Create quantum_learner.py

quantum_learner.py

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+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):
+        self.model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
+    
+    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()}