textblob

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import tensorflow as tf
-import pandas as pd
 from transformers import pipeline
 from textblob import TextBlob
 
@@ -18,16 +17,14 @@ preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in
 st.write('According to Pipeline, input text is ', preds[0]['label'], ' with a confidence of ', preds[0]['score'])
 
 # textblob
-def textblob_polarity(text):
-    return TextBlob(text).sentiment.polarity
-def getAnalysis(score):
-    if score < 0:
-        return 'Negative'
-    elif score == 0:
-        return 'Neutral'
-    else:
-        return 'Positive'
-df['polarity'] = df[text].apply(textblob_polarity)
-df['classification'] = df['polarity'].apply(getAnalysis)
-st.write('According to textblob, input text is ', df['classification'], ' with a subjectivity score of ', df['polarity'])
+polarity = TextBlob(textIn).sentiment.polarity
+sentiment = TextBlob(textIn).sentiment
+if score < 0:
+    sentiment = 'Negative'
+elif score == 0:
+    sentiment = 'Neutral'
+else:
+    sentiment = 'Positive'
+
+st.write('According to textblob, input text has a subjectivity score of ', sentiment, ' and a polarity score of ', polarity)