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| import streamlit as st | |
| from transformers import (AutoTokenizer, TFAutoModelForSequenceClassification, | |
| pipeline) | |
| st.title("CS-GY-6613 Project Milestone 2") | |
| model_choices = ( | |
| "distilbert-base-uncased-finetuned-sst-2-english", | |
| "j-hartmann/emotion-english-distilroberta-base", | |
| "joeddav/distilbert-base-uncased-go-emotions-student", | |
| ) | |
| with st.form("Input Form"): | |
| text = st.text_area("Write your text here:", "CS-GY-6613 is a great course!") | |
| model_name = st.selectbox("Select a model:", model_choices) | |
| submitted = st.form_submit_button("Submit") | |
| if submitted: | |
| model = TFAutoModelForSequenceClassification.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) | |
| res = classifier(text) | |
| label = res[0]["label"].upper() | |
| score = res[0]["score"] | |
| st.markdown( | |
| f"This text was classified as **{label}** with a confidence score of **{score}**." | |
| ) | |