EnzoBustos commited on
Commit
3a29a54
1 Parent(s): 17aa280

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +18 -4
app.py CHANGED
@@ -1,6 +1,7 @@
1
  from textblob import TextBlob
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  from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
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  from transformers import pipeline
 
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  import re
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  import streamlit as st
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@@ -79,10 +80,12 @@ def theme_classification(text, text_classifier):
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  sid_obj = SentimentIntensityAnalyzer()
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  classifier = pipeline("zero-shot-classification", model="joeddav/xlm-roberta-large-xnli")
 
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  header = st.container()
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  model = st.container()
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  dataset = st.container()
 
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  with st.sidebar:
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  st.markdown("# Lorem Ipsum\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent sapien tortor, suscipit quis ornare ut, laoreet vitae nisi. Mauris quis consectetur risus, non blandit mauris. Sed ut odio tempor, ullamcorper leo eu, mollis eros. Curabitur pretium sollicitudin sapien, vel mattis augue convallis quis. Suspendisse eleifend turpis non nunc gravida, aliquet hendrerit orci viverra. Sed aliquet, nunc eu posuere tempor, libero ex dignissim velit, ut ultricies erat felis at urna. Proin metus augue, commodo in faucibus sed, aliquet ac eros. Nullam turpis leo, dictum eu tellus a, aliquam egestas velit. Suspendisse cursus augue a sem dapibus, eu faucibus nisl ultrices. Cras tortor ipsum, luctus vitae tincidunt id, dapibus id justo. Sed mi nunc, tempor eu iaculis in, tristique cursus massa. Integer metus felis, pulvinar ut aliquam ut, consectetur in nulla.")
@@ -97,11 +100,22 @@ with model:
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  if text:
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  text_en = translate_text(text)
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- st.write("Translation: {}".format(text_en))
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  sentiment = sentiment_classification(text_en, sid_obj)
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- st.write("Sentiment: {} - {}".format(sentiment[0], sentiment[1]))
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  theme = theme_classification(text_en, classifier)
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- st.write("Theme: {}".format(theme))
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  with dataset:
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- st.header("Dados utilizados no projeto!")
 
 
 
 
 
 
 
 
 
 
 
 
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  from textblob import TextBlob
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  from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
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  from transformers import pipeline
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+ import pandas as pd
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  import re
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  import streamlit as st
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  sid_obj = SentimentIntensityAnalyzer()
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  classifier = pipeline("zero-shot-classification", model="joeddav/xlm-roberta-large-xnli")
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+ df = pd.read_excel("Hugging Face DF.xslx")
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  header = st.container()
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  model = st.container()
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  dataset = st.container()
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+ analysis = st.container()
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  with st.sidebar:
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  st.markdown("# Lorem Ipsum\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Praesent sapien tortor, suscipit quis ornare ut, laoreet vitae nisi. Mauris quis consectetur risus, non blandit mauris. Sed ut odio tempor, ullamcorper leo eu, mollis eros. Curabitur pretium sollicitudin sapien, vel mattis augue convallis quis. Suspendisse eleifend turpis non nunc gravida, aliquet hendrerit orci viverra. Sed aliquet, nunc eu posuere tempor, libero ex dignissim velit, ut ultricies erat felis at urna. Proin metus augue, commodo in faucibus sed, aliquet ac eros. Nullam turpis leo, dictum eu tellus a, aliquam egestas velit. Suspendisse cursus augue a sem dapibus, eu faucibus nisl ultrices. Cras tortor ipsum, luctus vitae tincidunt id, dapibus id justo. Sed mi nunc, tempor eu iaculis in, tristique cursus massa. Integer metus felis, pulvinar ut aliquam ut, consectetur in nulla.")
 
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  if text:
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  text_en = translate_text(text)
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+ st.write("*Translation:* {}".format(text_en))
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  sentiment = sentiment_classification(text_en, sid_obj)
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+ st.write("*Sentiment:* {} - {}".format(sentiment[0], sentiment[1]))
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  theme = theme_classification(text_en, classifier)
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+ st.write("*Theme:* {}".format(theme))
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  with dataset:
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+ st.header("Dados utilizados no projeto!")
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+ st.write("Os dados blablablabla")
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+ st.dataframe(df)
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+ st.subheader("Descrição das colunas:")
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+ st.write("\t*- Texts:* Coluna que mostra os textos financeiros")
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+ st.write("\t*- Texts:* Coluna que mostra os textos financeiros")
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+ st.write("\t*- Texts:* Coluna que mostra os textos financeiros")
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+ st.write("\t*- Texts:* Coluna que mostra os textos financeiros")
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+ st.write("\t*- Texts:* Coluna que mostra os textos financeiros")
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+
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+ with analysis:
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+ st.header("Visualização dos dados utilizados")