fix: handle tiktoken network errors in offline environments (issue #264)

In air-gapped / offline Docker deployments, tiktoken.get_encoding() tries
to download the encoding file from openaipublic.blob.core.windows.net.
When that request fails it raises a URLError / OSError — not an ImportError
— so the previous except clause silently missed it and the crash surfaced in
the UI.

Widened `except ImportError` to `except Exception` so all failures —
"not installed" and "network unreachable" — fall through to the word-count
fallback (words × 1.3). Added a loguru WARNING so operators can see when
the fallback is active.

TIKTOKEN_CACHE_DIR now reads from the environment with a blank-safe
fallback (`or` guard prevents os.makedirs("") on empty env var). This lets
Docker images redirect the cache to a path outside /app/data/ so user-data
volume mounts cannot shadow the pre-baked encoding.

Both images now pre-download the o200k_base encoding during the builder
stage (internet is available at build time) and copy it into the runtime
image at /app/tiktoken-cache. ENV TIKTOKEN_CACHE_DIR=/app/tiktoken-cache
is set in the runtime stage so no network call is ever needed at runtime.

Added test_token_count_network_error_fallback in tests/test_utils.py:
patches tiktoken.get_encoding with a URLError and asserts token_count()
returns a positive int instead of raising.

Fixes #264

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

open_notebook/utils/token_utils.py

@@ -28,8 +28,15 @@ def token_count(input_string: str) -> int:
         encoding = tiktoken.get_encoding("o200k_base")
         tokens = encoding.encode(input_string)
         return len(tokens)
-    except ImportError:
-        # Fallback: simple word count estimation
+    except Exception:
+        # Fallback: handles ImportError (not installed) AND network errors
+        # (e.g., offline environments that can't download encoding from internet)
+        from loguru import logger
+
+        logger.warning(
+            "tiktoken unavailable (not installed or offline); "
+            "falling back to word-count estimation."
+        )
         return int(len(input_string.split()) * 1.3)