from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
from memory_utils import get_history, decrypt_data
import numpy as np

class QuantumLearner:
    def __init__(self):
        # Koristimo lightweight multi-jezički model
        self.model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
        self.model.max_seq_length = 256  # Optimizacija za performanse
    
    def analyze_conversations(self, history_limit=1000):
        """Analizira razgovore i pronalazi ključne teme"""
        try:
            history = get_history(limit=history_limit)
            if not history:
                return {"info": "Nema dostupne povijesti za analizu"}
            
            # Dekriptiraj i kombinuj poruke
            texts = [
                f"{decrypt_data(row[2])} → {decrypt_data(row[3])}" 
                for row in history
                if len(row) >= 4  # Zaštita od nepotpunih podataka
            ]
            
            # Vektorizacija teksta