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()