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}%')