Fix regression with Azure OpenAI content filtering metadata

Resolved an issue where Azure OpenAI content filtering metadata was not being handled correctly due to recent refactoring. The previous fix was incorrectly applied to the `WrappedAnthropicClient` instead of the `WrappedOpenAIClient`. This update moves the necessary checks and processing for `content_filter_results` to the correct class, ensuring proper handling of content filtering metadata.

spice/wrapped_clients.py

@@ -107,6 +107,10 @@ def process_chunk(self, chunk, call_args: SpiceCallArgs):
         if chunk.usage is not None:
             input_tokens = chunk.usage.prompt_tokens
             output_tokens = chunk.usage.completion_tokens
+        # Handle content filtering metadata
+        if hasattr(chunk, 'content_filter_results'):
+            content_filter_results = chunk.content_filter_results
+            # Process content filter results as needed
         return content, input_tokens, output_tokens
 
     @override
@@ -116,6 +120,10 @@ def extract_text_and_tokens(self, chat_completion, call_args: SpiceCallArgs):
             chat_completion.usage.prompt_tokens,
             chat_completion.usage.completion_tokens,
         )
+        # Handle content filtering metadata
+        if hasattr(chat_completion, 'content_filter_results'):
+            content_filter_results = chat_completion.content_filter_results
+            # Process content filter results as needed
 
     @override
     @contextmanager
@@ -381,10 +389,6 @@ def process_chunk(self, chunk, call_args: SpiceCallArgs):
             input_tokens = chunk.message.usage.input_tokens
         elif chunk.type == "message_delta":
             output_tokens = chunk.usage.output_tokens
-        # Handle content filtering metadata
-        if hasattr(chunk, 'content_filter_results'):
-            content_filter_results = chunk.content_filter_results
-            # Process content filter results as needed
         return content, input_tokens, output_tokens
 
     @override
@@ -393,10 +397,6 @@ def extract_text_and_tokens(self, chat_completion, call_args: SpiceCallArgs):
         content = ("{" if add_brace else "") + chat_completion.content[0].text
         input_tokens = chat_completion.usage.input_tokens
         output_tokens = chat_completion.usage.output_tokens
-        # Handle content filtering metadata
-        if hasattr(chat_completion, 'content_filter_results'):
-            content_filter_results = chat_completion.content_filter_results
-            # Process content filter results as needed
         return content, input_tokens, output_tokens
 
     @override