Update tweet_analyzer.py

tweet_analyzer.py

@@ -78,7 +78,7 @@ class TweetDatasetProcessor:
         analysis_prompt = f"""Perform a deep psychological analysis of the author based on these tweets. Analyze:
         Core beliefs, emotional tendencies, cognitive patterns, etc.
         Tweets for analysis:
-        {json.dumps(all_tweets[:30], indent=2)}
+        {json.dumps(all_tweets[:20], indent=2)}  # Reduce the number of tweets analyzed
         """
 
         response = self.groq_client.chat.completions.create(
@@ -92,7 +92,7 @@ class TweetDatasetProcessor:
         self.personality_profile = response.choices[0].message.content
         return self.personality_profile
 
-    def analyze_topics(self, n_topics=5):
+    def analyze_topics(self, n_topics=3):  # Reduce the number of topics
         """Extract and identify different topics the author has tweeted about."""
         all_tweets = [tweet['content'] for tweet in self.tweets]
         vectorizer = TfidfVectorizer(stop_words='english')
@@ -120,18 +120,18 @@ class TweetDatasetProcessor:
         ]
 
         # Extract historical topics and add them to additional contexts
-        historical_topics = self.analyze_topics(n_topics=10)  # Consider more topics for greater diversity
+        historical_topics = self.analyze_topics(n_topics=3)  # Reduced number of topics
         additional_contexts.extend(historical_topics)
 
         # Randomly select multiple contexts to increase diversity
         selected_contexts = random.sample(additional_contexts, min(3, len(additional_contexts)))
 
         # Randomly sample tweets across different time periods to avoid repetition of topics
-        tweet_sample = random.sample(self.tweets, min(30, len(self.tweets)))  # Increase sample size for diversity
+        tweet_sample = random.sample(self.tweets, min(20, len(self.tweets)))  # Reduce the number of tweets sampled
         all_tweets = [tweet['content'] for tweet in tweet_sample]
 
         # If personality profile is too long, truncate it (adjust length as needed)
-        personality_profile_excerpt = self.personality_profile[:1000]  # Truncate profile to first 1000 characters
+        personality_profile_excerpt = self.personality_profile[:500]  # Truncate further
 
         generation_prompt = f"""Based on this personality profile:
         {personality_profile_excerpt}