Update app.py

app.py

@@ -10,6 +10,16 @@ from sentence_transformers import SentenceTransformer, util
 import torch
 import numpy as np
 import networkx as nx
+from collections import Counter
+import nltk
+
+# Ensure NLTK resources are available
+try:
+    nltk.data.find('tokenizers/punkt')
+    nltk.data.find('averaged_perceptron_tagger')
+except LookupError:
+    nltk.download('punkt')
+    nltk.download('averaged_perceptron_tagger')
 
 @dataclass
 class ChatMessage:
@@ -75,6 +85,20 @@ class XylariaChat:
         ]
 
         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 """
+        
+        # --- SCALED-DOWN AGI FEATURES ---
+
+        # 1. Advanced Knowledge Representation & Reasoning (Simplified)
+        self.causal_rules_db = {  # Simple rule-based causal relationships
+            "rain": ["wet roads", "flooding"],
+            "study": ["good grades"],
+            "exercise": ["better health"]
+        }
+        self.concept_generalizations = {  # Basic concept generalizations
+            "planet": "system with orbiting bodies",
+            "electron": "system with orbiting bodies",
+            "atom": "system with orbiting bodies"
+        }
 
     def update_internal_state(self, emotion_deltas, cognitive_load_deltas, introspection_delta, engagement_delta):
         # Update emotions with more nuanced changes
@@ -472,6 +496,53 @@ class XylariaChat:
                 stream=True
             )
             
+             # --- SCALED-DOWN AGI FEATURE INTEGRATION INTO RESPONSE GENERATION ---
+
+            # 1.b. Abstract Reasoning (Simplified):
+            # Check if the current message involves a concept with a known generalization.
+            for concept, generalization in self.concept_generalizations.items():
+                if concept in user_input.lower():
+                    inferred_knowledge = f"This reminds me of a general principle: {generalization}."
+                    self.store_information("Inferred Knowledge", inferred_knowledge)  # Store for later retrieval, if needed
+            
+             # 1.c. Dynamic Updating of Beliefs (Simplified):
+            # Very basic example: increase belief if something is stated repeatedly.
+            belief_updates = Counter()
+            for msg in self.conversation_history:
+                if msg['role'] == 'user':
+                    sentences = nltk.sent_tokenize(msg['content']) # Using nltk for sentence tokenization
+                    for sentence in sentences:
+                        belief_updates[sentence] += 1
+
+            for statement, count in belief_updates.items():
+                if count >= 2:  # If a statement is repeated 2 or more times
+                    current_belief_score = self.belief_system.get(statement, 0.5) # Default belief 0.5
+                    updated_belief_score = min(current_belief_score + 0.2, 1.0) # Increase belief, max 1.0
+                    self.update_belief_system(statement, updated_belief_score)
+            
+             # 2.a. Lifelong Learning (Simplified):
+            # Store key information from user input in persistent memory.
+            if user_input:
+                self.store_information("User Input", user_input)
+
+            # 2.b. Autonomous Knowledge Discovery (Very Simplified):
+            # Simulate seeking information if curiosity is high and the user asks a question.
+            if self.internal_state["emotions"]["curiosity"] > 0.8 and "?" in user_input:
+                print("Simulating external knowledge seeking...")
+                # In a real implementation, you might query an API or database here.
+                simulated_external_info = "This is a placeholder for external information I would have found."
+                self.store_information("External Knowledge", simulated_external_info)
+                # The chatbot can then use this "External Knowledge" in its response.
+            
+            # 1.a. Causal Reasoning (Simplified):
+            # Check for potential causal relationships in the user input and conversation history.
+            for cause, effects in self.causal_rules_db.items():
+                if cause in user_input.lower():
+                    for effect in effects:
+                        if effect in " ".join([msg['content'].lower() for msg in self.conversation_history]).lower():
+                            causal_inference = f"It seems {cause} might be related to {effect}."
+                            self.store_information("Causal Inference", causal_inference)
+            
             return stream
         
         except Exception as e:
@@ -596,7 +667,6 @@ class XylariaChat:
             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 {