import gradio as gr
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="dark")

def generate_plot():
    # Simulate data from a bivariate Gaussian
    n = 10000
    mean = [0, 0]
    cov = [(2, .4), (.4, .2)]
    rng = np.random.RandomState(0)
    x, y = rng.multivariate_normal(mean, cov, n).T

    # Create the plot
    fig, ax = plt.subplots(figsize=(6, 6))
    sns.scatterplot(x=x, y=y, s=5, color=".15", ax=ax)
    sns.histplot(x=x, y=y, bins=50, pthresh=.1, cmap="mako", ax=ax)
    sns.kdeplot(x=x, y=y, levels=5, color="w", linewidths=1, ax=ax)

    return fig

# Gradio interface
demo = gr.Interface(
    fn=generate_plot,
    inputs=[],
    outputs=gr.Plot(label="Bivariate Gaussian Plot"),
    title="Bivariate Distribution Visualizer",
    description="Generates a scatterplot, histogram, and KDE contours from a simulated bivariate Gaussian distribution."
)

demo.launch()