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import streamlit as st
import streamlit.components.v1 as com
#import libraries
from transformers import AutoModelForSequenceClassification,AutoTokenizer, AutoConfig
import numpy as np
#convert logits to probabilities
from scipy.special import softmax
#import the model
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
model_path = f"UholoDala/tweet_sentiments_analysis"
config = AutoConfig.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
#Set the page configs
st.set_page_config(page_title='Sentiments Analysis',page_icon='😎',layout='wide')
#welcome Animation
com.iframe("https://embed.lottiefiles.com/animation/149093")
st.markdown('<h1> Tweet Sentiments </h1>',unsafe_allow_html=True)
#Create a form to take user inputs
with st.form(key='tweet',clear_on_submit=True):
text=st.text_area('Copy and paste a tweet or type one',placeholder='I find it quite amusing how people ignore the effects of not taking the vaccine')
submit=st.form_submit_button('submit')
#create columns to show outputs
col1,col2,col3=st.columns(3)
col1.title('Sentiment Emoji')
col2.title('How this user feels about the vaccine')
col3.title('Confidence of this prediction')
if submit:
print('submitted')
#pass text to preprocessor
def preprocess(text):
#initiate an empty list
new_text = []
#split text by space
for t in text.split(" "):
#set username to @user
t = '@user' if t.startswith('@') and len(t) > 1 else t
#set tweet source to http
t = 'http' if t.startswith('http') else t
#store text in the list
new_text.append(t)
#change text from list back to string
return " ".join(new_text)
#pass text to model
#change label id
config.id2label = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
text = preprocess(text)
# PyTorch-based models
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
#Process scores
ranking = np.argsort(scores)
ranking = ranking[::-1]
l = config.id2label[ranking[0]]
s = scores[ranking[0]]
#output
if l=='NEGATIVE':
with col1:
com.iframe("https://embed.lottiefiles.com/animation/125694")
col2.write('Negative')
col3.write(f'{s}%')
elif l=='POSITIVE':
with col1:
com.iframe("https://embed.lottiefiles.com/animation/148485")
col2.write('Positive')
col3.write(f'{s:.2%}')
else:
with col1:
com.iframe("https://embed.lottiefiles.com/animation/136052")
col2.write('Neutral')
col3.write(f'{s}%')
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