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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."
@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() |