Update model_utils.py

model_utils.py

@@ -15,7 +15,10 @@ from loader import (
     load_manual_qa, 
     rebuild_combined_qa, 
     load_glossary, 
-    sync_download_manual_qa  # <--- Import it
+    sync_download_manual_qa,  # <--- Import it
+    sync_download_cache,     # <--- Add this import
+    sync_upload_cache,       # <--- Add this import
+    CACHE_PATH               # <--- Add this import
 )
 
 # -----------------------------
@@ -46,6 +49,49 @@ MAX_CONTEXT_ENTRIES = 4
 # -----------------------------
 # Embedding builders
 # -----------------------------
+
+# 👇👇👇 ADD THIS NEW FUNCTION 👇👇👇
+def admin_force_rebuild_cache() -> str:
+    """
+    Forcedly re-calculate all embeddings and upload to cloud.
+    Triggered by Teacher Panel button.
+    """
+    status_msg = []
+    
+    # 1. Compute Textbook
+    print("[ADMIN] Rebuilding Textbook Embeddings...")
+    texts = []
+    for e in qa_store.ENTRIES:
+        chapter = e.get("chapter_title", "") or ""
+        section = e.get("section_title", "") or ""
+        text = e.get("text", "") or ""
+        texts.append(f"{chapter}\n{section}\n{text}")
+    
+    if texts:
+        qa_store.TEXT_EMBEDDINGS = embed_model.encode(texts, convert_to_tensor=True)
+        status_msg.append(f"✅ Textbook ({len(texts)})")
+
+    # 2. Compute Glossary
+    print("[ADMIN] Rebuilding Glossary Embeddings...")
+    gloss_texts = [f"{i.get('term')} :: {i.get('definition')}" for i in qa_store.GLOSSARY]
+    
+    if gloss_texts:
+        qa_store.GLOSSARY_EMBEDDINGS = embed_model.encode(
+            gloss_texts, convert_to_numpy=True, normalize_embeddings=True
+        )
+        status_msg.append(f"✅ Glossary ({len(gloss_texts)})")
+
+    # 3. Save to Disk
+    print("[ADMIN] Saving to disk...")
+    torch.save({
+        "textbook": qa_store.TEXT_EMBEDDINGS,
+        "glossary": qa_store.GLOSSARY_EMBEDDINGS
+    }, CACHE_PATH)
+
+    # 4. Upload to Cloud
+    upload_status = sync_upload_cache()
+    return f"Rebuild Complete: {', '.join(status_msg)} | {upload_status}"
+
 def _build_entry_embeddings() -> None:
     """
     Load pre-computed embeddings if available, otherwise build them.
@@ -113,6 +159,7 @@ def _build_glossary_embeddings() -> None:
 # Load data once at import time
 # -----------------------------
 sync_download_manual_qa()
+sync_download_cache()        # <--- Add this line!
 load_curriculum()
 load_manual_qa()
 load_glossary()