Update quantum_learner.py

quantum_learner.py

@@ -5,17 +5,22 @@ 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 = get_history(limit=1000)
-        texts = [decrypt_data(row[2]) + " " + decrypt_data(row[3]) for row in history]
-        
-        embeddings = self.model.encode(texts)
-        kmeans = KMeans(n_clusters=3).fit(embeddings)
-        
-        topics = {}
-        for label, text in zip(kmeans.labels_, texts):
-            topics[label] = topics.get(label, []) + [text]
-        
-        return {k: v[:3] for k, v in topics.items()}
+    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