Update app.py

app.py

@@ -16,6 +16,10 @@ import string
 import spaces
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoConfig
+import torch
+
 
 warnings.filterwarnings("ignore")
 
@@ -75,8 +79,17 @@ class SafeGeocoder:
             self.cache[location] = None
             return None
 
+
+# Replace the model loading section with this:
 def load_model():
     global tokenizer, model
+    try:
+        # First ensure we have the right tokenizer class available
+        from transformers import Qwen2Tokenizer
+    except ImportError:
+        # Fallback to AutoTokenizer if specific import fails
+        pass
+        
     try:
         # Generate a random location and text each time
         random_city = random.choice(["Berlin", "Paris", "London", "Tokyo", "Rome", "Madrid"])
@@ -86,7 +99,22 @@ def load_model():
         
         # Initialize model if not already loaded
         if model is None:
-            tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+            # Load config first to check for tokenizer class
+            config = AutoConfig.from_pretrained(MODEL_NAME, trust_remote_code=True)
+            
+            # Load tokenizer with explicit class if needed
+            if hasattr(config, "tokenizer_class"):
+                tokenizer = AutoTokenizer.from_pretrained(
+                    MODEL_NAME,
+                    trust_remote_code=True,
+                    tokenizer_class=config.tokenizer_class
+                )
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(
+                    MODEL_NAME,
+                    trust_remote_code=True
+                )
+            
             model = AutoModelForCausalLM.from_pretrained(
                 MODEL_NAME,
                 torch_dtype=TORCH_DTYPE,
@@ -107,7 +135,6 @@ def load_model():
             
     except Exception as e:
         return f"❌ Fehler beim Laden des Modells: {str(e)}"
-
 @spaces.GPU
 def extract_info(template, text):
     global tokenizer, model