| import numpy as np | |
| import streamlit as st | |
| from transformers import pipeline | |
| from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification | |
| import torch | |
| def bertweet(data): | |
| specific_model = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis") | |
| result = specific_model(data) | |
| label = result[0]['label'] | |
| score = result[0]['score'] | |
| return label, score | |
| def roberta(data): | |
| specific_model = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment") | |
| result = specific_model(data) | |
| label = result[0]['label'] | |
| score = result[0]['score'] | |
| if(label == 'LABEL_0'): | |
| label = 'Negative' | |
| elif(label == 'LABEL_1'): | |
| label = 'Neutral' | |
| else: | |
| label = 'Positive' | |
| return label, score | |
| def siebert(data): | |
| specific_model = pipeline(model='siebert/sentiment-roberta-large-english') | |
| result = specific_model(data) | |
| label = result[0]['label'] | |
| score = result[0]['score'] | |
| return label, score | |
| def finetuned(data): | |
| model_name = "dahongj/finetuned_toxictweets" | |
| tokenizer = DistilBertTokenizerFast.from_pretrained(model_name) | |
| model = DistilBertForSequenceClassification.from_pretrained(model_name) | |
| tokenized_text = tokenizer(data, return_tensors="pt") | |
| res = model(**tokenized_text) | |
| mes = torch.sigmoid(res.logits) | |
| Dict = {0: "toxic", 1: "severe_toxic", 2: "obscene", 3: "threat", 4: "insult", 5: "identity_hate"} | |
| maxres, maxscore, sec, secscore = Dict[0], mes[0][0].item(), 0, 0 | |
| for i in range(1,6): | |
| if mes[0][i].item() > secscore: | |
| sec = i | |
| secscore = mes[0][i].item() | |
| return maxres, maxscore, Dict[sec], secscore | |
| def getSent(data, model): | |
| if(model == 'Bertweet'): | |
| label,score = bertweet(data) | |
| col1, col2 = st.columns(2) | |
| col1.metric("Feeling",label,None) | |
| col2.metric("Score",score,None) | |
| elif(model == 'Roberta'): | |
| label,score = roberta(data) | |
| col1, col2 = st.columns(2) | |
| col1.metric("Feeling",label,None) | |
| col2.metric("Score",score,None) | |
| elif(model == 'Siebert'): | |
| label,score = siebert(data) | |
| col1, col2 = st.columns(2) | |
| col1.metric("Feeling",label,None) | |
| col2.metric("Score",score,None) | |
| elif(model == 'Finetuned'): | |
| label, score, sec, secsc = finetuned(data) | |
| col1, col2 = st.columns(2) | |
| col3, col4 = st.columns(2) | |
| col1.metric("Highest",label,None) | |
| col2.metric("Score",score,None) | |
| col3.metric("Second Highest", sec, None) | |
| col4.metric("Score", secsc, None) | |
| def rendPage(): | |
| st.title("Sentiment Analysis") | |
| userText = st.text_area('User Input', "Hope you are having a great day!") | |
| st.text("") | |
| type = st.selectbox( | |
| 'Choose your model', | |
| ('Bertweet','Roberta','Siebert','Finetuned')) | |
| st.text("") | |
| if st.button('Calculate'): | |
| if(userText!="" and type != None): | |
| st.text("") | |
| getSent(userText,type) | |
| rendPage() | |