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import streamlit as st
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import BertTokenizer, BertForSequenceClassification
from huggingface_hub.inference_api import InferenceApi
import os
models = ["cardiffnlp/twitter-xlm-roberta-base-sentiment", "nlptown/bert-base-multilingual-uncased-sentiment", "Tatyana/rubert-base-cased-sentiment-new", "junming-qiu/BertToxicClassifier"]
st.title('Sentiment Analysis Demo')
with st.form("form"):
selection = st.selectbox('Select Transformer:', models)
text = st.text_input('Enter text: ', "I do not like to walk")
submitted = st.form_submit_button('Submit')
if submitted:
model_name = models[models.index(selection)]
if model_name == "junming-qiu/BertToxicClassifier":
API_TOKEN=os.environ['API-KEY']
inference = InferenceApi(repo_id=model_name, token=API_TOKEN)
predictions = inference(inputs=text)[0]
predictions = sorted(predictions, key=lambda x: x['score'], reverse=True)
st.write(predictions[0]['label']+":", predictions[0]['score'])
st.write(predictions[1]['label']+":", predictions[1]['score'])
else:
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
result = classifier(text)
st.write("Label:", result[0]["label"])
st.write('Score: ', result[0]['score'])
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