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import gradio as gr | |
from transformers import pipeline | |
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 contains (word, score) pairs | |
# Format expected by gr.components.Interpretation | |
return {"original": text, "interpretation": scores} | |
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(): | |
interpretation = gr.components.Interpretation(input_text) | |
classify.click(classifier, input_text, label) | |
interpret.click(interpretation_function, input_text, interpretation) | |
demo.launch(share = True) |