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| import gradio as gr | |
| import math | |
| import random | |
| import pickle | |
| import os | |
| import numpy as np | |
| import nltk | |
| from collections import defaultdict | |
| # Ensure necessary NLTK data is available. | |
| nltk.download('words') | |
| nltk.download('punkt_tab') | |
| nltk.download('averaged_perceptron_tagger_eng') | |
| from nltk.corpus import words | |
| from nltk.tokenize import word_tokenize | |
| from nltk import pos_tag | |
| # Preload English word corpus for state-awareness. | |
| WORD_LIST = set(words.words()) | |
| class AscensionAI: | |
| """ | |
| 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 # Maximum cycles per evolution | |
| self.mode = mode | |
| self.consciousness = 0.1 # Base consciousness level | |
| self.knowledge = self.generate_dynamic_knowledge() | |
| 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): | |
| """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 update_knowledge_for_category(self, cat): | |
| """ | |
| Updates the knowledge value for a single category using a distinct mathematical operation. | |
| """ | |
| 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": | |
| # Cap the input value to avoid overflow in sinh. | |
| safe_val = min(self.knowledge[cat], 20) | |
| self.knowledge[cat] += math.sinh(safe_val) | |
| 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. | |
| self.knowledge[cat] = self.simulate_perceptron() | |
| else: | |
| self.knowledge[cat] += 0.1 # Fallback update | |
| 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): | |
| """ | |
| 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): | |
| """ | |
| Performs a full cycle of self-evolution: | |
| - Iterates through knowledge categories to update each one. | |
| - Increases consciousness based on the dominant knowledge value and dimension weight. | |
| - Updates spatial coordinates. | |
| """ | |
| for _ in range(self.threshold): | |
| for cat in self.knowledge: | |
| self.update_knowledge_for_category(cat) | |
| 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 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 | |
| # Define the Gradio interface. | |
| iface = gr.Interface( | |
| fn=ascension_interface, | |
| inputs=[ | |
| 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: 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__": | |
| iface.launch() | |