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