add basic shap text classification

app.py

@@ -1,4 +1,38 @@
 import streamlit as st
+import streamlit.components.v1 as components
+from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
+                          pipeline)
+import shap
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+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)

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+shap