Update app.py

app.py

@@ -16,7 +16,7 @@ def fetch_tweets(client, query, tweet_fields):
     try:
         tweets = client.search_recent_tweets(
             query=query,
-            max_results=10,  # Reduzido para 10 para evitar rate limits
+            max_results=10,
             tweet_fields=tweet_fields
         )
         return tweets
@@ -36,107 +36,106 @@ def fetch_tweets(client, query, tweet_fields):
 def post_tweet(api, text):
     return api.update_status(status=text)
 
-# Verificar variáveis de ambiente
-required_vars = [
-    'TWITTER_API_KEY',
-    'TWITTER_API_SECRET_KEY',
-    'TWITTER_ACCESS_TOKEN',
-    'TWITTER_ACCESS_TOKEN_SECRET',
-    'TWITTER_BEARER_TOKEN'
-]
+def main():
+    try:
+        # Verificar variáveis de ambiente
+        required_vars = [
+            'TWITTER_API_KEY',
+            'TWITTER_API_SECRET_KEY',
+            'TWITTER_ACCESS_TOKEN',
+            'TWITTER_ACCESS_TOKEN_SECRET',
+            'TWITTER_BEARER_TOKEN'
+        ]
 
-# Verificação inicial das variáveis de ambiente
-missing_vars = []
-for var in required_vars:
-    if os.getenv(var) is None:
-        missing_vars.append(var)
-        print(f"Erro: A variável de ambiente '{var}' não está definida.")
-    else:
-        print(f"{var} carregada com sucesso.")
+        # Verificação inicial das variáveis de ambiente
+        missing_vars = []
+        for var in required_vars:
+            if os.getenv(var) is None:
+                missing_vars.append(var)
+                print(f"Erro: A variável de ambiente '{var}' não está definida.")
+            else:
+                print(f"{var} carregada com sucesso.")
 
-if missing_vars:
-    raise ValueError(f"As seguintes variáveis de ambiente são necessárias: {', '.join(missing_vars)}")
+        if missing_vars:
+            raise ValueError(f"As seguintes variáveis de ambiente são necessárias: {', '.join(missing_vars)}")
 
-# Autenticação com Twitter para leitura
-client = tweepy.Client(
-    bearer_token=os.getenv('TWITTER_BEARER_TOKEN'),
-    wait_on_rate_limit=True  # Importante: aguarda automaticamente quando atingir rate limit
-)
+        # Autenticação com Twitter para leitura
+        client = tweepy.Client(
+            bearer_token=os.getenv('TWITTER_BEARER_TOKEN'),
+            wait_on_rate_limit=True
+        )
 
-# Autenticação com Twitter para postagem
-auth = tweepy.OAuth1UserHandler(
-    os.getenv('TWITTER_API_KEY'),
-    os.getenv('TWITTER_API_SECRET_KEY'),
-    os.getenv('TWITTER_ACCESS_TOKEN'),
-    os.getenv('TWITTER_ACCESS_TOKEN_SECRET')
-)
+        # Autenticação com Twitter para postagem
+        auth = tweepy.OAuth1UserHandler(
+            os.getenv('TWITTER_API_KEY'),
+            os.getenv('TWITTER_API_SECRET_KEY'),
+            os.getenv('TWITTER_ACCESS_TOKEN'),
+            os.getenv('TWITTER_ACCESS_TOKEN_SECRET')
+        )
 
-api = tweepy.API(auth, wait_on_rate_limit=True)
+        api = tweepy.API(auth, wait_on_rate_limit=True)
 
-# Configuração da query e campos do tweet
-query = 'BBB25 -filter:retweets lang:pt -is:reply'
-tweet_fields = ['text', 'created_at', 'lang', 'public_metrics']
+        # Configuração da query e campos do tweet
+        query = 'BBB25 -filter:retweets lang:pt -is:reply'
+        tweet_fields = ['text', 'created_at', 'lang', 'public_metrics']
 
-try:
-    with st.spinner('Buscando tweets...'):
-        tweets = fetch_tweets(client, query, tweet_fields)
-    
-    if not tweets.data:
-        st.warning("Nenhum tweet encontrado")
-        st.stop()
+        with st.spinner('Buscando tweets...'):
+            tweets = fetch_tweets(client, query, tweet_fields)
         
-    # Análise de sentimentos
-    with st.spinner('Analisando sentimentos...'):
-        sentiment_pipeline = pipeline(
-            'sentiment-analysis',
-            model='cardiffnlp/twitter-xlm-roberta-base-sentiment'
-        )
-        
-        sentiments = []
-        for tweet in tweets.data:
-            if hasattr(tweet, 'lang') and tweet.lang == 'pt':
-                result = sentiment_pipeline(tweet.text)
-                sentiments.append(result[0]['label'])
-                
-        # Adicionar delay entre processamentos
-        time.sleep(1)
+        if not tweets.data:
+            st.warning("Nenhum tweet encontrado")
+            return
+
+        # Análise de sentimentos
+        with st.spinner('Analisando sentimentos...'):
+            sentiment_pipeline = pipeline(
+                'sentiment-analysis',
+                model='cardiffnlp/twitter-xlm-roberta-base-sentiment'
+            )
             
-    # Calcular taxas
-    if sentiments:
-        positive = sentiments.count('positive')
-        negative = sentiments.count('negative')
-        neutral = sentiments.count('neutral')
-        total = len(sentiments)
-        
-        positive_ratio = positive / total
-        negative_ratio = negative / total
-        neutral_ratio = neutral / total
-        
-        # Gerar mensagem com IA
-        with st.spinner('Gerando novo tweet...'):
-            tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-            model = GPT2LMHeadModel.from_pretrained('gpt2')
+            sentiments = []
+            for tweet in tweets.data:
+                if hasattr(tweet, 'lang') and tweet.lang == 'pt':
+                    result = sentiment_pipeline(tweet.text)
+                    sentiments.append(result[0]['label'])
+                    
+            time.sleep(1)
+                
+        # Calcular taxas
+        if sentiments:
+            positive = sentiments.count('positive')
+            negative = sentiments.count('negative')
+            neutral = sentiments.count('neutral')
+            total = len(sentiments)
             
-            if positive_ratio > 0.6:
-                prompt = "Write an exciting tweet about BBB25 with a positive tone in Portuguese."
-            elif negative_ratio > 0.6:
-                prompt = "Write an informative tweet about BBB25 with a neutral tone in Portuguese."
-            else:
-                prompt = "Write a buzzing tweet about BBB25 with an engaging tone in Portuguese."
+            positive_ratio = positive / total
+            negative_ratio = negative / total
+            neutral_ratio = neutral / total
             
-            input_ids = tokenizer.encode(prompt, return_tensors='pt')
-            outputs = model.generate(
-                input_ids,
-                max_length=25,
-                do_sample=True,
-                pad_token_id=tokenizer.eos_token_id
-            )
+            # Gerar mensagem com IA
+            with st.spinner('Gerando novo tweet...'):
+                tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+                model = GPT2LMHeadModel.from_pretrained('gpt2')
+                
+                if positive_ratio > 0.6:
+                    prompt = "Write an exciting tweet about BBB25 with a positive tone in Portuguese."
+                elif negative_ratio > 0.6:
+                    prompt = "Write an informative tweet about BBB25 with a neutral tone in Portuguese."
+                else:
+                    prompt = "Write a buzzing tweet about BBB25 with an engaging tone in Portuguese."
+                
+                input_ids = tokenizer.encode(prompt, return_tensors='pt')
+                outputs = model.generate(
+                    input_ids,
+                    max_length=25,
+                    do_sample=True,
+                    pad_token_id=tokenizer.eos_token_id
+                )
+                
+                generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+                generated_text = generated_text[:280]
             
-            generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            generated_text = generated_text[:280]
-        
-        try:
-            # Postar no Twitter com retry
+            # Postar tweet
             with st.spinner('Postando tweet...'):
                 post_tweet(api, generated_text)
                 st.success("Tweet postado com sucesso!")
@@ -168,18 +167,22 @@ try:
             
             with open('posting_log.txt', 'a') as f:
                 f.write(f"{str(log_entry)}\n")
-                
-except Exception as e:
-    st.error(f"Erro: {str(e)}")
-    print(f"Erro: {e}")
 
-# Footer
-st.markdown("---")
-st.markdown(
-    """
-    <div style='text-align: center'>
-        <small>Desenvolvido com ❤️ usando Streamlit e Transformers</small>
-    </div>
-    """,
-    unsafe_allow_html=True
-)
+    except Exception as e:
+        st.error(f"Erro: {str(e)}")
+        print(f"Erro: {e}")
+
+    finally:
+        # Footer
+        st.markdown("---")
+        st.markdown(
+            """
+            <div style='text-align: center'>
+                <small>Desenvolvido com ❤️ usando Streamlit e Transformers</small>
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
+
+if __name__ == "__main__":
+    main()