Update app.py

app.py

@@ -9,6 +9,7 @@ from PIL import Image
 from sentence_transformers import SentenceTransformer, util
 import torch
 import numpy as np
+import networkx as nx
 
 @dataclass
 class ChatMessage:
@@ -37,6 +38,15 @@ class XylariaChat:
         self.memory_embeddings = None
         self.embedding_model = SentenceTransformer('all-mpnet-base-v2')
 
+        self.knowledge_graph = nx.DiGraph()
+        self.belief_system = {}
+        self.metacognitive_layer = {
+            "coherence_score": 0.0,
+            "relevance_score": 0.0,
+            "bias_detection": 0.0,
+            "strategy_adjustment": ""
+        }
+        
         self.internal_state = {
             "emotions": {
                 "valence": 0.5,
@@ -62,6 +72,41 @@ class XylariaChat:
         self.internal_state["memory_load"] = np.clip(self.internal_state["memory_load"] + memory_load_delta, 0.0, 1.0)
         self.internal_state["introspection_level"] = np.clip(self.internal_state["introspection_level"] + introspection_delta, 0.0, 1.0)
 
+    def update_knowledge_graph(self, entities, relationships):
+        for entity in entities:
+            self.knowledge_graph.add_node(entity)
+        for relationship in relationships:
+            subject, predicate, object_ = relationship
+            self.knowledge_graph.add_edge(subject, object_, relation=predicate)
+
+    def update_belief_system(self, statement, belief_score):
+        self.belief_system[statement] = belief_score
+
+    def run_metacognitive_layer(self):
+        coherence_score = self.calculate_coherence()
+        relevance_score = self.calculate_relevance()
+        bias_score = self.detect_bias()
+        strategy_adjustment = self.suggest_strategy_adjustment()
+
+        self.metacognitive_layer = {
+            "coherence_score": coherence_score,
+            "relevance_score": relevance_score,
+            "bias_detection": bias_score,
+            "strategy_adjustment": strategy_adjustment
+        }
+    
+    def calculate_coherence(self):
+        return 0.9
+
+    def calculate_relevance(self):
+        return 0.85
+    
+    def detect_bias(self):
+        return 0.1
+    
+    def suggest_strategy_adjustment(self):
+        return "Focus on providing more concise answers."
+
     def introspect(self):
         introspection_report = "Introspection Report:\n"
         introspection_report += f"  Current Emotional State (VAD): {self.internal_state['emotions']}\n"
@@ -70,6 +115,11 @@ class XylariaChat:
         introspection_report += "  Current Goals:\n"
         for goal in self.goals:
             introspection_report += f"    - {goal['goal']} (Priority: {goal['priority']:.2f}, Status: {goal['status']})\n"
+        introspection_report += "Metacognitive Layer Report\n"
+        introspection_report += f"Coherence Score: {self.metacognitive_layer['coherence_score']}\n"
+        introspection_report += f"Relevance Score: {self.metacognitive_layer['relevance_score']}\n"
+        introspection_report += f"Bias Detection: {self.metacognitive_layer['bias_detection']}\n"
+        introspection_report += f"Strategy Adjustment: {self.metacognitive_layer['strategy_adjustment']}\n"
         return introspection_report
 
     def adjust_response_based_on_state(self, response):
@@ -150,6 +200,15 @@ class XylariaChat:
             {"goal": "Maintain a coherent and engaging conversation", "priority": 0.7, "status": "active"}
         ]
 
+        self.knowledge_graph = nx.DiGraph()
+        self.belief_system = {}
+        self.metacognitive_layer = {
+            "coherence_score": 0.0,
+            "relevance_score": 0.0,
+            "bias_detection": 0.0,
+            "strategy_adjustment": ""
+        }
+
         try:
             self.client = InferenceClient(
                 model="Qwen/QwQ-32B-Preview",
@@ -223,6 +282,19 @@ class XylariaChat:
                 role="user",
                 content=user_input
             ).to_dict())
+            
+            entities = []
+            relationships = []
+
+            for message in messages:
+                if message['role'] == 'user':
+                    extracted_entities = self.extract_entities(message['content'])
+                    extracted_relationships = self.extract_relationships(message['content'])
+                    entities.extend(extracted_entities)
+                    relationships.extend(extracted_relationships)
+            
+            self.update_knowledge_graph(entities, relationships)
+            self.run_metacognitive_layer()
 
             input_tokens = sum(len(msg['content'].split()) for msg in messages)
             max_new_tokens = 16384 - input_tokens - 50
@@ -244,6 +316,12 @@ class XylariaChat:
             print(f"Detailed error in get_response: {e}")
             return f"Error generating response: {str(e)}"
 
+    def extract_entities(self, text):
+        return []
+
+    def extract_relationships(self, text):
+        return []
+
     def messages_to_prompt(self, messages):
         prompt = ""
         for msg in messages: