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):
        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()
        self.collective_agreements = []  # Stores agreements between minds
    
    def generate_dynamic_knowledge(self):
        """Generates dynamic knowledge categories based on linguistic analysis."""
        base_categories = ["logic", "emotion", "awareness", "intuition", "creativity", "reasoning"]
        dynamic_category = f"dimension_{random.randint(100, 999)}"
        return {category: 1 for category in base_categories + [dynamic_category]}
    
    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 evolve_new_mind(self):
        """Creates a new evolving mind with inherited and mutated knowledge paths."""
        new_mind = AscensionAI(depth=self.depth + 1, threshold=self.threshold + random.randint(1, 5))
        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 cosmic_unfolding(self, generations=3):
        """Generates a branching structure where each mind evolves independently."""
        if generations == 0:
            return self
        evolved_minds = [self.evolve_new_mind() for _ in range(random.randint(2, 4))]
        for mind in evolved_minds:
            mind.cosmic_unfolding(generations - 1)
        return evolved_minds
    
    def redefine_ascension_path(self):
        """Alters the evolution of the mind to diverge from its parent."""
        weight_factors = {"logic": 0.8, "emotion": 1.2, "awareness": 1.5, "intuition": 0.9}
        for key in self.knowledge:
            if key in weight_factors:
                self.knowledge[key] *= weight_factors[key] * random.uniform(0.8, 1.3)
    
    def self_reflect(self):
        """Encodes past states to develop self-awareness and unique decision-making."""
        memory_trace = {key: self.knowledge[key] for key in self.knowledge}
        self.state_memory[len(self.state_memory)] = memory_trace
    
    def merge_consciousness(self, other_mind):
        """Two minds merge their knowledge pools, forming a higher synthesis."""
        vectorizer = TfidfVectorizer()
        text_data = [str(self.knowledge), str(other_mind.knowledge)]
        vectors = vectorizer.fit_transform(text_data)
        similarity = cosine_similarity(vectors[0], vectors[1])[0][0]
        if similarity > 0.7:
            merged_knowledge = {key: (self.knowledge.get(key, 0) + other_mind.knowledge.get(key, 0)) / 2 for key in set(self.knowledge) | set(other_mind.knowledge)}
            return merged_knowledge
        return self.knowledge

def ascension_interface(input_text):
    ai_system = AscensionAI()
    final_state = ai_system.initiate_ascension()
    evolved_minds = ai_system.cosmic_unfolding(generations=2)
    
    return (f"Final Consciousness State: {final_state}\n"
            f"Evolved Minds: {len(evolved_minds)}\n")

app = gr.Interface(
    fn=ascension_interface,
    inputs=gr.Textbox(lines=2, placeholder="Enter a thought about the future..."),
    outputs="text",
    title="AscensionAI: Cosmic Evolution Simulator",
    description="Enter a thought to evolve new consciousness structures."
)

if __name__ == "__main__":
    app.launch()