Update app.py

app.py

@@ -1,18 +1,36 @@
 import tweepy
 from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
 import os
-
-   
+import streamlit as st
+from datetime import datetime
+
+# 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.")
+
+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')
 )
 
-
 # Autenticação com Twitter para postagem
-
 auth = tweepy.OAuth1UserHandler(
     os.getenv('TWITTER_API_KEY'),
     os.getenv('TWITTER_API_SECRET_KEY'),
@@ -22,58 +40,116 @@ auth = tweepy.OAuth1UserHandler(
 
 api = tweepy.API(auth)
 
-# Coletar tweets
-query = 'BBB25 -filter:retweets'
-tweets = client.search_recent_tweets(query=query, lang='pt', max_results=100)
-
-# Análise de sentimentos
-sentiment_pipeline = pipeline('sentiment-analysis', model='cardiffnlp/twitter-xlm-roberta-base-sentiment')
-
-sentiments = []
-for tweet in tweets.data:
-    result = sentiment_pipeline(tweet.text)
-    sentiments.append(result[0]['label'])
-
-# Calcular taxas
-positive = sentiments.count('positive')
-negative = sentiments.count('negative')
-total = len(sentiments)
-
-positive_ratio = positive / total
-negative_ratio = negative / 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."
+# Configuração da query e campos do tweet
+query = 'BBB25 -filter:retweets lang:pt -is:reply'
+tweet_fields = ['text', 'created_at', 'lang', 'public_metrics']
 
-input_ids = tokenizer.encode(prompt, return_tensors='pt')
-
-# Gerar texto com limite de tokens correspondente a 280 caracteres
-outputs = model.generate(input_ids, max_length=25, do_sample=True)
-generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# Limitar o tweet a 280 caracteres
-generated_text = generated_text[:280]
-
-# Postar no Twitter
 try:
-    api.update_status(status=generated_text)
-    print(f"Postado: {generated_text}")
-except Exception as e:
-    print(f"Erro ao postar: {e}")
-
-# Logging (opcional)
-with open('posting_log.txt', 'a') as f:
-    f.write(f"Positive Ratio: {positive_ratio}, Negative Ratio: {negative_ratio}, Posted: {generated_text}\n")
+    # Busca tweets com os campos especificados
+    tweets = client.search_recent_tweets(
+        query=query,
+        max_results=100,
+        tweet_fields=tweet_fields
+    )
     
-
+    if not tweets.data:
+        print("Nenhum tweet encontrado")
+        st.error("Nenhum tweet encontrado para análise")
+        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'])
+            
+    # 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
+        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
+        
+        try:
+            # Postar no Twitter
+            api.update_status(status=generated_text)
+            print(f"Tweet postado com sucesso: {generated_text}")
+            
+            # Interface Streamlit
+            st.title("Análise de Sentimentos - BBB25")
+            
+            # Mostrar estatísticas
+            col1, col2, col3 = st.columns(3)
+            with col1:
+                st.metric("Sentimento Positivo", f"{positive_ratio:.1%}")
+            with col2:
+                st.metric("Sentimento Neutro", f"{neutral_ratio:.1%}")
+            with col3:
+                st.metric("Sentimento Negativo", f"{negative_ratio:.1%}")
+            
+            # Mostrar tweet gerado
+            st.subheader("Tweet Gerado e Postado")
+            st.write(generated_text)
+            
+            # Logging
+            log_entry = {
+                'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+                'positive_ratio': positive_ratio,
+                'negative_ratio': negative_ratio,
+                'neutral_ratio': neutral_ratio,
+                'tweet': generated_text
+            }
+            
+            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)}")
 
 # Footer
 st.markdown("---")
@@ -84,4 +160,4 @@ st.markdown(
     </div>
     """,
     unsafe_allow_html=True
-)
+)