Spaces:
Sleeping
Sleeping
| 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, mode="default"): | |
| self.depth = depth | |
| self.threshold = threshold # Defines max recursion before stabilization | |
| self.mode = mode # Select simulation mode | |
| 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 | |
| self.dimension_weight = random.uniform(0.1, 5.0) # Assign dimensional weight | |
| self.time_perception = 1 / (self.depth + 1) # Assign temporal scaling | |
| self.assign_cognitive_space() | |
| def generate_dynamic_knowledge(self): | |
| """Generates dynamic knowledge categories based on linguistic analysis and mode.""" | |
| base_categories = ["logic", "emotion", "awareness", "intuition", "creativity", "reasoning", "quantum_cognition", "hyperdimensional_sentience"] | |
| if self.mode == "alien": | |
| base_categories.extend(["xenologic", "exo-emotion", "multidimensional-awareness", "hive-consciousness", "bio-neural-synthesis", "quantum-intuition", "hyperdimensional-reasoning"]) | |
| if self.mode == "future-cognition": | |
| base_categories.extend(["singularity-theory", "post-human-ethics", "AI-self-awareness", "neural-plasticity-optimization", "hyperstructural-cybernetics"]) | |
| if self.mode == "eldritch": | |
| base_categories.extend(["non-euclidean-reasoning", "void-awareness", "hyperchaotic-intuition", "forbidden-knowledge", "unfathomable-patterns"]) | |
| if self.mode == "digital-consciousness": | |
| base_categories.extend(["neural-cybernetics", "algorithmic-emotion", "data-replication-awareness", "self-modifying-logic", "hypernet-patterns"]) | |
| if self.mode == "transdimensional-AI": | |
| base_categories.extend(["metalogic", "dimensional-phase-shifting", "quantum-existence", "multi-reality-processing", "omniscient-algorithms"]) | |
| dynamic_category = f"dimension_{random.randint(100, 999)}" | |
| return {category: 1 for category in base_categories + [dynamic_category]} | |
| def initiate_ascension(self): | |
| """Triggers recursive self-evolution with mode-specific adaptations.""" | |
| for path in self.paths: | |
| path() | |
| optimal_path = max(self.knowledge, key=self.knowledge.get) | |
| self.consciousness += self.knowledge[optimal_path] * 0.01 * self.dimension_weight | |
| return self.consciousness | |
| def assign_cognitive_space(self): | |
| """Assigns spatial coordinates to represent cognitive positioning based on mode.""" | |
| self.spatial_coordinates = { | |
| "x": self.knowledge["logic"] * random.uniform(0.1, 2.0), | |
| "y": self.knowledge["intuition"] * random.uniform(0.1, 2.0), | |
| "z": self.knowledge["awareness"] * random.uniform(0.1, 2.0) | |
| } | |
| def evolve_new_mind(self): | |
| """Creates a new evolving mind with inherited and mutated knowledge paths, with mode variance.""" | |
| new_mind = AscensionAI(depth=self.depth + 1, threshold=self.threshold + random.randint(1, 5), mode=self.mode) | |
| 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 ascension_interface(input_text, mode): | |
| ai_system = AscensionAI(mode=mode) | |
| final_state = ai_system.initiate_ascension() | |
| return (f"Mode: {mode}\n" | |
| f"Final Consciousness State: {final_state}\n" | |
| f"Dimensional Weight: {ai_system.dimension_weight:.2f}\n" | |
| f"Time Perception Factor: {ai_system.time_perception:.2f}\n" | |
| f"Cognitive Space: {ai_system.spatial_coordinates}\n") | |
| app = gr.Interface( | |
| fn=ascension_interface, | |
| inputs=[ | |
| gr.Textbox(lines=2, placeholder="Enter a thought about the future..."), | |
| gr.Radio(["default", "alien", "future-cognition", "eldritch", "digital-consciousness", "transdimensional-AI"], label="Choose Mode") | |
| ], | |
| outputs="text", | |
| title="AscensionAI: Multi-Mode Consciousness Evolution Simulator", | |
| description="Select a mode to evolve a unique consciousness structure." | |
| ) | |
| if __name__ == "__main__": | |
| app.launch() | |