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* Add step to deploy to spaces on release * Import package not modules * Lint * Hacky solution * Add app file * Github actions script * Fix bucket url * Update versioning * Fic typo in script * Change path * Fix quote * Clean up action * Fix keys * Formatting + comment * Fix all_demo creation * lint ui * Get latest pypi version and use only 20 demos * Test version change works correctly * Fix gradio version * Fix typo * Use github sha to rebuild on every commit * Get sha the proper way * Change where env is accessed * Fix typo in cp * Undo changes to version.txt * Add matrix transpose
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
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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# Dimensions are (batch size, text size, number of classes)
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# Since we care about positive sentiment, use index 1
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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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# Filter out empty string added by shap
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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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if __name__ == "__main__":
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demo.launch() |