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import os
from pathlib import Path
import time
import gradio as gr
from gradio.themes import Soft
import numpy as np
import matplotlib.pyplot as plt
from huggingface_hub import hf_hub_download
from src.infer import load_model, predict
os.environ.setdefault("HF_HOME", str(Path.home() / ".cache" / "huggingface"))
_model = None
def _warmup():
global _model
if _model is not None:
return
t0 = time.time()
ckpt_path = hf_hub_download(
repo_id="rhasan/UPLME",
filename="UPLME_NewsEmp_tuned-lambdas.ckpt",
repo_type="model"
)
load_model(ckpt_path)
return f"Model loaded in {time.time() - t0:.1f} seconds."
def ci_plot(mean: float, low: float, upp: float):
fig, ax = plt.subplots(figsize=(6, 1))
ax.hlines(1, 0, 100, linewidht=2, alpha=0.15)
ax.hlines(1, low, upp, linewidht=6)
ax.plot([mean], [1], "o")
ax.set_xlim(0, 100)
ax.set_yticks([])
ax.set_xlabel("Empathy Score (0-100)")
fig.tight_layout()
return fig
def predict_with_ci(essay: str, article: str) -> tuple[float, float, float, plt.Figure]:
_warmup()
mean, var = predict(essay, article)
# scores were originally in [1, 7]
# lets scale them to [0, 100]
mean = (mean - 1) / 6 * 100
std = np.sqrt(var)
ci_low = max(0.0, mean - 1.96 * std)
ci_upp = min(100.0, mean + 1.96 * std)
fig = ci_plot(mean, ci_low, ci_upp)
return mean, ci_low, ci_upp, fig
with gr.Blocks(title="UPLME", theme=Soft(primary_hue="blue")) as demo:
gr.Markdown("# Empathy Prediction with Uncertainty Estimation")
with gr.Row():
with gr.Column():
essay_input = gr.Textbox(label="Response (E.g., Essay) towards the stimulus", lines=10, placeholder="Enter the essay text here...")
article_input = gr.Textbox(label="Stimulus (E.g., News Article)", lines=10, placeholder="Enter the article text here...")
button = gr.Button("Predict")
with gr.Column():
output_mean = gr.Number(label="Predicted Empathy Score (0-100)", precision=2)
ci_low = gr.Number(label="95% CI Lower Bound", precision=2)
ci_upp = gr.Number(label="95% CI Upper Bound", precision=2)
fig = gr.Plot(label="Prediction +/- 95% CI")
button.click(fn=predict_with_ci, inputs=[essay_input, article_input], outputs=[output_mean, ci_low, ci_upp, fig])
gr.Markdown("## About")
gr.Markdown("""
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**.
- Paper: https://arxiv.org/abs/2508.03520
- Code: https://github.com/hasan-rakibul/UPLME
""")
if __name__ == "__main__":
demo.launch() |