use ferret to explain

requirements.txt

@@ -1 +1,2 @@
-transformers==4.20.1
+transformers==4.20.1
+ferret-xai>=0.1.0

single.py

@@ -1,5 +1,6 @@
 import streamlit as st
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from ferret import Benchmark
 
 
 @st.cache()
@@ -7,7 +8,6 @@ def get_model(model_name):
     return AutoModelForSequenceClassification.from_pretrained(model_name)
 
 
-@st.cache()
 def get_tokenizer(tokenizer_name):
     return AutoTokenizer.from_pretrained(tokenizer_name, use_fast=True)
 
@@ -38,8 +38,10 @@ def body():
 
     compute = st.button("Compute")
 
-    if text and compute and model_name and tokenizer_name:
-        st.text("hellp")
+    if compute and model_name and tokenizer_name:
+        model = get_model(model_name)
+        tokenizer = get_tokenizer(tokenizer_name)
 
-    # model = get_model(model_name)
-    # tokenizer = get_tokenizer(tokenizer_name)
+        bench = Benchmark(model, tokenizer)
+        explanations = bench.explain(text)
+        st.dataframe(bench.show_table(explanations))