diff --git a/src/beaky/image_classifier/classifier.py b/src/beaky/image_classifier/classifier.py
index a1bc8d4..5db2579 100644
--- a/src/beaky/image_classifier/classifier.py
+++ b/src/beaky/image_classifier/classifier.py
@@ -1,7 +1,9 @@
 import datetime
+import logging
+import re
 from pathlib import Path
 
-import pytesseract
+from pytesseract import pytesseract
 
 from beaky.datamodels.ticket import (
     Advance,
@@ -23,7 +25,7 @@ def img_to_text(path: str) -> str:
     Bypasses PIL and lets Tesseract read the file directly.
     """
     try:
-        text = pytesseract.image_to_string(path)
+        text = pytesseract.image_to_string(path, lang="ces")
         return text.strip()
     except pytesseract.TesseractNotFoundError:
         print("Error: Tesseract executable not found on your system.")
@@ -37,6 +39,7 @@ def classify(text: str) -> Bet:
     """Given text extracted from an image and a date, return a Bet object that is
     relevant to that text."""
 
+    logger = logging.getLogger(__name__)
     if not text:
         return UnknownTicket(
             ticketType=BetType.UNKNOWN,
@@ -149,7 +152,19 @@ def img_classify(path: str, ticket_id: int) -> Ticket:
             print(extracted_text)
 
             # 2. Classify based on the extracted text (called separately)
-            result = classify(extracted_text)
+            try:
+                result = classify(extracted_text)
+            except Exception as exc:  # pragma: no cover - defensive fallback
+                # Ensure result is always defined so downstream code cannot reference an unbound name
+                print(f"classify() raised an exception: {exc}")
+                result = UnknownTicket(
+                    ticketType=BetType.UNKNOWN,
+                    team1Name="N/A",
+                    team2Name="N/A",
+                    league="N/A",
+                    raw_text=extracted_text,
+                    date=datetime.datetime.now(),
+                )
 
             # 3. Add the resulting tickets to our main list
             # Support classifier returning either a single Bet or a list of Bet