| import streamlit as st | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification | |
| st.title("Sentiment Analysis App - beta") | |
| st.header("This app is to analyze the sentiments behind a text. Currently it uses \ | |
| pre-trained models without fine-tuning.") | |
| user_input = st.text_input("Enter your text:", value="Missing Sophie.Z...") | |
| user_model = st.selectbox("Please select a model:", | |
| ("distilbert-base-uncased-finetuned-sst-2-english", | |
| "cardiffnlp/twitter-roberta-base-sentiment", | |
| "finiteautomata/bertweet-base-sentiment-analysis")) | |
| def analyze(model_name, text): | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) | |
| return classifier(text) | |
| if st.button("Analyze"): | |
| if not user_input: | |
| st.write("Please enter a text.") | |
| else: | |
| with st.spinner("Hang on.... Analyzing..."): | |
| st.write(analyze(user_model, user_input)) | |
| else: | |
| st.write("Go on! Try the app!") |