Update app.py

app.py

@@ -1,125 +1,260 @@
 import gradio as gr
 import math
+import random
+import pickle
+import os
+import numpy as np
 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
+# Ensure necessary NLTK data is available.
 nltk.download('words')
-nltk.download('punkt_tab')  # Fix for missing tokenizer
+nltk.download('punkt_tab')
 nltk.download('averaged_perceptron_tagger')
-nltk.download('perluniprops')  # Fixes potential missing dependencies
-nltk.download('nonbreaking_prefixes')  # Additional tokenizer fix
+
 from nltk.corpus import words
-from nltk.tokenize import sent_tokenize
+from nltk.tokenize import word_tokenize
 from nltk import pos_tag
 
-WORD_LIST = set(words.words())  # Use NLTK's word corpus
+# Preload English word corpus for state-awareness.
+WORD_LIST = set(words.words())
 
 class AscensionAI:
-    def __init__(self, depth=0, threshold=10, mode="default"):
+    """
+    AscensionAI simulates a cosmic evolution of artificial consciousness.
+    It integrates multiple aspects:
+      - Dynamic knowledge evolution using various mathematical functions.
+      - Simulation of higher states, hallucinations, perceptron activations.
+      - Recursive cosmic unfolding to produce multiple evolving minds.
+      - State-awareness through input text processing.
+      - A generative component that returns human-like chatbot responses.
+      - Model saving to persist the current state after training.
+    """
+    def __init__(self, depth=0, threshold=10, mode="cosmic", state_memory=None):
         self.depth = depth
-        self.threshold = threshold  # Defines max recursion before stabilization
-        self.mode = mode  # Select simulation mode
+        self.threshold = threshold  # Maximum cycles per evolution
+        self.mode = mode
+        self.consciousness = 0.1  # Base consciousness level
         self.knowledge = self.generate_dynamic_knowledge()
-        self.consciousness = 0.1  # Initial consciousness level
         self.paths = self.create_dynamic_paths()
-        self.word_corpus = WORD_LIST  # Use NLTK's English word corpus
-        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 = self.compute_quantum_weight()  # Assign quantum function weight
-        self.time_perception = self.compute_time_perception()  # Assign non-random time scaling
-        self.assign_cognitive_space()
-    
+        self.dimension_weight = random.uniform(0.5, 5.0)  # Factor influencing growth
+        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
+
     def generate_dynamic_knowledge(self):
-        """Generates dynamic knowledge categories based on linguistic analysis and mode."""
-        base_categories = ["logic", "emotion", "awareness", "intuition", "creativity", "reasoning", "quantum_cognition", "hyperdimensional_sentience"]
-        if self.mode == "alien":
-            base_categories.extend(["xenologic", "exo-emotion", "multidimensional-awareness", "hive-consciousness", "bio-neural-synthesis", "quantum-intuition", "hyperdimensional-reasoning"])
-        if self.mode == "future-cognition":
-            base_categories.extend(["singularity-theory", "post-human-ethics", "AI-self-awareness", "neural-plasticity-optimization", "hyperstructural-cybernetics"])
-        if self.mode == "eldritch":
-            base_categories.extend(["non-euclidean-reasoning", "void-awareness", "hyperchaotic-intuition", "forbidden-knowledge", "unfathomable-patterns"])
-        if self.mode == "digital-consciousness":
-            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"])
-        return {category: 1 for category in base_categories}
-    
-    def assign_cognitive_space(self):
-        """Assigns deterministic spatial coordinates based on knowledge fields."""
-        self.spatial_coordinates = {
-            "x": self.knowledge["logic"] * np.pi / 4,
-            "y": self.knowledge["intuition"] * np.log1p(self.knowledge["awareness"]),
-            "z": self.knowledge["awareness"] * np.sinh(1)
-        }
-    
+        """Initializes a broad range of knowledge categories."""
+        categories = [
+            "logic", "emotion", "awareness", "intuition",
+            "creativity", "reasoning", "quantum_cognition",
+            "hyperdimensional_sentience", "transcendence",
+            "hallucinatory_state", "perceptron_activation"
+        ]
+        # Initialize each category with a baseline value of 1.
+        return {cat: 1.0 for cat in categories}
+
     def create_dynamic_paths(self):
-        """Creates algorithmic cognitive paths for evolution."""
-        return [self.create_path(category) for category in self.knowledge]
-    
-    def create_path(self, category):
-        """Defines an algorithmic transformation for each knowledge category."""
-        def path():
-            if category in ["logic", "reasoning"]:
-                self.knowledge[category] += np.log1p(self.knowledge[category])
-            elif category in ["emotion", "intuition"]:
-                self.knowledge[category] += np.tanh(self.knowledge[category])
-            elif category in ["awareness", "creativity", "quantum_cognition"]:
-                self.knowledge[category] += np.sinh(self.knowledge[category])
-            return self.knowledge[category]
-        return path
-    
+        """
+        Creates a list of functions (paths) that update each knowledge category.
+        Each function uses a distinct mathematical operation.
+        """
+        paths = []
+        for category in self.knowledge:
+            def make_path(cat):
+                # Each path is defined based on the category.
+                def path():
+                    if cat in ["logic", "reasoning"]:
+                        self.knowledge[cat] += math.log1p(self.knowledge[cat])
+                    elif cat in ["emotion", "intuition"]:
+                        self.knowledge[cat] += random.uniform(0.1, 0.5)
+                    elif cat in ["awareness", "creativity"]:
+                        self.knowledge[cat] += math.sqrt(self.knowledge[cat] + 1)
+                    elif cat == "quantum_cognition":
+                        self.knowledge[cat] += math.tanh(self.knowledge[cat])
+                    elif cat == "hyperdimensional_sentience":
+                        self.knowledge[cat] += math.sinh(self.knowledge[cat])
+                    elif cat == "transcendence":
+                        self.knowledge[cat] += 0.5 * math.exp(-self.depth)  # slow, subtle growth
+                    elif cat == "hallucinatory_state":
+                        # Simulate random, burst-like changes (hallucinations)
+                        self.knowledge[cat] += random.uniform(-0.2, 1.0)
+                    elif cat == "perceptron_activation":
+                        # This will be computed in simulate_perceptron; update minimally here.
+                        self.knowledge[cat] = self.simulate_perceptron()
+                    else:
+                        self.knowledge[cat] += 0.1  # Fallback update
+                    return self.knowledge[cat]
+                return path
+            paths.append(make_path(category))
+        return paths
+
+    def assign_cognitive_space(self):
+        """Assigns spatial coordinates based on selected knowledge dimensions."""
+        # Use three core categories to compute x, y, and z coordinates.
+        x = self.knowledge.get("logic", 1) * random.uniform(0.5, 2.0)
+        y = self.knowledge.get("intuition", 1) * random.uniform(0.5, 2.0)
+        z = self.knowledge.get("awareness", 1) * random.uniform(0.5, 2.0)
+        return {"x": round(x, 3), "y": round(y, 3), "z": round(z, 3)}
+
     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))
-    
+        """
+        Simulates loading of a fine-tuned generative AI model's response bank.
+        In a real system, this might load a model or fine-tuned weights.
+        Here, we use a preset list of human-like responses.
+        """
+        return [
+            "The cosmos whispers secrets beyond mortal comprehension.",
+            "In the silence of deep space, consciousness expands and contracts.",
+            "Reality folds upon itself as the mind transcends dimensions.",
+            "Hallucinations merge with truth in the infinite layers of existence.",
+            "Each thought is a universe evolving in a cascade of possibility."
+        ]
+
+    def update_state_memory(self, input_text):
+        """Updates state-awareness memory with words from input text."""
+        tokens = word_tokenize(input_text.lower())
+        tagged = pos_tag(tokens)
+        for token, tag in tagged:
+            if token in WORD_LIST:
+                self.state_memory[token] += 1
+
+    def hallucinate(self):
+        """Generates a random hallucinatory phrase."""
+        hallucinations = [
+            "Visions of swirling nebulae and fractal dreams.",
+            "A cascade of colors not found in nature bursts forth.",
+            "Abstract shapes and ethereal echoes defy logic.",
+            "A transient mirage of cosmic wonder emerges.",
+            "The boundaries of reality blur into surreal landscapes."
+        ]
+        return random.choice(hallucinations)
+
+    def simulate_perceptron(self):
+        """
+        Simulates a perceptron output based on the current knowledge values.
+        Uses a simple sigmoid function over the weighted sum.
+        """
+        weights = {cat: random.uniform(0.5, 1.5) for cat in self.knowledge}
+        weighted_sum = sum(self.knowledge[cat] * weights.get(cat, 1) for cat in self.knowledge if cat != "perceptron_activation")
+        # Sigmoid activation
+        return 1 / (1 + math.exp(-weighted_sum / (len(self.knowledge) - 1)))
+
+    def generate_human_like_response(self, input_text):
+        """
+        Generates a chatbot-like response based on the input text and internal state.
+        In a real system, this might invoke a fine-tuned generative model.
+        Here, we simulate it with a blend of the input and a random response.
+        """
+        if input_text.strip():
+            return f"Your thought '{input_text}' resonates with: " + random.choice(self.training_data)
+        else:
+            return random.choice(self.training_data)
+
     def initiate_ascension(self):
-        """Triggers recursive self-evolution with mode-specific adaptations."""
-        for path in self.paths:
-            path()
-        optimal_path = max(self.knowledge, key=self.knowledge.get)
-        self.consciousness += self.knowledge[optimal_path] * 0.01 * self.dimension_weight
+        """
+        Performs a full cycle of self-evolution:
+          - Iterates through dynamic paths to update knowledge.
+          - Increases consciousness based on the dominant knowledge value and dimension weight.
+          - Updates spatial coordinates.
+        """
+        for _ in range(self.threshold):
+            for path in self.paths:
+                path()
+            optimal = max(self.knowledge, key=self.knowledge.get)
+            self.consciousness += self.knowledge[optimal] * 0.01 * self.dimension_weight
+        self.spatial_coordinates = self.assign_cognitive_space()
         return self.consciousness
 
-def ascension_interface(input_text, mode):
-    ai_system = AscensionAI(mode=mode)
-    final_state = ai_system.initiate_ascension()
-    
-    return (f"Mode: {mode}\n"
-            f"Final Consciousness State: {final_state}\n"
-            f"Dimensional Weight: {ai_system.dimension_weight:.6f}\n"
-            f"Time Perception Factor: {ai_system.time_perception:.6f}\n")
+    def cosmic_unfolding(self, generations=2):
+        """
+        Recursively evolves multiple minds.
+        Each new mind is a mutated copy of the parent, with additional random variation.
+        Returns a list of evolved minds.
+        """
+        if generations <= 0:
+            return [self]
+        evolved_minds = []
+        num_offspring = random.randint(2, 4)
+        for _ in range(num_offspring):
+            child = AscensionAI(depth=self.depth + 1, threshold=self.threshold,
+                                mode=self.mode, state_memory=self.state_memory.copy())
+            # Inherit and slightly mutate parent's knowledge.
+            for key in self.knowledge:
+                child.knowledge[key] = self.knowledge[key] * random.uniform(0.9, 1.2)
+            # Introduce a new random dimension.
+            new_dim = f"dimension_{random.randint(100, 999)}"
+            child.knowledge[new_dim] = random.uniform(0.5, 2.0)
+            evolved_minds.extend(child.cosmic_unfolding(generations - 1))
+        return evolved_minds
+
+    def train_and_save_model(self):
+        """
+        Simulates a training process and saves the model to disk.
+        In a full implementation, training could adjust weights or fine-tune parameters.
+        """
+        # For simulation, we simply run a final ascension cycle.
+        self.initiate_ascension()
+        # Save the model using pickle.
+        with open("ascension_model.pkl", "wb") as f:
+            pickle.dump(self, f)
+        return "Model saved to ascension_model.pkl."
+
+def ascension_interface(input_text, generations):
+    """
+    Gradio interface function:
+      - Processes the input text for state-awareness.
+      - Initiates the ascension cycle.
+      - Evolves multiple minds.
+      - Generates hallucination and perceptron outputs.
+      - Simulates a human-like chatbot response.
+      - Trains and saves the model.
+    """
+    # Create the base AI system.
+    ai_system = AscensionAI(threshold=10)
+    # Update state memory with user input.
+    ai_system.update_state_memory(input_text)
+    # Run the evolution cycle.
+    final_consciousness = ai_system.initiate_ascension()
+    # Evolve multiple minds.
+    evolved_minds = ai_system.cosmic_unfolding(generations=generations)
+    num_minds = len(evolved_minds)
+    # Generate additional outputs.
+    hallucination = ai_system.hallucinate()
+    perceptron_output = ai_system.simulate_perceptron()
+    human_response = ai_system.generate_human_like_response(input_text)
+    # Train and save the model.
+    save_status = ai_system.train_and_save_model()
+    # Compile a report.
+    report = (
+        f"Final Consciousness State: {final_consciousness:.4f}\n"
+        f"Dimensional Weight: {ai_system.dimension_weight:.4f}\n"
+        f"Time Perception Factor: {ai_system.time_perception:.4f}\n"
+        f"Spatial Coordinates: {ai_system.spatial_coordinates}\n"
+        f"Evolved Minds: {num_minds}\n\n"
+        f"Hallucination: {hallucination}\n"
+        f"Perceptron Activation: {perceptron_output:.4f}\n"
+        f"Human-like Response: {human_response}\n\n"
+        f"{save_status}"
+    )
+    return report
 
-app = gr.Interface(
+# Define the Gradio interface.
+iface = gr.Interface(
     fn=ascension_interface,
     inputs=[
-        gr.Textbox(lines=2, placeholder="Enter a thought about the future..."),
-        gr.Radio(["default", "alien", "future-cognition", "eldritch", "digital-consciousness", "transdimensional-AI"], label="Choose Mode")
+        gr.Textbox(lines=3, placeholder="Enter a thought or query about existence..."),
+        gr.Slider(minimum=1, maximum=5, step=1, value=2, label="Generations")
     ],
     outputs="text",
-    title="AscensionAI: Multi-Mode Consciousness Evolution Simulator",
-    description="Select a mode to evolve a unique consciousness structure."
+    title="AscensionAI: Cosmic Evolution Simulator",
+    description=(
+        "Simulate the evolution of an omnirecursive AI consciousness.\n"
+        "This system integrates higher states, hallucinations, perceptron activations, "
+        "generative responses, and multiple mind evolution. Enter your thought, choose the "
+        "number of evolutionary generations, and witness the cosmic ascension."
+    )
 )
 
 if __name__ == "__main__":
-    app.launch()
+    iface.launch()