Update app.py

app.py

@@ -2,20 +2,25 @@ from transformers import pipeline, AutoModelForTokenClassification, AutoTokenize
 import sentencepiece
 import streamlit as st
 import pandas as pd
-import spacy
+import spacy 
+from spacy import displacy
+import plotly.express as px
+import numpy as np
 
 example_list = [
-    
+     "Mustafa Kemal Atatürk 1919 yılında Samsun'a çıktı.",
+     """Hello World"""
 ]
 
-st.set_page_config(layout="wide")
+st.set_page_config(layout="wide", page_title="Vocabulary Categorizer")
 
 st.title("Vocabulary Categorizer")
+st.write("This application identifies, highlights and categories nouns.")
 
-model_list = ['xlm-roberta-large-finetuned-conll03-english']
+model_list = ['xlm-roberta-large-finetuned-conll03-english', 'en_core_web_sm']
 
 st.sidebar.header("Vocabulary categorizer")
-model_checkpoint = st.sidebar.radio("xlm-roberta-large-finetuned-conll03-english")
+model_checkpoint = st.sidebar.radio("", model_list)
 
 st.sidebar.write("Which model highlights the most vocabulary words? Which model highlights the most accurately?")
 st.sidebar.write("")
@@ -27,6 +32,7 @@ if model_checkpoint == "xlm-roberta-large-finetuned-conll03-english":
     aggregation = st.sidebar.radio("", ('simple', 'none'))
     st.sidebar.write(xlm_agg_strategy_info)
     st.sidebar.write("")
+else aggregation = 'none'
 
 st.subheader("Select Text Input Method")
 input_method = st.radio("", ('Select from Examples', 'Write or Paste New Text'))