Create app.py

app.py

@@ -0,0 +1,30 @@
+import streamlit as st
+import torch
+from typing import List
+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from scipy.special import softmax
+
+task = 'sentiment'
+MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+
+def polarity_scores_roberta(example):
+    encoded_text = tokenizer(example, truncation=True, return_tensors='pt')
+    output = model(**encoded_text)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+    scores_dict = {
+        'Roberta_NEG' : scores[0],
+        'Roberta_NEU' : scores[1],
+        'Roberta_POS' : scores[2]
+    }
+    return scores_dict
+
+text = st.text_area('enter some texts!')
+
+if text:
+    out = polarity_scores_roberta(text)
+    st.json(out)