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import gradio as gr |
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import shap |
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from transformers import pipeline |
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import matplotlib |
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import matplotlib.pyplot as plt |
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matplotlib.use('Agg') |
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sentiment_classifier = pipeline("text-classification", return_all_scores=True) |
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def classifier(text): |
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pred = sentiment_classifier(text) |
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return {p["label"]: p["score"] for p in pred[0]} |
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def interpretation_function(text): |
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explainer = shap.Explainer(sentiment_classifier) |
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shap_values = explainer([text]) |
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scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1])) |
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scores_desc = sorted(scores, key=lambda t: t[1])[::-1] |
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scores_desc = [t for t in scores_desc if t[0] != ""] |
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fig_m = plt.figure() |
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plt.bar(x=[s[0] for s in scores_desc[:5]], |
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height=[s[1] for s in scores_desc[:5]]) |
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plt.title("Top words contributing to positive sentiment") |
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plt.ylabel("Shap Value") |
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plt.xlabel("Word") |
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return {"original": text, "interpretation": scores}, fig_m |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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input_text = gr.Textbox(label="Input Text") |
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with gr.Row(): |
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classify = gr.Button("Classify Sentiment") |
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interpret = gr.Button("Interpret") |
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with gr.Column(): |
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label = gr.Label(label="Predicted Sentiment") |
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with gr.Column(): |
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with gr.Tabs(): |
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with gr.TabItem("Display interpretation with built-in component"): |
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interpretation = gr.components.Interpretation(input_text) |
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with gr.TabItem("Display interpretation with plot"): |
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interpretation_plot = gr.Plot() |
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classify.click(classifier, input_text, label) |
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interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot]) |
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demo.launch() |
