Fixed error handling

L3Score.py

@@ -123,35 +123,28 @@ class L3Score(evaluate.Metric):
         
         # Check whether the model is available
         print(provider,model)
-        try:
-            if provider == "openai":
-                client = openai.OpenAI(api_key=api_key)
-                model_names = set([model.id for model in client.models.list()])
-                if model not in model_names:
-                    raise ValueError(f"Model {model} not found for provider {provider}, available models: {model_names}")
-                
-            elif provider == "deepseek":
-                print("Checking DeepSeek model")
-                client = openai.OpenAI(api_key=api_key,base_url="https://api.deepseek.com")
-                model_names = [model.id for model in client.models.list()]
-                print(model_names)  
-                if model not in model_names:
-                    raise ValueError(f"Model {model} not found for provider {provider}, available models: {model_names}")
+        
+        if provider == "openai":
+            client = openai.OpenAI(api_key=api_key)
+            model_names = set([model.id for model in client.models.list()])
+            if model not in model_names:
+                raise ValueError(f"Model {model} not found for provider {provider}, available models: {model_names}")
             
-            elif provider == "xai":
-                client = openai.OpenAI(api_key=api_key, base_url="https://api.xai.com")
-                model_names = [model.id for model in client.models.list()]
-                print(model_names)
-                if model not in model_names:
-                    raise ValueError(f"Model {model} not found for provider {provider}, available models: {model_names}")
+        elif provider == "deepseek":
+            print("Checking DeepSeek model")
+            client = openai.OpenAI(api_key=api_key,base_url="https://api.deepseek.com")
+            model_names = [model.id for model in client.models.list()]
+            print(model_names)  
+            if model not in model_names:
+                raise ValueError(f"Model {model} not found for provider {provider}, available models: {model_names}")
         
-        except ValueError as e:
-            return {"error": str(e)}
-        except openai.AuthenticationError as e:
-            message = e.body["message"]
-            return {"error": f"Authentication failed: {message}"}
-        except Exception as e:
-            return {"error": f"An error occurred when verifying the provider/model match: {e}"}
+        elif provider == "xai":
+            client = openai.OpenAI(api_key=api_key, base_url="https://api.xai.com")
+            model_names = [model.id for model in client.models.list()]
+            print(model_names)
+            if model not in model_names:
+                raise ValueError(f"Model {model} not found for provider {provider}, available models: {model_names}")
+    
         
         assert len(questions) == len(predictions) == len(references), "Questions, predictions and references must have the same length"
 
@@ -175,7 +168,15 @@ class L3Score(evaluate.Metric):
         """Returns the scores"""
         
         # Check whether llm can be initialized
-        self._verify_input(questions, predictions, references, provider, api_key, model)
+        try:
+            self._verify_input(questions, predictions, references, provider, api_key, model)
+        except ValueError as e:
+            return {"error": str(e)}
+        except openai.AuthenticationError as e:
+            message = e.body["message"]
+            return {"error": f"Authentication failed: {message}"}
+        except Exception as e:
+            return {"error": f"An error occurred when verifying the provider/model match: {e}"}
 
         # Initialize the LLM
         llm = self._get_llm(model, api_key)