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from pathlib import Path
import torch
from torch.utils.data import Dataset
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
from transformers import Trainer, TrainingArguments
import streamlit as st
import requests
model_one = "distilbert-base-uncased-finetuned-sst-2-english"
model_two = "Newtral/xlm-r-finetuned-toxic-political-tweets-es"
def toxicRating(text, model):
model = AutoModelForSequenceClassification.from_pretrained(model)
tokenizer = AutoTokenizer.from_pretrained(model)
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
results = classifier(text)
return results
def main():
st.title("TOXIC TWEETS, \n TOXIC OR NOT?")
prompt = st.header("Select Model")
selection = st.radio("Models",('Model 1', 'Model 2'))
input = st.text_area("Enter Tweet: ")
if st.button('Rate') and input:
if selection == 'Model 1':
rating = toxicRating(input, model_one)
st.write(f"Label: {rating[1]} \n Score : {rating[3]}")
elif selection == 'Model 2':
rating = toxicRating(input, model_two)
st.write(f"Label: {rating[1]} \n Score : {rating[4]}")
else:
st.warning("Enter Tweet")
if __name__ == "__main__":
main(); |