SemSup-XC / main2.py
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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)