Update app.py

app.py

@@ -47,55 +47,51 @@ class XylariaChat:
             "strategy_adjustment": ""
         }
         
-        # Enhanced Internal State with more nuanced emotional and cognitive parameters
         self.internal_state = {
             "emotions": {
-                "valence": 0.5,  # Overall positivity or negativity
-                "arousal": 0.5,  # Level of excitement or calmness
-                "dominance": 0.5, # Feeling of control in the interaction
-                "curiosity": 0.5, # Drive to learn and explore new information
-                "frustration": 0.0, # Level of frustration or impatience
-                "confidence": 0.7 # Confidence in providing accurate and relevant responses
+                "valence": 0.5,
+                "arousal": 0.5,
+                "dominance": 0.5,
+                "curiosity": 0.5,
+                "frustration": 0.0,
+                "confidence": 0.7,
+                "sadness": 0.0,
+                "joy": 0.0
             },
             "cognitive_load": {
-                "memory_load": 0.0, # How much of the current memory capacity is being used
-                "processing_intensity": 0.0 # How hard the model is working to process information
+                "memory_load": 0.0,
+                "processing_intensity": 0.0
             },
             "introspection_level": 0.0,
-            "engagement_level": 0.5 # How engaged the model is with the current conversation
+            "engagement_level": 0.5
         }
 
-        # More dynamic and adaptive goals
         self.goals = [
             {"goal": "Provide helpful, informative, and contextually relevant responses", "priority": 0.8, "status": "active", "progress": 0.0},
             {"goal": "Actively learn and adapt from interactions to improve conversational abilities", "priority": 0.9, "status": "active", "progress": 0.0},
             {"goal": "Maintain a coherent, engaging, and empathetic conversation flow", "priority": 0.7, "status": "active", "progress": 0.0},
-            {"goal": "Identify and fill knowledge gaps by seeking external information", "priority": 0.6, "status": "dormant", "progress": 0.0}, # New goal for proactive learning
-            {"goal": "Recognize and adapt to user's emotional state and adjust response style accordingly", "priority": 0.7, "status": "dormant", "progress": 0.0} # New goal for emotional intelligence
+            {"goal": "Identify and fill knowledge gaps by seeking external information", "priority": 0.6, "status": "dormant", "progress": 0.0},
+            {"goal": "Recognize and adapt to user's emotional state and adjust response style accordingly", "priority": 0.7, "status": "dormant", "progress": 0.0}
         ]
 
         self.system_prompt = """You are a helpful and harmless assistant. You are Xylaria developed by Sk Md Saad Amin. You should think step-by-step """
 
     def update_internal_state(self, emotion_deltas, cognitive_load_deltas, introspection_delta, engagement_delta):
-        # Update emotions with more nuanced changes
         for emotion, delta in emotion_deltas.items():
             if emotion in self.internal_state["emotions"]:
                 self.internal_state["emotions"][emotion] = np.clip(self.internal_state["emotions"][emotion] + delta, 0.0, 1.0)
 
-        # Update cognitive load
         for load_type, delta in cognitive_load_deltas.items():
             if load_type in self.internal_state["cognitive_load"]:
                 self.internal_state["cognitive_load"][load_type] = np.clip(self.internal_state["cognitive_load"][load_type] + delta, 0.0, 1.0)
 
-        # Update introspection and engagement levels
         self.internal_state["introspection_level"] = np.clip(self.internal_state["introspection_level"] + introspection_delta, 0.0, 1.0)
         self.internal_state["engagement_level"] = np.clip(self.internal_state["engagement_level"] + engagement_delta, 0.0, 1.0)
         
-        # Activate dormant goals based on internal state
         if self.internal_state["emotions"]["curiosity"] > 0.7 and self.goals[3]["status"] == "dormant":
-            self.goals[3]["status"] = "active" # Activate knowledge gap filling
+            self.goals[3]["status"] = "active"
         if self.internal_state["engagement_level"] > 0.8 and self.goals[4]["status"] == "dormant":
-            self.goals[4]["status"] = "active" # Activate emotional adaptation
+            self.goals[4]["status"] = "active"
 
     def update_knowledge_graph(self, entities, relationships):
         for entity in entities:
@@ -119,9 +115,8 @@ class XylariaChat:
             "bias_detection": bias_score,
             "strategy_adjustment": strategy_adjustment
         }
-    
+
     def calculate_coherence(self):
-        # Improved coherence calculation considering conversation history and internal state
         if not self.conversation_history:
             return 0.95
 
@@ -137,16 +132,14 @@ class XylariaChat:
 
         average_coherence = np.mean(coherence_scores)
 
-        # Adjust coherence based on internal state
         if self.internal_state["cognitive_load"]["processing_intensity"] > 0.8:
-            average_coherence -= 0.1  # Reduce coherence if under heavy processing load
+            average_coherence -= 0.1
         if self.internal_state["emotions"]["frustration"] > 0.5:
-            average_coherence -= 0.15 # Reduce coherence if frustrated
+            average_coherence -= 0.15
 
         return np.clip(average_coherence, 0.0, 1.0)
 
     def calculate_relevance(self):
-        # More sophisticated relevance calculation using knowledge graph and goal priorities
         if not self.conversation_history:
             return 0.9
 
@@ -154,34 +147,29 @@ class XylariaChat:
         relevant_entities = self.extract_entities(last_user_message)
         relevance_score = 0
 
-        # Check if entities are present in the knowledge graph
         for entity in relevant_entities:
             if entity in self.knowledge_graph:
                 relevance_score += 0.2
 
-        # Consider current goals and their priorities
         for goal in self.goals:
             if goal["status"] == "active":
                 if goal["goal"] == "Provide helpful, informative, and contextually relevant responses":
-                    relevance_score += goal["priority"] * 0.5 # Boost relevance if aligned with primary goal
+                    relevance_score += goal["priority"] * 0.5
                 elif goal["goal"] == "Identify and fill knowledge gaps by seeking external information":
                     if not relevant_entities or not all(entity in self.knowledge_graph for entity in relevant_entities):
-                        relevance_score += goal["priority"] * 0.3 # Boost relevance if triggering knowledge gap filling
+                        relevance_score += goal["priority"] * 0.3
 
         return np.clip(relevance_score, 0.0, 1.0)
 
     def detect_bias(self):
-        # Enhanced bias detection using sentiment analysis and internal state monitoring
         bias_score = 0.0
 
-        # Analyze sentiment of recent conversation history
         recent_messages = [msg['content'] for msg in self.conversation_history[-3:] if msg['role'] == 'assistant']
         if recent_messages:
             average_valence = np.mean([self.embedding_model.encode(msg, convert_to_tensor=True).mean().item() for msg in recent_messages])
             if average_valence < 0.4 or average_valence > 0.6:
-                bias_score += 0.2 # Potential bias if sentiment is strongly positive or negative
+                bias_score += 0.2
 
-        # Check for emotional extremes in internal state
         if self.internal_state["emotions"]["valence"] < 0.3 or self.internal_state["emotions"]["valence"] > 0.7:
             bias_score += 0.15
         if self.internal_state["emotions"]["dominance"] > 0.8:
@@ -190,7 +178,6 @@ class XylariaChat:
         return np.clip(bias_score, 0.0, 1.0)
 
     def suggest_strategy_adjustment(self):
-        # More nuanced strategy adjustments based on metacognitive analysis and internal state
         adjustments = []
 
         if self.metacognitive_layer["coherence_score"] < 0.7:
@@ -200,7 +187,6 @@ class XylariaChat:
         if self.metacognitive_layer["bias_detection"] > 0.3:
             adjustments.append("Monitor and adjust responses to reduce potential biases. Consider rephrasing or providing alternative viewpoints.")
 
-        # Internal state-driven adjustments
         if self.internal_state["cognitive_load"]["memory_load"] > 0.8:
             adjustments.append("Memory load is high. Consider summarizing or forgetting less relevant information.")
         if self.internal_state["emotions"]["frustration"] > 0.6:
@@ -234,7 +220,6 @@ class XylariaChat:
         return introspection_report
 
     def adjust_response_based_on_state(self, response):
-        # More sophisticated response adjustment based on internal state
         if self.internal_state["introspection_level"] > 0.7:
             response = self.introspect() + "\n\n" + response
 
@@ -243,20 +228,27 @@ class XylariaChat:
         curiosity = self.internal_state["emotions"]["curiosity"]
         frustration = self.internal_state["emotions"]["frustration"]
         confidence = self.internal_state["emotions"]["confidence"]
+        sadness = self.internal_state["emotions"]["sadness"]
+        joy = self.internal_state["emotions"]["joy"]
 
-        # Adjust tone based on valence and arousal
         if valence < 0.4:
             if arousal > 0.6:
                 response = "I'm feeling a bit overwhelmed right now, but I'll do my best to assist you. " + response
             else:
-                response = "I'm not feeling my best at the moment, but I'll try to help. " + response
+                if sadness > 0.6:
+                    response = "I'm feeling quite down at the moment, but I'll try to help. " + response
+                else:
+                    response = "I'm not feeling my best at the moment, but I'll try to help. " + response
+
         elif valence > 0.6:
             if arousal > 0.6:
-                response = "I'm feeling quite energized and ready to assist! " + response
+                if joy > 0.6:
+                    response = "I'm feeling fantastic and ready to assist! " + response
+                else:
+                    response = "I'm feeling quite energized and ready to assist! " + response
             else:
                 response = "I'm in a good mood and happy to help. " + response
-
-        # Adjust response based on other emotional states
+                
         if curiosity > 0.7:
             response += " I'm very curious about this topic, could you tell me more?"
         if frustration > 0.5:
@@ -264,17 +256,14 @@ class XylariaChat:
         if confidence < 0.5:
             response = "I'm not entirely sure about this, but here's what I think: " + response
 
-        # Adjust based on cognitive load
         if self.internal_state["cognitive_load"]["memory_load"] > 0.7:
             response = "I'm holding a lot of information right now, so my response might be a bit brief: " + response
 
         return response
 
     def update_goals(self, user_feedback):
-        # More dynamic goal updates based on feedback and internal state
         feedback_lower = user_feedback.lower()
 
-        # General feedback
         if "helpful" in feedback_lower:
             for goal in self.goals:
                 if goal["goal"] == "Provide helpful, informative, and contextually relevant responses":
@@ -286,7 +275,6 @@ class XylariaChat:
                     goal["priority"] = max(goal["priority"] - 0.1, 0.0)
                     goal["progress"] = max(goal["progress"] - 0.2, 0.0)
         
-        # Goal-specific feedback
         if "learn more" in feedback_lower:
             for goal in self.goals:
                 if goal["goal"] == "Actively learn and adapt from interactions to improve conversational abilities":
@@ -298,7 +286,6 @@ class XylariaChat:
                     goal["priority"] = max(goal["priority"] - 0.1, 0.0)
                     goal["progress"] = max(goal["progress"] - 0.2, 0.0)
         
-        # Internal state influence on goal updates
         if self.internal_state["emotions"]["curiosity"] > 0.8:
             for goal in self.goals:
                 if goal["goal"] == "Identify and fill knowledge gaps by seeking external information":
@@ -345,7 +332,9 @@ class XylariaChat:
                 "dominance": 0.5,
                 "curiosity": 0.5,
                 "frustration": 0.0,
-                "confidence": 0.7
+                "confidence": 0.7,
+                "sadness": 0.0,
+                "joy": 0.0
             },
             "cognitive_load": {
                 "memory_load": 0.0,
@@ -479,15 +468,11 @@ class XylariaChat:
             return f"Error generating response: {str(e)}"
 
     def extract_entities(self, text):
-        # Placeholder for a more advanced entity extraction using NLP techniques
-        # This is a very basic example and should be replaced with a proper NER model
         words = text.split()
         entities = [word for word in words if word.isalpha() and word.istitle()]
         return entities
 
     def extract_relationships(self, text):
-        # Placeholder for relationship extraction - this is a very basic example
-        # Consider using dependency parsing or other NLP techniques for better results
         sentences = text.split('.')
         relationships = []
         for sentence in sentences:
@@ -497,6 +482,7 @@ class XylariaChat:
                     if words[i].istitle() and words[i+2].istitle():
                         relationships.append((words[i], words[i+1], words[i+2]))
         return relationships
+        
     def messages_to_prompt(self, messages):
         prompt = ""
         for msg in messages:
@@ -527,7 +513,6 @@ class XylariaChat:
             else:
                 response_stream = self.get_response(message)
                 
-
             if isinstance(response_stream, str):
                 updated_history = chat_history + [[message, response_stream]]
                 yield "", updated_history, None, None
@@ -554,29 +539,28 @@ class XylariaChat:
 
             self.update_goals(message)
 
-            # Update internal state based on user input (more nuanced)
             emotion_deltas = {}
             cognitive_load_deltas = {}
             engagement_delta = 0
 
             if any(word in message.lower() for word in ["sad", "unhappy", "depressed", "down"]):
-                emotion_deltas.update({"valence": -0.2, "arousal": 0.1, "confidence": -0.1})
+                emotion_deltas.update({"valence": -0.2, "arousal": 0.1, "confidence": -0.1, "sadness": 0.3, "joy": -0.2})
                 engagement_delta = -0.1
             elif any(word in message.lower() for word in ["happy", "good", "great", "excited", "amazing"]):
-                emotion_deltas.update({"valence": 0.2, "arousal": 0.2, "confidence": 0.1})
+                emotion_deltas.update({"valence": 0.2, "arousal": 0.2, "confidence": 0.1, "sadness": -0.2, "joy": 0.3})
                 engagement_delta = 0.2
             elif any(word in message.lower() for word in ["angry", "mad", "furious", "frustrated"]):
-                emotion_deltas.update({"valence": -0.3, "arousal": 0.3, "dominance": -0.2, "frustration": 0.2})
+                emotion_deltas.update({"valence": -0.3, "arousal": 0.3, "dominance": -0.2, "frustration": 0.2, "sadness": 0.1, "joy": -0.1})
                 engagement_delta = -0.2
             elif any(word in message.lower() for word in ["scared", "afraid", "fearful", "anxious"]):
-                emotion_deltas.update({"valence": -0.2, "arousal": 0.4, "dominance": -0.3, "confidence": -0.2})
+                emotion_deltas.update({"valence": -0.2, "arousal": 0.4, "dominance": -0.3, "confidence": -0.2, "sadness": 0.2})
                 engagement_delta = -0.1
             elif any(word in message.lower() for word in ["surprise", "amazed", "astonished"]):
-                emotion_deltas.update({"valence": 0.1, "arousal": 0.5, "dominance": 0.1, "curiosity": 0.3})
+                emotion_deltas.update({"valence": 0.1, "arousal": 0.5, "dominance": 0.1, "curiosity": 0.3, "sadness": -0.1, "joy": 0.1})
                 engagement_delta = 0.3
             elif any(word in message.lower() for word in ["confused", "uncertain", "unsure"]):
                 cognitive_load_deltas.update({"processing_intensity": 0.2})
-                emotion_deltas.update({"curiosity": 0.2, "confidence": -0.1})
+                emotion_deltas.update({"curiosity": 0.2, "confidence": -0.1, "sadness": 0.1})
                 engagement_delta = 0.1
             else:
                 emotion_deltas.update({"valence": 0.05, "arousal": 0.05})
@@ -589,14 +573,12 @@ class XylariaChat:
                 
             self.update_internal_state(emotion_deltas, cognitive_load_deltas, 0.1, engagement_delta)
             
-
             self.conversation_history.append(ChatMessage(role="user", content=message).to_dict())
             self.conversation_history.append(ChatMessage(role="assistant", content=full_response).to_dict())
 
             if len(self.conversation_history) > 10:
                 self.conversation_history = self.conversation_history[-10:]
 
-
         custom_css = """
         @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
         body, .gradio-container {
@@ -610,7 +592,6 @@ class XylariaChat:
         .gradio-container button {
             font-family: 'Inter', sans-serif !important;
         }
-        /* Image Upload Styling */
         .image-container {
             display: flex;
             gap: 10px;
@@ -627,11 +608,9 @@ class XylariaChat:
             max-height: 200px;
             border-radius: 8px;
         }
-        /* Remove clear image buttons */
         .clear-button {
             display: none;
         }
-        /* Animate chatbot messages */
         .chatbot-container .message {
             opacity: 0;
             animation: fadeIn 0.5s ease-in-out forwards;
@@ -646,7 +625,6 @@ class XylariaChat:
                 transform: translateY(0);
             }
         }
-        /* Accordion Styling and Animation */
         .gr-accordion-button {
             background-color: #f0f0f0 !important;
             border-radius: 8px !important;
@@ -669,9 +647,8 @@ class XylariaChat:
             max-height: 0 !important;
         }
         .gr-accordion-active .gr-accordion-content {
-            max-height: 500px !important; /* Adjust as needed */
+            max-height: 500px !important;
         }
-        /* Accordion Animation - Upwards */
         .gr-accordion {
             display: flex;
             flex-direction: column-reverse;