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| import streamlit as st | |
| from transformers import pipeline | |
| import torch | |
| import torch.nn.functional as F | |
| def main(): | |
| models = ["distilbert-base-uncased-finetuned-sst-2-english","cardiffnlp/twitter-roberta-base-sentiment","finiteautomata/bertweet-base-sentiment-analysis","papluca/xlm-roberta-base-language-detection","cardiffnlp/twitter-roberta-base-sentiment-latest","yiyanghkust/finbert-tone","ProsusAI/finbert","j-hartmann/emotion-english-distilroberta-base"] | |
| st.title("Streamlit Sentiment Analysis App ") | |
| st.header("Sentiments analysis using Trnasformers by 🤗") | |
| st.header("Jozef Janosko - CS 482, Milestone 2") | |
| st.text("Input a test string for sentiment analysis.") | |
| input=st.text_input("input string","Here is a default string. I love machine learning!") | |
| model = st.selectbox("Select Model...", models) | |
| st.text("Result using "+model+": ") | |
| st.text(str(sentiment_Analysis(input,model))) | |
| def sentiment_Analysis(input, model): | |
| classifier = pipeline("sentiment-analysis",model) | |
| ret=classifier(input) | |
| return ret | |
| if __name__ == '__main__' : | |
| main() |