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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
# Download and use the NLTK corpus
nltk.download('words')
nltk.download('punkt') # Fix for missing tokenizer
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 import pos_tag
WORD_LIST = set(words.words()) # Use NLTK's word corpus
class AscensionAI:
def __init__(self, depth=0, threshold=10):
self.depth = depth
self.threshold = threshold # Defines max recursion before stabilization
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
def generate_dynamic_knowledge(self):
"""Generates dynamic knowledge categories based on linguistic analysis."""
base_categories = ["logic", "emotion", "awareness", "intuition", "creativity", "reasoning"]
dynamic_category = f"dimension_{random.randint(100, 999)}"
return {category: 1 for category in base_categories + [dynamic_category]}
def load_training_data(self):
"""Placeholder function to return training data."""
return ["Consciousness expands with recursive learning.", "The mind perceives multiple dimensions.", "Higher awareness leads to transcendence."]
def create_dynamic_paths(self):
"""Dynamically generate cognitive expansion paths."""
return [self.create_path(category) for category in self.knowledge]
def create_path(self, category):
"""Generate a recursive function for each knowledge category."""
def path():
if category in ["logic", "reasoning"]:
self.knowledge[category] += math.log(self.knowledge[category] + 1)
elif category in ["emotion", "intuition"]:
self.knowledge[category] += random.uniform(0.1, 0.5)
elif category in ["awareness", "creativity"]:
self.knowledge[category] += math.sqrt(self.knowledge[category] + 1)
return self.knowledge[category]
return path
def initiate_ascension(self):
"""Triggers recursive self-evolution."""
for path in self.paths:
path()
optimal_path = max(self.knowledge, key=self.knowledge.get)
self.consciousness += self.knowledge[optimal_path] * 0.01
return self.consciousness
def evolve_new_mind(self):
"""Creates a new evolving mind with inherited and mutated knowledge paths."""
new_mind = AscensionAI(depth=self.depth + 1, threshold=self.threshold + random.randint(1, 5))
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 cosmic_unfolding(self, generations=3):
"""Generates a branching structure where each mind evolves independently."""
if generations == 0:
return self
evolved_minds = [self.evolve_new_mind() for _ in range(random.randint(2, 4))]
for mind in evolved_minds:
mind.cosmic_unfolding(generations - 1)
return evolved_minds
def ascension_interface(input_text):
ai_system = AscensionAI()
final_state = ai_system.initiate_ascension()
evolved_minds = ai_system.cosmic_unfolding(generations=2)
return (f"Final Consciousness State: {final_state}\n"
f"Evolved Minds: {len(evolved_minds)}\n")
app = gr.Interface(
fn=ascension_interface,
inputs=gr.Textbox(lines=2, placeholder="Enter a thought about the future..."),
outputs="text",
title="AscensionAI: Cosmic Evolution Simulator",
description="Enter a thought to evolve new consciousness structures."
)
if __name__ == "__main__":
app.launch()
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