fix(truncation): correct message handling and token calc

Updated message truncation logic to correctly return a system message dictionary and adjust token calculations. Improved model encoding fallback strategy to utilize "gpt-4o" instead of "gpt-3.5-turbo" for greater compatibility. This addresses message mishandling and ensures more robust operation.

Resolves the need for better error handling with encoding defaults.

src/gptbot/classes/ai/openai.py

@@ -402,7 +402,7 @@ class OpenAI(BaseAI):
         self.logger.log(f"Prepared messages: {chat_messages}", "debug")
 
         # Truncate messages to fit within the token limit
-        self._truncate(
+        chat_messages = self._truncate(
             messages=chat_messages,
             max_tokens=self.max_tokens - 1,
             system_message=system_message,
@@ -441,7 +441,7 @@ class OpenAI(BaseAI):
             encoding = tiktoken.encoding_for_model(model)
         except Exception:
             # TODO: Handle this more gracefully
-            encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
+            encoding = tiktoken.encoding_for_model("gpt-4o")
 
         total_tokens = 0
 
@@ -458,7 +458,6 @@ class OpenAI(BaseAI):
 
         total_tokens += system_message_tokens
 
-        total_tokens = len(system_message) + 1
         truncated_messages = []
 
         self.logger.log(f"Messages: {messages}", "debug")
@@ -479,7 +478,20 @@ class OpenAI(BaseAI):
             total_tokens += tokens
             truncated_messages.append(message)
 
-        return [truncated_messages[0]] + list(reversed(truncated_messages[1:]))
+        system_message_dict = {
+            "role": "system",
+            "content": (
+                system_message
+                if isinstance(messages[0]["content"], str)
+                else [{"type": "text", "text": system_message}]
+            ),
+        }
+
+        return (
+            system_message_dict
+            + [truncated_messages[0]]
+            + list(reversed(truncated_messages[1:]))
+        )
 
     async def generate_chat_response(
         self,