Update app.py

app.py

@@ -100,12 +100,30 @@ if not all(col in questions_df.columns for col in ["Skill", "Question", "Answer"
     exit(1)
 logger.info(f"questions_df loaded with {len(questions_df)} rows. Skills available: {list(questions_df['Skill'].unique())}")
 
-# Load or Initialize Models (lighter model)
-if os.path.exists(UNIVERSAL_MODEL_PATH):
-    universal_model = SentenceTransformer(UNIVERSAL_MODEL_PATH)
-else:
-    universal_model = SentenceTransformer("all-MiniLM-L6-v2-distilled")  # Lighter model
-    universal_model.save(UNIVERSAL_MODEL_PATH)
+# Load or Initialize Models with Fallback
+def load_universal_model():
+    default_model = "all-MiniLM-L6-v2"
+    try:
+        if os.path.exists(UNIVERSAL_MODEL_PATH):
+            logger.info(f"Loading universal model from {UNIVERSAL_MODEL_PATH}")
+            return SentenceTransformer(UNIVERSAL_MODEL_PATH)
+        else:
+            logger.info(f"Loading universal model: all-MiniLM-L6-v2")
+            model = SentenceTransformer("all-MiniLM-L6-v2")
+            model.save(UNIVERSAL_MODEL_PATH)
+            return model
+    except Exception as e:
+        logger.error(f"Failed to load universal model all-MiniLM-L6-v2: {e}. Falling back to default.")
+        try:
+            logger.info(f"Loading fallback model: {default_model}")
+            model = SentenceTransformer(default_model)
+            model.save(UNIVERSAL_MODEL_PATH)
+            return model
+        except Exception as e:
+            logger.error(f"Failed to load fallback model {default_model}: {e}. Exiting.")
+            exit(1)
+
+universal_model = load_universal_model()
 
 if os.path.exists(DETECTOR_MODEL_PATH):
     detector_tokenizer = AutoTokenizer.from_pretrained(DETECTOR_MODEL_PATH)