app.py

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()