app.py

import streamlit as st
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, TFAutoModelForSequenceClassification, DistillbertForSequenceClassification

# Options for models from transformers library
MODEL_OPTS = ['default', 'bertweet-base-sentiment-analysis', 'twitter-roberta-base', 'distilRoberta-financial-sentiment']
DEFAULT_OPT = MODEL_OPTS[0]

# returns loaded model and tokenizer, if any
def load_model(opt):
    if opt not in MODEL_OPTS: print("Incorrect model selection. Try again!")
    model, tokenizer = None, None

    # Load the chosen sentiment analysis model from transformers
    if opt == DEFAULT_OPT:
        return pipeline("sentiment-analysis"), tokenizer
    elif opt == 'bertweet-base-sentiment-analysis':
        tokenizer = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
        model = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
    elif opt == 'twitter-roberta-base-sentiment':
        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
        model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
    elif opt == 'distilRoberta-financial-sentiment'
        tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
        model = AutoModelForSequenceClassification.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
    elif opt == 'bert-base-multilingual-uncased-sentiment ':
        tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
        model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncas
    
    elif not model and not tokenizer: 
        print("Model not loaded correctly. Try again!")

    return model, tokenizer

def sentiment_analysis(model, tokenizer):
    if tokenizer:
        return pipeline('text-classification', model=model, tokenizer=tokenizer)
    else: return pipeline('text-classification', model=model)

# Title the Streamlit app 'Sentiment Analysis'
st.title('Sentiment Analysis')

# Take in user input
user_text = st.text_input('Input text to perform sentiment analysis on here.')

# The user can interact with a dropdown menu to choose a sentiment analysis model.
dropdown_value = st.selectbox('Select one of the following sentiment analysis models', MODEL_OPTS, index=MODEL_OPTS.index(DEFAULT_OPT))
model, tokenizer = load_model(dropdown_value)

# Perform sentiment analysis on the user's input
result = sentiment_analysis(text_input)

# Display the sentiment analysis results
st.write('Sentiment:', result[0]['label'], '; Score:', result[0]['score'])