import numpy as np import streamlit as st from transformers import pipeline from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification from PIL import Image 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) image = Image.open("milestone3.jpg") st.image(image, caption="10 Example Texts") rendPage()