Update app.py

app.py

@@ -163,24 +163,48 @@ def load_llm_model(model_id, hf_token):
 
 # --- Load all assets on startup ---
 print("Loading assets...")
-# Removed nlp = load_spacy_model()
+# Removed nlp = load_spacy_model() # Keep this line commented out if you removed spaCy
 embedder = load_sentence_transformer()
+print(f"Embedder loaded: {embedder is not None}") # Add this print
+
 data, descriptions, _ = load_google_sheet_data(SHEET_ID, GOOGLE_SERVICE_ACCOUNT_KEY_BASE64)
+print(f"Google Sheet data loaded: {len(data)} rows") # Add this print
+print(f"Google Sheet descriptions loaded: {len(descriptions)} items") # Add this print
 
 if embedder and descriptions:
     print("Encoding Google Sheet descriptions...")
     try:
         embeddings = embedder.encode(descriptions, convert_to_tensor=True)
         print("Encoding complete.")
+        print(f"Embeddings shape: {embeddings.shape}") # Add this print
     except Exception as e:
         print(f"Error during embedding: {e}")
-        embeddings = torch.tensor([])
+        embeddings = torch.tensor([]) # Ensure embeddings is an empty tensor on error
 else:
      print("Skipping embedding due to missing embedder or descriptions.")
-     embeddings = torch.tensor([])
+     embeddings = torch.tensor([]) # Ensure embeddings is an empty tensor when skipped
+     print(f"Embeddings tensor after skip: {embeddings.shape}") # Should print torch.Size([])
 
 model, tokenizer = load_llm_model(model_id, HF_TOKEN)
+print(f"LLM Model loaded: {model is not None}") # Add this print
+print(f"LLM Tokenizer loaded: {tokenizer is not None}") # Add this print
 
+# Check if essential components loaded
+# This block provides a summary if anything failed during loading
+if not model or not tokenizer or not embedder or embeddings is None or embeddings.numel() == 0 or not data:
+    print("\nERROR: Essential components failed to load. The application may not function correctly.")
+    if not model: print("- LLM Model failed to load.")
+    if not tokenizer: print("- LLM Tokenizer failed to load.")
+    if not embedder: print("- Sentence Embedder failed to load.")
+    # Check if embeddings is not None before accessing numel()
+    if embeddings is None or embeddings.numel() == 0: print("- Embeddings are empty or None.")
+    if not data: print("- Google Sheet Data is empty.")
+    # Descriptions being empty is implicitly covered by data being empty in this context
+    # if not descriptions: print("- Google Sheet Descriptions are empty.")
+    # Removed spaCy error message
+    # Continue, but the main inference function will need checks (already handled by the check at start of respond)
+else:
+     print("\nAll essential components loaded successfully.") # Add this print
 # Check if essential components loaded (Removed nlp from this check)
 if not model or not tokenizer or not embedder:
     print("\nERROR: Essential components failed to load. The application may not function correctly.")