appversion3.py

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()