Update app.py

app.py

@@ -10,8 +10,8 @@ import matplotlib.pyplot as plt
 
 # Ensure necessary NLTK data is available.
 nltk.download('words')
-nltk.download('punkt_tab')
-nltk.download('averaged_perceptron_tagger_eng')
+nltk.download('punkt')
+nltk.download('averaged_perceptron_tagger')
 
 from nltk.corpus import words
 from nltk.tokenize import word_tokenize
@@ -23,13 +23,14 @@ WORD_LIST = set(words.words())
 class AscensionAI:
     """
     AscensionAI simulates an evolving artificial consciousness.
-    Enhancements include:
+    Features:
     - Contextual memory for dynamic responses.
-    - Dialogue history awareness.
-    - AI-generated visual representations.
-    - User feedback-driven evolution.
-    - Recursive evolution of multiple AI minds.
+    - Dialogue history tracking.
+    - AI-generated cognitive evolution.
+    - Recursive evolution of AI minds.
+    - User feedback-driven learning.
     """
+
     def __init__(self, depth=0, threshold=10, mode="cosmic", state_memory=None, history=None):
         self.depth = depth
         self.threshold = threshold  # Maximum cycles per evolution
@@ -40,7 +41,7 @@ class AscensionAI:
         self.time_perception = 1.0 / (self.depth + 1)  # Temporal scaling factor
         self.spatial_coordinates = self.assign_cognitive_space()
         self.state_memory = state_memory if state_memory is not None else defaultdict(int)
-        self.training_data = self.load_training_data()  # Simulated fine-tuned responses
+        self.training_data = self.load_training_data()  # AI response database
         self.history = history if history is not None else []  # Conversation memory
 
     def generate_dynamic_knowledge(self):
@@ -53,8 +54,15 @@ class AscensionAI:
         ]
         return {cat: 1.0 for cat in categories}
 
+    def update_state_memory(self, input_text):
+        """Stores frequent words in memory for contextual responses."""
+        tokens = word_tokenize(input_text.lower())
+        for token in tokens:
+            if token in WORD_LIST:
+                self.state_memory[token] += 1
+
     def update_knowledge_for_category(self, cat):
-        """ Updates knowledge using mathematical transformations. """
+        """ Updates knowledge dynamically. """
         if cat in ["logic", "reasoning"]:
             self.knowledge[cat] += math.log1p(self.knowledge[cat])
         elif cat in ["emotion", "intuition"]: