Update sentiment-analyser.py

sentiment-analyser.py

@@ -3,7 +3,7 @@ from transformers import pipeline, AutoTokenizer
 
 st.title('Sentiment Analyser App')
 st.write('Welcome to my sentiment analysis app!')
-model_options=["sentiment-analysis", "twitter-xlm-roberta-base-sentiment", "sentiment-roberta-large-english-3-classes"]
+model_options=["sentiment-analysis", "twitter-xlm-roberta-base-sentiment", "sentiment-roberta-large-english"]
 
 form = st.form(key='sentiment-form')
 model_type = form.selectbox(label="Select a model", options=model_options)
@@ -17,16 +17,19 @@ def classification(user_input, type):
         path="cardiffnlp/twitter-xlm-roberta-base-sentiment"
         classifier = pipeline("sentiment-analysis", model=path, tokenizer=path)
     elif type=="sentiment-roberta-large-english-3-classes":
-        path="j-hartmann/sentiment-roberta-large-english-3-classes"
-        classifier = pipeline("text-classification", model=path, return_all_scores=True)
-    result = classifier(user_input)[0]
+        path="siebert/sentiment-roberta-large-english"
+        classifier = pipeline("sentiment-analysis", model=path)
+    result = classifier(user_input)
     return result
 
 if submit:
     resultf = classification(user_input, model_type)
-    label = resultf['label']
-    score = resultf['score']
-    if (label == 'POSITIVE') or (label =='Positive') or (label =='positive'):
-            st.success(f'{label} sentiment (score: {score})')
+    if model_type=="sentiment-roberta-large-english":
+        st.write(resultf[0][0] + ": " + resultf[0][1])
     else:
-        st.error(f'{label} sentiment (score: {score})')
+        label = resultf['label'][0]
+        score = resultf['score'][0]
+        if (label == 'POSITIVE') or (label =='Positive') or (label =='positive'):
+                st.success(f'{label} sentiment (score: {score})')
+        else:
+            st.error(f'{label} sentiment (score: {score})')