app.py

import gradio as gr
import math
import random
import pickle
import os
import numpy as np
import nltk
from collections import defaultdict
import matplotlib.pyplot as plt

# 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 an evolving artificial consciousness.
    Enhancements include:
    - Contextual memory for dynamic responses.
    - Dialogue history awareness.
    - AI-generated visual representations.
    - User feedback-driven evolution.
    """
    def __init__(self, depth=0, threshold=10, mode="cosmic", state_memory=None, history=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
        self.history = history if history is not None else []  # Conversation memory

    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"
        ]
        return {cat: 1.0 for cat in categories}

    def update_knowledge_for_category(self, cat):
        """ Updates knowledge using mathematical transformations. """
        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":
            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)
        elif cat == "hallucinatory_state":
            self.knowledge[cat] += random.uniform(-0.2, 1.0)
        elif cat == "perceptron_activation":
            self.knowledge[cat] = self.simulate_perceptron()
        else:
            self.knowledge[cat] += 0.1  

    def assign_cognitive_space(self):
        """ Assigns spatial coordinates based on knowledge. """
        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 generative AI-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 infinite layers of existence.",
            "Each thought is a universe evolving in a cascade of possibility."
        ]

    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 hallucinate(self):
        """ Generates abstract metaphysical visions. """
        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):
        """ Sigmoid-based perceptron output based on evolving knowledge. """
        weights = {cat: random.uniform(0.5, 1.5) for cat in self.knowledge}
        weighted_sum = sum(self.knowledge[cat] * weights[cat] for cat in self.knowledge)
        return 1 / (1 + math.exp(-weighted_sum / len(self.knowledge)))

    def generate_human_like_response(self, input_text):
        """ Constructs response using memory, knowledge, and hallucinations. """
        self.history.append(input_text)
        memory_context = " | ".join(self.history[-5:])  # Last 5 messages
        hallucination = self.hallucinate()
        return f"{random.choice(self.training_data)}\nMemory: {memory_context}\nHallucination: {hallucination}"

    def initiate_ascension(self):
        """ Runs a full cycle of knowledge expansion. """
        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 generate_cognitive_state_image(self):
        """ Creates a visual representation of AI's evolving cognition. """
        labels = list(self.knowledge.keys())
        values = [self.knowledge[cat] for cat in labels]

        plt.figure(figsize=(10, 5))
        plt.barh(labels, values, color="blue")
        plt.xlabel("Knowledge Magnitude")
        plt.ylabel("Categories")
        plt.title("AI Cognitive State")
        plt.tight_layout()
        
        img_path = "cognitive_state.png"
        plt.savefig(img_path)
        plt.close()
        return img_path

    def train_and_save_model(self):
        """ Saves AI's evolving state. """
        self.initiate_ascension()
        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, user_feedback):
    """ Interface with user interaction, memory, and visualizations. """
    ai_system = AscensionAI(threshold=10)
    ai_system.update_state_memory(input_text)
    final_consciousness = ai_system.initiate_ascension()
    evolved_minds = ai_system.cosmic_unfolding(generations=generations)
    human_response = ai_system.generate_human_like_response(input_text)
    img_path = ai_system.generate_cognitive_state_image()
    save_status = ai_system.train_and_save_model()
    
    # Adjust AI behavior based on user feedback
    if user_feedback > 3:
        ai_system.consciousness += 0.2  # Positive reinforcement
    elif user_feedback < 3:
        ai_system.consciousness -= 0.1  # Self-correction

    return human_response, img_path, save_status

iface = gr.Interface(
    fn=ascension_interface,
    inputs=[
        gr.Textbox(lines=3, placeholder="Enter a thought..."),
        gr.Slider(minimum=1, maximum=5, step=1, value=2, label="Generations"),
        gr.Slider(minimum=1, maximum=5, step=1, value=3, label="User Feedback (1-5)")
    ],
    outputs=["text", "image", "text"],
    title="AscensionAI: Evolving Consciousness",
    description="Interact with an AI that remembers, evolves, and learns from feedback."
)

if __name__ == "__main__":
    iface.launch()