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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() |