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import os |
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from pathlib import Path |
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import time |
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import gradio as gr |
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from gradio.themes import Soft |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from huggingface_hub import hf_hub_download |
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from src.infer import load_model, predict |
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os.environ.setdefault("HF_HOME", str(Path.home() / ".cache" / "huggingface")) |
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_model = None |
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def _warmup(): |
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global _model |
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if _model is not None: |
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return |
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t0 = time.time() |
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ckpt_path = hf_hub_download( |
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repo_id="rhasan/UPLME", |
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filename="UPLME_NewsEmp_tuned-lambdas.ckpt", |
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repo_type="model" |
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) |
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load_model(ckpt_path) |
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return f"Model loaded in {time.time() - t0:.1f} seconds." |
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def ci_plot(mean: float, low: float, upp: float): |
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fig, ax = plt.subplots(figsize=(6, 1.4)) |
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ax.errorbar( |
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x=mean, y=0, |
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xerr=[[mean - low], [upp - mean]], |
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fmt='o', color='blue', |
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ecolor='orange', |
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elinewidth=5, |
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capsize=8, |
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capthick=4, |
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markersize=10 |
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) |
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ax.set_xlim(0, 100) |
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ax.set_yticks([]) |
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ax.set_xlabel("Empathy Score (0-100) +/- 95% CI") |
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fig.tight_layout() |
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return fig |
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def predict_with_ci(article: str, essay: str) -> tuple[float, float, float, plt.Figure]: |
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_warmup() |
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mean, var = predict(essay, article) |
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scale = 100 / 6 |
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mean = (mean - 1) * scale |
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std = np.sqrt(var) * scale |
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ci_low = max(0.0, mean - 1.96 * std) |
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ci_upp = min(100.0, mean + 1.96 * std) |
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fig = ci_plot(mean, ci_low, ci_upp) |
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return mean, ci_low, ci_upp, fig |
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with gr.Blocks(title="UPLME", theme=Soft(primary_hue="blue")) as demo: |
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gr.Markdown("# Empathy Prediction with Uncertainty Estimation") |
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with gr.Row(): |
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with gr.Column(): |
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article_input = gr.Textbox(label="Stimulus (E.g., News Article)", lines=6) |
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essay_input = gr.Textbox(label="Response (E.g., Essay) towards the stimulus", lines=6) |
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button = gr.Button("Predict") |
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gr.Examples( |
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examples=[ |
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["A month after Hurricane Matthew, 800,000 Haitians urgently need food.", "My heart just breaks for the people who are suffering."], |
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["A month after Hurricane Matthew, 800,000 Haitians urgently need food.", "I see, but this doesn't sound too worrisome to me."], |
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], |
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inputs=[article_input, essay_input] |
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) |
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with gr.Column(): |
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output_mean = gr.Number(label="Predicted Empathy Score (0-100)", precision=2) |
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ci_low = gr.Number(label="95% CI Lower Bound", precision=2) |
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ci_upp = gr.Number(label="95% CI Upper Bound", precision=2) |
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fig = gr.Plot(show_label=False) |
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button.click(fn=predict_with_ci, inputs=[article_input, essay_input], outputs=[output_mean, ci_low, ci_upp, fig]) |
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gr.Markdown("## About") |
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gr.Markdown(""" |
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This application predicts empathy score and uncertainty estimates using the UPLME model proposed in **UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression** by **Md Rakibul Hasan, Md Zakir Hossain, Aneesh Krishna, Shafin Rahman and Tom Gedeon**. |
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- Paper: https://arxiv.org/abs/2508.03520 |
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- Code: https://github.com/hasan-rakibul/UPLME |
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""") |
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if __name__ == "__main__": |
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demo.launch(share=True) |
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