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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TextClassificationPipeline
import operator
import matplotlib.pyplot as plt
import pandas as pd
def get_sentiment(out):
d = dict()
for k in out:
print(k)
label = k['label']
score = k['score']
d[label] = score
winning_lab = max(d.items(), key=operator.itemgetter(1))[0]
winning_score = d[winning_lab]
df = pd.DataFrame.from_dict(d, orient = 'index')
return df #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)
df = get_sentiment(out[0])
fig, ax = plt.subplots()
c = ['#C34A36', '#FFC75F', '#008F7A']
ax.bar(df.index, df[0], color=c, width=0.4)
st.pyplot(fig)
#st.json(get_sentiment(out[0][0]))
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