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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def plot_3d(max_t, num_points, sentence, show_words): | |
| # Generate spiral | |
| t = np.linspace(0, max_t, num_points) | |
| x = np.sin(t) | |
| y = np.cos(t) | |
| z = t | |
| # Plot | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111, projection="3d") | |
| ax.plot(x, y, z, label="3D Spiral") | |
| ax.legend() | |
| # Add token labels if requested | |
| if show_words and sentence.strip(): | |
| tokens = sentence.strip().split() | |
| idxs = np.linspace(0, len(t) - 1, len(tokens), dtype=int) | |
| for i, token in zip(idxs, tokens): | |
| ax.text(x[i], y[i], z[i], token, fontsize=9, color="red") | |
| return fig | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 3D Hidden States Visualization") | |
| gr.Markdown( | |
| """ | |
| This plot shows a **3D spiral**, generated using sine and cosine for the x/y axes | |
| and a linear sequence for the z-axis: | |
| - $x = \\sin(t)$ | |
| - $y = \\cos(t)$ | |
| - $z = t$ | |
| Together, these equations trace a spiral around the z-axis. | |
| Think of it as an analogy for **hidden states in a neural network**, | |
| which evolve over time (the z-axis), while oscillating in complex patterns (x & y axes). | |
| ✨ Try typing your own sentence — each word will be placed along the spiral, | |
| showing how tokens could be mapped into hidden state space. | |
| """ | |
| ) | |
| with gr.Row(): | |
| max_t = gr.Slider(5, 50, value=20, step=1, label="Spiral Length") | |
| num_points = gr.Slider(100, 2000, value=500, step=50, label="Number of Points") | |
| sentence = gr.Textbox(value="I love hidden states in transformers", label="Sentence") | |
| show_words = gr.Checkbox(label="Show Tokens", value=True) | |
| plot = gr.Plot() | |
| btn = gr.Button("Generate Plot") | |
| btn.click(plot_3d, inputs=[max_t, num_points, sentence, show_words], outputs=plot) | |
| if __name__ == "__main__": | |
| demo.launch() | |