added senitment task

app.py

@@ -8,16 +8,21 @@ with st.spinner(text="Loading model..."):
     classifier = t.pipeline("zero-shot-classification",
                             model="facebook/bart-large-mnli",
                             multi_class=True)
+    sentiment_task = t.pipeline("sentiment-analysis",
+                                model="cardiffnlp/twitter-xlm-roberta-base-sentiment",
+                                tokenizer="cardiffnlp/twitter-xlm-roberta-base-sentiment")
+
 
 x = st.text_input("Enter your title here:")
 candidate_labels = ['anger', 'sadness', 'fear', 'joy', 'interest',
-                    'surprise', 'disgust', 'shame', 'guilt', 'compassion', 'other']
+                    'surprise', 'disgust', 'shame', 'compassion', 'other']
 
 if x != "":
 
     with st.spinner(text="Evaluating your input..."):
         output = classifier(x, candidate_labels)
-    # st.write(str(output))
+        sentiment = sentiment_task(x)
+    st.write(str(sentiment))
 
     ordered_results = []
     for lbl in candidate_labels: