import streamlit as st from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification # Define analyze function def analyze(model_name: str, text: str) -> dict: ''' Output result of sentiment analysis of a text through a defined model ''' model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) return classifier(text) # App title st.title("Sentiment Analysis App - Milestone2") st.write("This app is to analyze the sentiments behind a text.") st.write("Currently it uses pre-trained models without fine-tuning.") # Model hub model_descrip = { "distilbert-base-uncased-finetuned-sst-2-english": "This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. \ Labels: POSITIVE; NEGATIVE ", "cardiffnlp/twitter-roberta-base-sentiment": "This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. \ Labels: 0 -> Negative; 1 -> Neutral; 2 -> Positive", "finiteautomata/bertweet-base-sentiment-analysis": "Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets. \ Labels: POS; NEU; NEG" } user_input = st.text_input("Enter your text:", value="NYU is the better than Columbia.") user_model = st.selectbox("Please select a model:", model_descrip) # Display model information st.write("### Model Description:") st.write(model_descrip[user_model]) # Perform analysis and print result if st.button("Analyze"): if not user_input: st.write("Please enter a text.") else: with st.spinner("Hang on.... Analyzing..."): result = analyze(user_model, user_input) st.write("Result:") st.write(f"Label: **{result[0]['label']}**") st.write(f"Confidence Score: **{result[0]['score']}**") else: st.write("Go on! Try the app!")