Update app.py

app.py

@@ -3,6 +3,38 @@ from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
 import os
 import streamlit as st
 from datetime import datetime
+import time
+from tenacity import retry, stop_after_attempt, wait_exponential
+
+# Função com retry para buscar tweets
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=lambda e: isinstance(e, tweepy.errors.TooManyRequests)
+)
+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
+            tweet_fields=tweet_fields
+        )
+        return tweets
+    except tweepy.errors.TooManyRequests as e:
+        reset_time = int(e.response.headers.get('x-rate-limit-reset', 0))
+        wait_time = max(reset_time - time.time(), 0)
+        print(f"Rate limit atingido. Aguardando {wait_time:.0f} segundos...")
+        time.sleep(wait_time + 1)
+        raise e
+
+# Função com retry para postar tweets
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=lambda e: isinstance(e, tweepy.errors.TooManyRequests)
+)
+def post_tweet(api, text):
+    return api.update_status(status=text)
 
 # Verificar variáveis de ambiente
 required_vars = [
@@ -27,7 +59,8 @@ if missing_vars:
 
 # Autenticação com Twitter para leitura
 client = tweepy.Client(
-    bearer_token=os.getenv('TWITTER_BEARER_TOKEN')
+    bearer_token=os.getenv('TWITTER_BEARER_TOKEN'),
+    wait_on_rate_limit=True  # Importante: aguarda automaticamente quando atingir rate limit
 )
 
 # Autenticação com Twitter para postagem
@@ -38,37 +71,35 @@ auth = tweepy.OAuth1UserHandler(
     os.getenv('TWITTER_ACCESS_TOKEN_SECRET')
 )
 
-api = tweepy.API(auth)
+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']
 
 try:
-    # Busca tweets com os campos especificados
-    tweets = client.search_recent_tweets(
-        query=query,
-        max_results=100,
-        tweet_fields=tweet_fields
-    )
+    with st.spinner('Buscando tweets...'):
+        tweets = fetch_tweets(client, query, tweet_fields)
     
     if not tweets.data:
-        print("Nenhum tweet encontrado")
-        st.error("Nenhum tweet encontrado para análise")
+        st.warning("Nenhum tweet encontrado")
         st.stop()
         
     # Análise de sentimentos
-    sentiment_pipeline = pipeline(
-        'sentiment-analysis',
-        model='cardiffnlp/twitter-xlm-roberta-base-sentiment'
-    )
-    
-    sentiments = []
-    for tweet in tweets.data:
-        # Verificação adicional para garantir que temos tweets em português
-        if hasattr(tweet, 'lang') and tweet.lang == 'pt':
-            result = sentiment_pipeline(tweet.text)
-            sentiments.append(result[0]['label'])
+    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)
             
     # Calcular taxas
     if sentiments:
@@ -82,32 +113,33 @@ try:
         neutral_ratio = neutral / total
         
         # Gerar mensagem com IA
-        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."
-        
-        # Gerar texto
-        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]  # Limitar a 280 caracteres
+        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]
         
         try:
-            # Postar no Twitter
-            api.update_status(status=generated_text)
-            print(f"Tweet postado com sucesso: {generated_text}")
+            # Postar no Twitter com retry
+            with st.spinner('Postando tweet...'):
+                post_tweet(api, generated_text)
+                st.success("Tweet postado com sucesso!")
             
             # Interface Streamlit
             st.title("Análise de Sentimentos - BBB25")
@@ -137,19 +169,9 @@ try:
             with open('posting_log.txt', 'a') as f:
                 f.write(f"{str(log_entry)}\n")
                 
-        except Exception as e:
-            st.error(f"Erro ao postar tweet: {str(e)}")
-            print(f"Erro ao postar: {e}")
-            
-except tweepy.errors.BadRequest as e:
-    st.error(f"Erro na requisição ao Twitter: {str(e)}")
-    print(f"Erro na requisição: {str(e)}")
-except tweepy.errors.TweepyException as e:
-    st.error(f"Erro do Tweepy: {str(e)}")
-    print(f"Erro do Tweepy: {str(e)}")
 except Exception as e:
-    st.error(f"Erro inesperado: {str(e)}")
-    print(f"Erro inesperado: {str(e)}")
+    st.error(f"Erro: {str(e)}")
+    print(f"Erro: {e}")
 
 # Footer
 st.markdown("---")
@@ -160,4 +182,4 @@ st.markdown(
     </div>
     """,
     unsafe_allow_html=True
-)
+)