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| import streamlit as st | |
| import streamlit.components.v1 as components | |
| from transformers import (AutoModelForSequenceClassification, AutoTokenizer, | |
| pipeline) | |
| import shap | |
| from PIL import Image | |
| import time | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| output_width = 800 | |
| output_height = 300 | |
| rescale_logits = False | |
| st.set_page_config(page_title='Text Classification with Shap') | |
| st.title('Interpreting HF Pipeline Text Classification with Shap') | |
| form = st.sidebar.form("Model Selection") | |
| form.header('Model Selection') | |
| model_name = form.text_input("Enter the name of the text classification LLM (note: model must be fine-tuned on a text classification task)", value = "Hate-speech-CNERG/bert-base-uncased-hatexplain") | |
| form.form_submit_button("Submit") | |
| def load_model(model_name): | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| return tokenizer, model | |
| tokenizer, model = load_model(model_name) | |
| pred = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None) | |
| explainer = shap.Explainer(pred, rescale_to_logits = rescale_logits) | |
| col1, col2, col3 = st.columns(3) | |
| text = col1.text_area("Enter text input", value = "Classify me.") | |
| start_time = time.time() | |
| result = pred(text) | |
| inference_time = time.time() - start_time | |
| col3.write('') | |
| col3.write(f'**Inference Time:** {inference_time: .4f}') | |
| top_pred = result[0][0]['label'] | |
| col2.write('') | |
| for label in result[0]: | |
| col2.write(f'**{label["label"]}**: {label["score"]: .2f}') | |
| shap_values = explainer([text]) | |
| explanation_time = shap_values.compute_time | |
| col3.write('') | |
| col3.write(f'**Explanation Time:** {explanation_time: .4f}') | |
| force_plot = shap.plots.text(shap_values, display=False) | |
| bar_plot = shap.plots.bar(shap_values[0, :, top_pred], order=shap.Explanation.argsort.flip, show=False) | |
| st.markdown(""" | |
| <style> | |
| .big-font { | |
| font-size:35px !important; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| st.markdown(f'<center><p class="big-font">Shap Bar Plot for <i>{top_pred}</i> Prediction</p></center>', unsafe_allow_html=True) | |
| st.pyplot(bar_plot, clear_figure=True) | |
| st.markdown('<center><p class="big-font">Shap Interactive Force Plot</p></center>', unsafe_allow_html=True) | |
| components.html(force_plot, height=output_height, width=output_width, scrolling=True) |