app.py

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')
  df = df.reset_index().rename(columns={'index': 'sentiment', 0:'score'})
  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()
  ax.bar(df.index, df[0])
  
  st.pyplot(fig)
  
  #st.json(get_sentiment(out[0][0]))