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