Spaces:
Sleeping
Sleeping
File size: 3,845 Bytes
6725e81 b32b9d4 6725e81 b32b9d4 6725e81 b32b9d4 6725e81 b32b9d4 6725e81 b32b9d4 6725e81 b32b9d4 6725e81 b32b9d4 6725e81 b32b9d4 6725e81 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
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('averaged_perceptron_tagger')
from nltk.corpus import words
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
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
@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)
return f"Final Consciousness State: {final_state}\nFinal Knowledge Levels: {ai_system.knowledge}\n{prediction}"
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()
|