Anthos23/hummus

1import streamlit as st2from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TextClassificationPipeline3import operator4import matplotlib.pyplot as plt5import pandas as pd6 7def get_sentiment(out):8  d = dict()9  for k in out:10    print(k)11    label = k['label']12    score = k['score']13    d[label] = score14 15  winning_lab = max(d.items(), key=operator.itemgetter(1))[0]16  winning_score = d[winning_lab]17 18  df = pd.DataFrame.from_dict(d, orient = 'index')19  return  df #winning_lab, winning_score20  21model_name =  "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"22model = AutoModelForSequenceClassification.from_pretrained(model_name)23tokenizer = AutoTokenizer.from_pretrained(model_name)24 25pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)26text = st.text_area(f'Ciao! This app uses {model_name}.\nEnter your text  to test it ❤️')27 28 29if text:30  out = pipe(text)31  df = get_sentiment(out[0])32  fig, ax = plt.subplots()33  c = ['#C34A36', '#FFC75F', '#008F7A']34  ax.bar(df.index, df[0], color=c, width=0.4)35  36  st.pyplot(fig)37  38  #st.json(get_sentiment(out[0][0]))39