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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
#import the model
pipe=pipeline(model="Junr-syl/sentiments_analysis_DISTILBERT")
# tokenizer = AutoTokenizer.from_pretrained('Junr-syl/sentiments_analysis_DISTILBERT')
# model_path = f"Junr-syl/sentiments_analysis_DISTILBERT"
# 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='centered')
#welcome Animation
com.iframe("https://embed.lottiefiles.com/animation/149093")
st.markdown("<h1 style='text-align: center'> 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
output=pipe(text)
output_dict=output[0]
lable=output_dict['label']
score=output_dict['score']
# #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 lable=='NEGATIVE':
with col1:
com.iframe("https://embed.lottiefiles.com/animation/125694")
col2.write('NEGATIVE')
col3.write(f'{score*100:.2f}%')
elif lable=='POSITIVE':
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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