testblob tester

app.py

@@ -1,17 +1,40 @@
 import streamlit as st
 import tensorflow as tf
 from transformers import pipeline
-# import numpy as np
+from textblob import TextBlob
 
 classifier = pipeline(task="sentiment-analysis")
 
 textIn = st.text_input("Input Text Here:", "I really like the color of your car!")
 
-option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('TensorFlow', 'PyTorch', 'JAX'))
+option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('Pipeline', 'textblob', ''))
 
 st.write('You selected:', option)
 
 # pipeline
 preds = classifier(textIn)
 preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
-st.write('Input text is ', preds[0]['label'], ' with a confidence of ', preds[0]['score'])
+st.write('According to Pipeline, input text is ', preds[0]['label'], ' with a confidence of ', preds[0]['score'])
+
+# textblob
+polarity = TextBlob(textIn).sentiment.polarity
+sentiment = ''
+if score < 0:
+    sentiment = 'Negative'
+elif score == 0:
+    sentiment = 'Neutral'
+else:
+    sentiment = 'Positive'
+st.write('According to textblob, input text is ', sentiment, ' with a polarity (subjectivity score) of ', 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)
+