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| 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']) | |