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| 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." | |
| 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() |