File size: 1,634 Bytes
571af81 0695e07 571af81 0695e07 571af81 c755336 571af81 fa988c2 571af81 fa988c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
import streamlit as st
from transformers import pipeline
import pandas as pd
# options to choose 2 models
option = st.selectbox(
'Choose your model',
("cardiffnlp/twitter-roberta-base-sentiment-latest", "yiyanghkust/finbert-tone"))
# class for toxicity
labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]
# text area to get the input text from the user
text = st.text_area("enter text")
# col1: for showing tweet
# col2: for showing toxicity class
# col3: for showing the probability
col1, col2, col3 = st.columns(3)
# display the prediction if and only if text is entered and model is chose
if text and option:
dd = {
"labels": labels,
"values": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
}
#shows which model was used
st.write(f"Analyzed with {option} model")
#tokenizer = AutoTokenizer.from_pretrained(option)
#prediction = model[option].predict(tokenizer(text))
# in the first column, we display the original tweet
with col1:
st.header("Original Tweet")
st.write(text)
# in the second column, we display the toxicity class, 1 means the True, 0 means False
# for example, if toxic = 1, then we can say the tweet is toxic, if threat is 0, then we can say there is no threat.
# if the value given by the prediction is above threshold, we put 1, 0 otherwise.
with col2:
st.header("Toxicity class")
st.write(dd)
# in the third and last collumn, we display the probability of each category, sorted in descending order
with col3:
st.header("Probability")
st.write(dd) |