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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
from transformers import pipeline
#Set the page configs
st.set_page_config(page_title='Sentiments Analysis',page_icon='😎',layout='centered')
#welcome Animation
com.iframe("https://embed.lottiefiles.com/animation/149093")
st.markdown("<h1 style='text-align: center'> Covid Vaccine Tweet Sentiments </h1>",unsafe_allow_html=True)
st.write("<h2 style='font-size: 24px;'> These models were trained to detect how a user feel about the covid vaccines based on their tweets(text) </h2>",unsafe_allow_html=True)
#Create a form to take user inputs
with st.form(key='tweet',clear_on_submit=True):
#input text
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')
#Set examples
alt_text=st.selectbox("Can't Type? Select an Example below",('I hate the vaccines','Vaccines made from dead human tissues','Take the vaccines or regret the consequences','Covid is a Hoax','Making the vaccines is a huge step forward for humanity. Just take them'))
#Select a model
models={'Roberta': 'Junr-syl/sentiments_analysis_Roberta',
'Bert': 'Junr-syl/sentiments_analysis_upgrade',
'Distilbert':'Junr-syl/sentiments_analysis_DISTILBERT'}
model=st.selectbox('Which model would you want to Use? Distilbert is Recommended',('Distilbert','Bert','Roberta'))
#Submit
submit=st.form_submit_button('Predict','Continue processing input')
selected_model=models[model]
#create columns to show outputs
col1,col2,col3=st.columns(3)
col1.write('<h2 style="font-size: 24px;"> Sentiment Emoji </h2>',unsafe_allow_html=True)
col2.write('<h2 style="font-size: 24px;"> How this user feels about the vaccine </h2>',unsafe_allow_html=True)
col3.write('<h2 style="font-size: 24px;"> Confidence of this prediction </h2>',unsafe_allow_html=True)
if submit:
#Check text
if text=="":
text=alt_text
st.success(f"input text is set to '{text}'")
else:
st.success('Text received',icon='✅')
#import the model
pipe=pipeline(model=selected_model)
#pass text to model
output=pipe(text)
output_dict=output[0]
lable=output_dict['label']
score=output_dict['score']
#output
if lable=='NEGATIVE' or lable=='LABEL_0':
with col1:
com.iframe("https://embed.lottiefiles.com/animation/125694")
col2.write('NEGATIVE')
col3.write(f'{score*100:.2f}%')
elif lable=='POSITIVE'or lable=='LABEL_2':
with col1:
com.iframe("https://embed.lottiefiles.com/animation/148485")
col2.write('POSITIVE')
col3.write(f'{score*100:.2f}%')
else:
with col1:
com.iframe("https://embed.lottiefiles.com/animation/136052")
col2.write('NEUTRAL')
col3.write(f'{score*100:.2f}%')
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