| import streamlit as st |
| import streamlit.components.v1 as components |
| from transformers import (AutoModelForSequenceClassification, AutoTokenizer, |
| pipeline) |
| import shap |
| from PIL import Image |
|
|
| 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") |
|
|
|
|
| @st.cache_data() |
| 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 = st.columns(2) |
| text = col1.text_area("Enter text input", value = "Classify me.") |
|
|
| result = pred(text) |
| 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]) |
|
|
| 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) |
|
|
