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import numpy as np
import streamlit as st
from transformers import pipeline
import torch
def bertweet(data):
specific_model = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis")
result = specific_model(data)
label = result[0]['label']
score = result[0]['score']
return label, score
def roberta(data):
specific_model = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment")
result = specific_model(data)
label = result[0]['label']
score = result[0]['score']
return label, score
def getSent(data, model):
if(model == 'Bertweet'):
label,score = bertweet(data)
col1, col2 = st.columns(2)
col1.metric("Feeling",label,None)
col2.metric("Score",score,None)
elif(model == 'Roberta'):
label,score = roberta(data)
col1, col2 = st.columns(2)
col1.metric("Feeling",label,None)
col2.metric("Score",score,None)
def rendPage():
st.title("Sentiment Analysis")
userText = st.text_input('User Input', "Hope you are having a great day!")
st.text("")
type = st.selectbox(
'Choose your model',
('Bertweet','Roberta',))
st.text("")
if st.button('Calculate'):
if(userText!="" and type != None):
st.text("")
getSent(userText,type)
rendPage()
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