app.py

import streamlit as st
import streamlit.components.v1 as components
from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
                          pipeline)
import shap

output_width = 800
output_height = 1000
rescale_logits = False

st.set_page_config(page_title='Text Classification with Shap')
st.title('Interpreting HF Pipeline Text Classification with Shap')

text = st.text_area("Enter text input", value = "Classify me.")

form = st.sidebar.form("Main Settings")
form.header('Main Settings')

model_name = form.text_area("Enter the name of the text classification model", 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)

shap_values = explainer([text])

shap_plot = shap.plots.text(shap_values, display=False)
st.title('Interactive Shap Force Plot')
components.html(shap_plot, height=output_height, width=output_width, scrolling=True)