testagain

app.py

@@ -14,14 +14,16 @@ while True:
     st.write('You selected:', option)
 
     if option == 'MILESTONE 3: FINE-TUNED':
-
-        model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-        model = AutoModelForSequenceClassification.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        classifier = pipeline(task="sentiment-analysis", model=model, tokenizer=tokenizer)
-        preds = classifier(textIn)
-        preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
-        st.write('According to Pipeline, input text is ', preds[0]['label'], ' with a confidence of ', preds[0]['score'])
+        polarity = TextBlob(textIn).sentiment.polarity
+        subjectivity = TextBlob(textIn).sentiment.subjectivity
+        sentiment = ''
+        if polarity < 0:
+            sentiment = 'Negative'
+        elif polarity == 0:
+            sentiment = 'Neutral'
+        else:
+            sentiment = 'Positive'
+        st.write('According to TextBlob, input text is ', sentiment, ' and a subjectivity score (from 0 being objective to 1 being subjective) of ', subjectivity)
 
         # model_name_0 = "Rathgeberj/milestone3_0"
         # model_0 = AutoModelForSequenceClassification.from_pretrained(model_name_0)
@@ -73,17 +75,17 @@ while True:
         preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
         st.write('According to Pipeline, input text is ', preds[0]['label'], ' with a confidence of ', preds[0]['score'])
 
-    if option == 'TextBlob':
-        polarity = TextBlob(textIn).sentiment.polarity
-        subjectivity = TextBlob(textIn).sentiment.subjectivity
-        sentiment = ''
-        if polarity < 0:
-            sentiment = 'Negative'
-        elif polarity == 0:
-            sentiment = 'Neutral'
-        else:
-            sentiment = 'Positive'
-        st.write('According to TextBlob, input text is ', sentiment, ' and a subjectivity score (from 0 being objective to 1 being subjective) of ', subjectivity)
+    # if option == 'TextBlob':
+    #     polarity = TextBlob(textIn).sentiment.polarity
+    #     subjectivity = TextBlob(textIn).sentiment.subjectivity
+    #     sentiment = ''
+    #     if polarity < 0:
+    #         sentiment = 'Negative'
+    #     elif polarity == 0:
+    #         sentiment = 'Neutral'
+    #     else:
+    #         sentiment = 'Positive'
+    #     st.write('According to TextBlob, input text is ', sentiment, ' and a subjectivity score (from 0 being objective to 1 being subjective) of ', subjectivity)
 
 
 #------------------------------------------------------------------------