Update app.py

app.py

@@ -1,5 +1,5 @@
 import tweepy
-from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
+from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer, AutoModelForSequenceClassification, AutoTokenizer
 import os
 import streamlit as st
 from datetime import datetime
@@ -141,17 +141,27 @@ def main():
         # Análise de sentimentos
         with st.spinner('Analisando sentimentos...'):
             debug_print("Iniciando análise de sentimentos...")
+            
+            # Inicializando o pipeline com modelo em português
             sentiment_pipeline = pipeline(
-                'sentiment-analysis',
-                model='cardiffnlp/twitter-xlm-roberta-base-sentiment'
+                "sentiment-analysis",
+                model="pierreguillou/bert-base-cased-gs-sentiment-pt",
+                tokenizer="pierreguillou/bert-base-cased-gs-sentiment-pt"
             )
             
             sentiments = []
             for tweet in tweets.data:
                 if hasattr(tweet, 'lang') and tweet.lang == 'pt':
                     result = sentiment_pipeline(tweet.text)
-                    sentiments.append(result[0]['label'])
-                    debug_print(f"Sentimento analisado: {result[0]['label']}")
+                    # Mapeando os resultados para positive/negative/neutral
+                    label = result[0]['label']
+                    if label == 'POSITIVE':
+                        sentiments.append('positive')
+                    elif label == 'NEGATIVE':
+                        sentiments.append('negative')
+                    else:
+                        sentiments.append('neutral')
+                    debug_print(f"Sentimento analisado: {label}")
                     
             time.sleep(1)