Create app.py

app.py

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+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()