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import os |
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import time |
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import gradio as gr |
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import numpy as np |
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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", "/data/.huggingface") |
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_model = None |
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_ckpt_path = None |
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def _warmup(): |
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global _model, _ckpt_path |
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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/empathy", |
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filename="UPLME_NewsEmp_tuned-lambdas.ckpt", |
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repo_type="model", |
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local_dir="/data/uplme_ckpt" |
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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 predict_with_ci(essay: str, article: str) -> dict: |
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_warmup() |
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mean, var = predict(essay, article) |
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mean = (mean - 1) / 6 * 100 |
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std = np.sqrt(var) |
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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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return {"mean": mean, "ci": (ci_low, ci_upp)} |
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with gr.Blocks(title="Empathy Prediction") 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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essay_input = gr.Textbox(label="Essay", lines=10, placeholder="Enter the essay text here...") |
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article_input = gr.Textbox(label="Article", lines=10, placeholder="Enter the article text here...") |
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button = gr.Button("Predict") |
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with gr.Column(): |
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output_mean = gr.Number(label="Predicted Empathy Mean", precision=4) |
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ci = gr.Number(label="95\% CI", precision=4) |
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button.click(fn=predict_with_ci, inputs=[essay_input, article_input], outputs=[output_mean, ci]) |
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if __name__ == "__main__": |
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demo.launch() |
