aa

gurt/background.py

@@ -13,7 +13,9 @@ from .config import (
     LEARNING_UPDATE_INTERVAL, EVOLUTION_UPDATE_INTERVAL, INTEREST_UPDATE_INTERVAL,
     INTEREST_DECAY_INTERVAL_HOURS, INTEREST_PARTICIPATION_BOOST,
     INTEREST_POSITIVE_REACTION_BOOST, INTEREST_NEGATIVE_REACTION_PENALTY,
-    INTEREST_FACT_BOOST, STATS_PUSH_INTERVAL # Added stats interval
+    INTEREST_FACT_BOOST, STATS_PUSH_INTERVAL, # Added stats interval
+    MOOD_OPTIONS, MOOD_CATEGORIES, MOOD_CHANGE_INTERVAL_MIN, MOOD_CHANGE_INTERVAL_MAX, # Mood change imports
+    BASELINE_PERSONALITY # For default traits
 )
 # Assuming analysis functions are moved
 from .analysis import (
@@ -26,7 +28,7 @@ if TYPE_CHECKING:
 # --- Background Task ---
 
 async def background_processing_task(cog: 'GurtCog'):
-    """Background task that periodically analyzes conversations, evolves personality, updates interests, and pushes stats."""
+    """Background task that periodically analyzes conversations, evolves personality, updates interests, changes mood, and pushes stats."""
     # Get API details from environment for stats pushing
     api_internal_url = os.getenv("API_INTERNAL_URL")
     gurt_stats_push_secret = os.getenv("GURT_STATS_PUSH_SECRET")
@@ -108,6 +110,9 @@ async def background_processing_task(cog: 'GurtCog'):
                 cog.last_interest_update = now # Reset timer after update and decay check
                 print("Interest update and decay check complete.")
 
+            # --- Automatic Mood Change (Runs based on its own interval check) ---
+            await maybe_change_mood(cog) # Call the mood change logic
+
     except asyncio.CancelledError:
         print("Background processing task cancelled")
     except Exception as e:
@@ -115,6 +120,111 @@ async def background_processing_task(cog: 'GurtCog'):
         traceback.print_exc()
         await asyncio.sleep(300) # Wait 5 minutes before retrying after an error
 
+# --- Automatic Mood Change Logic ---
+
+async def maybe_change_mood(cog: 'GurtCog'):
+    """Checks if enough time has passed and changes mood based on context."""
+    now = time.time()
+    time_since_last_change = now - cog.last_mood_change
+    next_change_interval = random.uniform(MOOD_CHANGE_INTERVAL_MIN, MOOD_CHANGE_INTERVAL_MAX)
+
+    if time_since_last_change > next_change_interval:
+        print(f"Time for a mood change (interval: {next_change_interval:.0f}s). Analyzing context...")
+        try:
+            # 1. Analyze Sentiment
+            positive_sentiment_score = 0
+            negative_sentiment_score = 0
+            neutral_sentiment_score = 0
+            sentiment_channels_count = 0
+            for channel_id, sentiment_data in cog.conversation_sentiment.items():
+                # Consider only channels active recently (e.g., within the last hour)
+                if now - cog.channel_activity.get(channel_id, 0) < 3600:
+                    if sentiment_data["overall"] == "positive":
+                        positive_sentiment_score += sentiment_data["intensity"]
+                    elif sentiment_data["overall"] == "negative":
+                        negative_sentiment_score += sentiment_data["intensity"]
+                    else:
+                        neutral_sentiment_score += sentiment_data["intensity"]
+                    sentiment_channels_count += 1
+
+            avg_pos_intensity = positive_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
+            avg_neg_intensity = negative_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
+            avg_neu_intensity = neutral_sentiment_score / sentiment_channels_count if sentiment_channels_count > 0 else 0
+            print(f"  - Sentiment Analysis: Pos={avg_pos_intensity:.2f}, Neg={avg_neg_intensity:.2f}, Neu={avg_neu_intensity:.2f}")
+
+            # Determine dominant sentiment category
+            dominant_sentiment = "neutral"
+            if avg_pos_intensity > avg_neg_intensity and avg_pos_intensity > avg_neu_intensity:
+                dominant_sentiment = "positive"
+            elif avg_neg_intensity > avg_pos_intensity and avg_neg_intensity > avg_neu_intensity:
+                dominant_sentiment = "negative"
+
+            # 2. Get Personality Traits
+            personality_traits = await cog.memory_manager.get_all_personality_traits()
+            if not personality_traits:
+                personality_traits = BASELINE_PERSONALITY.copy()
+                print("  - Warning: Using baseline personality traits for mood change.")
+            else:
+                print(f"  - Personality Traits: Mischief={personality_traits.get('mischief', 0):.2f}, Sarcasm={personality_traits.get('sarcasm_level', 0):.2f}, Optimism={personality_traits.get('optimism', 0.5):.2f}")
+
+            # 3. Calculate Mood Weights
+            mood_weights = {mood: 1.0 for mood in MOOD_OPTIONS} # Start with base weight
+
+            # Apply Sentiment Bias (e.g., boost factor of 2)
+            sentiment_boost = 2.0
+            if dominant_sentiment == "positive":
+                for mood in MOOD_CATEGORIES.get("positive", []):
+                    mood_weights[mood] *= sentiment_boost
+            elif dominant_sentiment == "negative":
+                for mood in MOOD_CATEGORIES.get("negative", []):
+                    mood_weights[mood] *= sentiment_boost
+            else: # Neutral sentiment
+                 for mood in MOOD_CATEGORIES.get("neutral", []):
+                    mood_weights[mood] *= (sentiment_boost * 0.75) # Slightly boost neutral too
+
+            # Apply Personality Bias
+            mischief_trait = personality_traits.get('mischief', 0.5)
+            sarcasm_trait = personality_traits.get('sarcasm_level', 0.3)
+            optimism_trait = personality_traits.get('optimism', 0.5)
+
+            if mischief_trait > 0.6: # If high mischief
+                mood_weights["mischievous"] *= (1.0 + mischief_trait) # Boost mischievous based on trait level
+            if sarcasm_trait > 0.5: # If high sarcasm
+                mood_weights["sarcastic"] *= (1.0 + sarcasm_trait)
+                mood_weights["sassy"] *= (1.0 + sarcasm_trait * 0.5) # Also boost sassy a bit
+            if optimism_trait > 0.7: # If very optimistic
+                for mood in MOOD_CATEGORIES.get("positive", []):
+                    mood_weights[mood] *= (1.0 + (optimism_trait - 0.5)) # Boost positive moods
+            elif optimism_trait < 0.3: # If pessimistic
+                 for mood in MOOD_CATEGORIES.get("negative", []):
+                    mood_weights[mood] *= (1.0 + (0.5 - optimism_trait)) # Boost negative moods
+
+            # Ensure current mood has very low weight to avoid picking it again
+            mood_weights[cog.current_mood] = 0.01
+
+            # Filter out moods with zero weight before choices
+            valid_moods = [mood for mood, weight in mood_weights.items() if weight > 0]
+            valid_weights = [mood_weights[mood] for mood in valid_moods]
+
+            if not valid_moods:
+                 print("  - Error: No valid moods with positive weight found. Skipping mood change.")
+                 return # Skip change if something went wrong
+
+            # 4. Select New Mood
+            new_mood = random.choices(valid_moods, weights=valid_weights, k=1)[0]
+
+            # 5. Update State & Log
+            old_mood = cog.current_mood
+            cog.current_mood = new_mood
+            cog.last_mood_change = now
+            print(f"Mood automatically changed: {old_mood} -> {new_mood} (Influenced by: Sentiment={dominant_sentiment}, Traits)")
+
+        except Exception as e:
+            print(f"Error during automatic mood change: {e}")
+            traceback.print_exc()
+            # Still update timestamp to avoid retrying immediately on error
+            cog.last_mood_change = now
+
 # --- Interest Update Logic ---
 
 async def update_interests(cog: 'GurtCog'):

gurt/config.py

@@ -48,6 +48,13 @@ MOOD_OPTIONS = [
     "nostalgic", "confused", "impressed", "skeptical", "enthusiastic",
     "distracted", "focused", "creative", "sarcastic", "wholesome"
 ]
+# Categorize moods for weighted selection
+MOOD_CATEGORIES = {
+    "positive": ["excited", "enthusiastic", "playful", "wholesome", "creative", "impressed"],
+    "negative": ["tired", "a bit bored", "sassy", "sarcastic", "skeptical", "dramatic", "distracted"],
+    "neutral": ["chill", "neutral", "curious", "philosophical", "focused", "confused", "nostalgic"],
+    "mischievous": ["mischievous"] # Special category for trait link
+}
 BASELINE_PERSONALITY = {
     "chattiness": 0.7, "emoji_usage": 0.5, "slang_level": 0.5, "randomness": 0.5,
     "verbosity": 0.4, "optimism": 0.5, "curiosity": 0.6, "sarcasm_level": 0.3,