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Runtime error
| import gradio as gr | |
| from transformers import pipeline | |
| import shap | |
| 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() | |
| demo.launch() |