Build app

app.py

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+from textblob import TextBlob
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+from transformers import pipeline
+import streamlit as st
+
+def translate_text(text):
+    blob = TextBlob(text)
+    return str(blob.translate(from_lang="pt", to="en"))
+
+def sentiment_classification(sentence):
+    sid_obj = SentimentIntensityAnalyzer()
+
+    sentiment_dict = sid_obj.polarity_scores(sentence)
+    negative = sentiment_dict['neg']
+    neutral = sentiment_dict['neu']
+    positive = sentiment_dict['pos']
+    compound = sentiment_dict['compound']
+
+    if sentiment_dict['compound'] >= 0.05 :
+        overall_sentiment = "Positive"
+
+    elif sentiment_dict['compound'] <= - 0.05 :
+        overall_sentiment = "Negative"
+
+    else :
+        overall_sentiment = "Neutral"
+  
+    return overall_sentiment, sentiment_dict['compound']
+ 
+def theme_classification(text):
+    
+    labels = ["Industrial Goods",
+              "Communications",
+              "Cyclic Consumption",
+              "Non-cyclical Consumption",
+              "Financial",
+              "Basic Materials",
+              #"Others",
+              "Oil, Gas and Biofuels",
+              "Health",
+              #"Initial Sector",
+              "Information Technology",
+              "Public utility"]
+    
+    template = "The economic sector of this set of words is {}."
+
+    classifier = pipeline("zero-shot-classification", model="joeddav/xlm-roberta-large-xnli")
+
+    results = classifier(text, labels, hypothesis_template=template)
+
+    index = results["scores"].index(max(results["scores"]))
+
+    return results["labels"][index]
+
+text = st.text_area("Coloque seu texto sobre mercado financeiro em português!")
+
+if text:
+   text_en = translate_text(text)
+   sentiment = sentiment_classification(text_en)
+   theme = theme_classification(text_en)
+   
+   st.json({
+   "Translation": text_en,
+   "Sentiment": sentiment,
+   "Theme": theme,
+   })