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import streamlit as st
import time
from transformers import pipeline
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = "True"
st.title("Sentiment Analysis App")
form = st.form(key='Sentiment Analysis')
box = form.selectbox('Select Pre-trained Model:', ['bertweet-base-sentiment-analysis',
'distilbert-base-uncased-finetuned-sst-2-english',
'twitter-roberta-base-sentiment'
], key=1)
tweet = form.text_input(label='Enter text to analyze:', value="\"We've seen in the last few months, unprecedented amounts of Voter Fraud.\" @SenTedCruz True!")
submit = form.form_submit_button(label='Submit')
if submit and tweet:
with st.spinner('Analyzing...'):
time.sleep(1)
# st.header(tweet)
if tweet is not None:
col1, col2, col3 = st.columns(3)
if box == 'bertweet-base-sentiment-analysis':
pipeline = pipeline(task="sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis")
elif box == 'twitter-xlm-roberta-base-sentiment':
pipeline = pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
else:
pipeline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
predictions = pipeline(tweet)
print(predictions)
col1.header("Tweet")
col1.subheader(tweet)
col2.header("Judgement")
col3.header("Probability")
for p in predictions:
if box == 'bertweet-base-sentiment-analysis':
if p['label'] == "POS":
col2.success(f"{ p['label'] }")
col3.success(f"{ round(p['score'] * 100, 1)}%")
elif p['label'] == "NEU":
col2.warning(f"{ p['label'] }")
col3.warning(f"{round(p['score'] * 100, 1)}%")
else:
col2.error(f"{p['label']}")
col3.error(f"{round(p['score'] * 100, 1)}%")
elif box == 'distilbert-base-uncased-finetuned-sst-2-english':
if p['label'] == "POSITIVE":
col2.success(f"{p['label']}")
col3.success(f"{round(p['score'] * 100, 1)}%")
else:
col2.error(f"{p['label']}")
col3.error(f"{round(p['score'] * 100, 1)}%")
else:
if p['label'] == "POSITIVE":
col2.success(f"{p['label']}")
col3.success(f"{round(p['score'] * 100, 1)}%")
else:
col2.error(f"{p['label']}")
col3.error(f"{round(p['score'] * 100, 1)}%") |