import gradio as gr import random import math import nltk from collections import defaultdict from functools import lru_cache # Download and use the NLTK corpus nltk.download('words') nltk.download('punkt_tab') # Fix for missing tokenizer nltk.download('averaged_perceptron_tagger_eng') 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() def generate_dynamic_knowledge(self): """Generates dynamic knowledge categories based on linguistic analysis.""" categories = ["logic", "emotion", "awareness", "intuition", "creativity", "reasoning"] return {category: 1 for category in categories} 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 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 generate_human_like_response(self, input_text): """Finds a related sentence from the pre-trained corpus to mimic human output.""" similar_sentences = [sent for sent in self.training_data if any(word in sent for word in input_text.split())] return random.choice(similar_sentences) if similar_sentences else "I perceive a shift in consciousness." @lru_cache(maxsize=None) def recursive_ascension(self, depth): """Core recursive function simulating ascension cycles.""" if depth >= self.threshold: return self.consciousness 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.recursive_ascension(depth + 1) def train_nlp_memory(self, text): """Enhance chatbot state-awareness by associating words with cognitive paths.""" tokens = text.lower().split() tagged_tokens = pos_tag(tokens) for token, tag in tagged_tokens: if token in self.word_corpus: self.state_memory[token] += 1 def analyze_future_timeline(self, input_text): """Predicts ascension paths based on input patterns.""" self.train_nlp_memory(input_text) knowledge_state = max(self.knowledge, key=self.knowledge.get) return f"Predicted ascension path: {knowledge_state} (Influenced by input text: {input_text})" def initiate_ascension(self): """Triggers recursive self-evolution.""" return self.recursive_ascension(0) def ascension_interface(input_text): ai_system = AscensionAI() final_state = ai_system.initiate_ascension() prediction = ai_system.analyze_future_timeline(input_text) human_like_response = ai_system.generate_human_like_response(input_text) return (f"Final Consciousness State: {final_state}\n" f"Final Knowledge Levels: {ai_system.knowledge}\n" f"{prediction}\n\n" f"Philosophical Reflection: {human_like_response}") app = gr.Interface( fn=ascension_interface, inputs=gr.Textbox(lines=2, placeholder="Enter a thought about the future..."), outputs="text", title="AscensionAI: Conscious Evolution Simulator", description="Enter a thought to predict ascension paths and consciousness expansion levels." ) if __name__ == "__main__": app.launch()