app.py

import streamlit as st
import tensorflow as tf
import pandas as pd
from transformers import pipeline
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?',('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('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'])