prototyping s-nlp roberta

app.py

@@ -1,5 +1,8 @@
 import streamlit as st
 
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+
+
 txt = st.text_area('Text to analyze', '''
     It was the best of times, it was the worst of times, it was
     the age of wisdom, it was the age of foolishness, it was
@@ -7,4 +10,13 @@ txt = st.text_area('Text to analyze', '''
     was the season of Light, it was the season of Darkness, it
     was the spring of hope, it was the winter of despair, (...)
     ''')
-st.write('Sentiment:', run_sentiment_analysis(txt))
+
+# load tokenizer and model weights
+tokenizer = RobertaTokenizer.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier')
+model = RobertaForSequenceClassification.from_pretrained('SkolkovoInstitute/roberta_toxicity_classifier')
+
+# prepare the input
+batch = tokenizer.encode('txt', return_tensors='pt')
+
+# inference
+model(batch)