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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TextClassificationPipeline
import operator
def get_sentiment(out):
print(out)
d = dict()
for k in out.keys():
label = out[k]['label']
score = out[k]['score']
d[label] = score
winning_lab = max(d.iteritems(), key=operator.itemgetter(1))[0]
winning_score = d[winning_lab]
return winning_lab, winning_score
model_name = "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
text = st.text_area(f'Ciao! This app uses {model_name}.\nEnter your text to test it ❤️')
if text:
out = pipe(text)
st.json(get_sentiment(out[0]))
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