)
            demo.load(fn=fn, inputs=[input_data, perplexity, n_samples], outputs=plot)


demo.launch()