app.py

import streamlit as st
import tensorflow as tf
from transformers import pipeline
from textblob import TextBlob
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

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)
# 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)


if option == 'Pipeline':
    # 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'])

if option == 'TextBlob':
    # 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)