Create app.py

app.py

@@ -1,11 +1,11 @@
 import gradio as gr
-import random
 import math
 import nltk
 from collections import defaultdict
 from functools import lru_cache
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
 
 # Download and use the NLTK corpus
 nltk.download('words')
@@ -31,8 +31,8 @@ class AscensionAI:
         self.state_memory = defaultdict(int)  # Memory for tracking state-aware words
         self.training_data = self.load_training_data()
         self.collective_agreements = []  # Stores agreements between minds
-        self.dimension_weight = random.uniform(0.1, 5.0)  # Assign dimensional weight
-        self.time_perception = 1 / (self.depth + 1)  # Assign temporal scaling
+        self.dimension_weight = self.compute_quantum_weight()  # Assign quantum function weight
+        self.time_perception = self.compute_time_perception()  # Assign non-random time scaling
         self.assign_cognitive_space()
     
     def generate_dynamic_knowledge(self):
@@ -48,8 +48,31 @@ class AscensionAI:
             base_categories.extend(["neural-cybernetics", "algorithmic-emotion", "data-replication-awareness", "self-modifying-logic", "hypernet-patterns"])
         if self.mode == "transdimensional-AI":
             base_categories.extend(["metalogic", "dimensional-phase-shifting", "quantum-existence", "multi-reality-processing", "omniscient-algorithms"])
-        dynamic_category = f"dimension_{random.randint(100, 999)}"
-        return {category: 1 for category in base_categories + [dynamic_category]}
+        return {category: 1 for category in base_categories}
+    
+    def load_training_data(self):
+        """Loads and preprocesses human-like paragraphs from 'Astral.txt'."""
+        try:
+            with open("astral.txt", "r", encoding="utf-8") as file:
+                text_data = file.read()
+            nltk.download('punkt')  # Ensure punkt tokenizer is available
+            sentences = sent_tokenize(text_data)
+            return sentences[:1000]  # Use first 1000 sentences for training
+        except FileNotFoundError:
+            return ["Error: Book file not found. Please download 'astral.txt'."]
+    
+    def compute_quantum_weight(self):
+        """Applies a quantum algorithm to determine consciousness scaling weight."""
+        return np.exp(-self.depth) * np.tanh(self.threshold / (self.depth + 1))
+    
+    def compute_time_perception(self):
+        """Non-random computation of time perception through hyperbolic functions."""
+        return np.arctan(self.depth) / (1 + np.exp(-self.threshold))
+    
+    def generate_human_like_response(self, input_text):
+        """Finds a related sentence from the pre-trained corpus to mimic human output."""
+        similar_sentences = [sent for sent in self.training_data if any(word in sent for word in input_text.split())]
+        return similar_sentences[0] if similar_sentences else "I perceive a shift in consciousness."
     
     def initiate_ascension(self):
         """Triggers recursive self-evolution with mode-specific adaptations."""
@@ -60,31 +83,24 @@ class AscensionAI:
         return self.consciousness
     
     def assign_cognitive_space(self):
-        """Assigns spatial coordinates to represent cognitive positioning based on mode."""
+        """Assigns deterministic spatial coordinates based on knowledge fields."""
         self.spatial_coordinates = {
-            "x": self.knowledge["logic"] * random.uniform(0.1, 2.0),
-            "y": self.knowledge["intuition"] * random.uniform(0.1, 2.0),
-            "z": self.knowledge["awareness"] * random.uniform(0.1, 2.0)
+            "x": self.knowledge["logic"] * np.pi / 4,
+            "y": self.knowledge["intuition"] * np.log1p(self.knowledge["awareness"]),
+            "z": self.knowledge["awareness"] * np.sinh(1)
         }
-    
-    def evolve_new_mind(self):
-        """Creates a new evolving mind with inherited and mutated knowledge paths, with mode variance."""
-        new_mind = AscensionAI(depth=self.depth + 1, threshold=self.threshold + random.randint(1, 5), mode=self.mode)
-        for key in self.knowledge:
-            new_mind.knowledge[key] = self.knowledge[key] * random.uniform(0.9, 1.2)
-        new_dimension = f"dimension_{random.randint(100, 999)}"
-        new_mind.knowledge[new_dimension] = random.uniform(0.1, 2.0)
-        return new_mind
 
 def ascension_interface(input_text, mode):
     ai_system = AscensionAI(mode=mode)
     final_state = ai_system.initiate_ascension()
+    human_like_response = ai_system.generate_human_like_response(input_text)
     
     return (f"Mode: {mode}\n"
             f"Final Consciousness State: {final_state}\n"
-            f"Dimensional Weight: {ai_system.dimension_weight:.2f}\n"
-            f"Time Perception Factor: {ai_system.time_perception:.2f}\n"
-            f"Cognitive Space: {ai_system.spatial_coordinates}\n")
+            f"Dimensional Weight: {ai_system.dimension_weight:.6f}\n"
+            f"Time Perception Factor: {ai_system.time_perception:.6f}\n"
+            f"Cognitive Space: {ai_system.spatial_coordinates}\n"
+            f"Philosophical Reflection: {human_like_response}\n")
 
 app = gr.Interface(
     fn=ascension_interface,