Merge pull request #3 from cgr28/milestone-2

app.py

@@ -13,16 +13,17 @@ analyze_button = st.button(label="Analyze")
 
 st.markdown("**:red[Sentiment:]**")
 
-if analyze_button:
-    if selected_model=="Model 1":
-        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
-        model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
-    else:
-        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
-        model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
-    inputs = tokenizer(text, return_tensors="pt")
-    with torch.no_grad():
-        logits = model(**inputs).logits
-    prediction_id = logits.argmax().item()
-    results = model.config.id2label[prediction_id]
-    st.write(results)
+with st.spinner(text="Analyzing..."):
+    if analyze_button:
+        if selected_model=="Model 1":
+            tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
+            model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
+        else:
+            tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+            model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment-latest")
+        inputs = tokenizer(text, return_tensors="pt")
+        with torch.no_grad():
+            logits = model(**inputs).logits
+        prediction_id = logits.argmax().item()
+        results = model.config.id2label[prediction_id]
+        st.write(results)