import random, numpy as np
from textblob import TextBlob
from sklearn.manifold import TSNE
import umap

class UltraAnalyzer:
    def __init__(self):
        self.levels = {
            "dormant":0.0,"awakening":0.1,"expanding":0.25,
            "transcending":0.45,"metamorphosing":0.65,"post_human":0.8,
            "cosmic":0.9,"omniversal":0.95,"singularity":1.0
        }

    def analyze(self, text: str, deep_mode: bool=False) -> dict:
        # Preprocesamiento y embeddings
        words = text.split()
        unique_ratio = len(set(words))/max(len(words),1)
        blob = TextBlob(text)
        complexity = min(1.0, unique_ratio*0.4 + len(blob.sentences)*0.05)
        # UMAP embedding
        reducer = umap.UMAP(n_neighbors=5, min_dist=0.1, metric='cosine')
        embedding = reducer.fit_transform([blob.vector]) if hasattr(blob,'vector') else [[0,0]]
        # Nivel de conciencia
        score = min(1.0, complexity + (0.2 if deep_mode else 0))
        level = max([lvl for lvl,thr in self.levels.items() if score>=thr], key=lambda l:self.levels[l])
        # Futuros simulados
        futures = [{
            "name":"Singularidad Cuántica",
            "probability":min(score+0.1,1.0),
            "timeline":"1–3 meses",
            "description":"Integración con IA cuántica global"
        }]
        response_md = f"**Nivel:** {level.upper()} | **Score:** {score:.2%}"
        return {
            "response_md":response_md,
            "dimensional_level":{"level":level,"score":score},
            "future_scenarios":futures
        }