diff --git a/router.py b/router.py
index 9c8204a..e49152e 100644
--- a/router.py
+++ b/router.py
@@ -270,37 +270,24 @@ def iso8601_ns():
 
 class rechunk:
     def openai_chat_completion2ollama(chunk: dict, stream: bool, start_ts: float):
+        rechunk = {   "model": chunk.model, 
+                    "created_at": iso8601_ns() ,
+                    "done_reason": chunk.choices[0].finish_reason, 
+                    "load_duration": None, 
+                    "prompt_eval_count": None, 
+                    "prompt_eval_duration": None, 
+                    "eval_count": None,
+                    "eval_duration": None,
+                    "eval_count":  (chunk.usage.completion_tokens if chunk.usage is not None else None),
+                    "prompt_eval_count": (chunk.usage.prompt_tokens if chunk.usage is not None else None),
+                    "eval_duration": (int((time.perf_counter() - start_ts) * 1000) if chunk.usage is not None else None),
+                    "response_token/s": (round(chunk.usage.total_tokens / (time.perf_counter() - start_ts), 2) if chunk.usage is not None else None)
+                }
         if stream == True:
-            chunk = {   "model": chunk.model, 
-                        "created_at": iso8601_ns() ,
-                        "done_reason": chunk.choices[0].finish_reason, 
-                        "load_duration": None, 
-                        "prompt_eval_count": None, 
-                        "prompt_eval_duration": None, 
-                        "eval_count": None,
-                        "eval_duration": None,
-                        "message": {"role": chunk.choices[0].delta.role, "content": chunk.choices[0].delta.content, "thinking": None, "images": None, "tool_name": None, "tool_calls": None},
-                        "eval_count":  (chunk.usage.completion_tokens if chunk.usage is not None else None),
-                        "prompt_eval_count": (chunk.usage.prompt_tokens if chunk.usage is not None else None),
-                        "eval_duration": (int((time.perf_counter() - start_ts) * 1000) if chunk.usage is not None else None),
-                        "response_token/s": (round(chunk.usage.total_tokens / (time.perf_counter() - start_ts), 2) if chunk.usage is not None else None)
-                    }
+            rechunk["message"] = {"role": chunk.choices[0].delta.role, "content": chunk.choices[0].delta.content, "thinking": None, "images": None, "tool_name": None, "tool_calls": None}
         else:
-            chunk = {   "model": chunk.model, 
-                        "created_at": iso8601_ns() ,
-                        "done_reason": chunk.choices[0].finish_reason, 
-                        "load_duration": None, 
-                        "prompt_eval_count": None, 
-                        "prompt_eval_duration": None, 
-                        "eval_count": None,
-                        "eval_duration": None,
-                        "message": {"role": chunk.choices[0].message.role, "content": chunk.choices[0].message.content, "thinking": None, "images": None, "tool_name": None, "tool_calls": None},
-                        "eval_count":  (chunk.usage.completion_tokens if chunk.usage is not None else None),
-                        "prompt_eval_count": (chunk.usage.prompt_tokens if chunk.usage is not None else None),
-                        "eval_duration": (int((time.perf_counter() - start_ts) * 1000) if chunk.usage is not None else None),
-                        "response_token/s": (round(chunk.usage.total_tokens / (time.perf_counter() - start_ts), 2) if chunk.usage is not None else None)
-                    }
-        return chunk
+            rechunk["message"] = {"role": chunk.choices[0].message.role, "content": chunk.choices[0].message.content, "thinking": None, "images": None, "tool_name": None, "tool_calls": None}
+        return rechunk
 
 # ------------------------------------------------------------------
 # SSE Helpser
@@ -555,21 +542,19 @@ async def chat_proxy(request: Request):
             if stream == True:
                 async for chunk in async_gen:
                     if is_openai_endpoint:
-                        print(chunk)
                         chunk = rechunk.openai_chat_completion2ollama(chunk, stream, start_ts)
                     # `chunk` can be a dict or a pydantic model – dump to JSON safely
                     if hasattr(chunk, "model_dump_json"):
                         json_line = chunk.model_dump_json()
                     else:
                         json_line = json.dumps(chunk)
-                    print(json_line)
                     yield json_line.encode("utf-8") + b"\n"
             else:
                 if is_openai_endpoint:
                     response = rechunk.openai_chat_completion2ollama(async_gen, stream, start_ts)
                     response = json.dumps(response)
                 else:
-                    repsonse = async_gen.model_dump_json()
+                    response = async_gen.model_dump_json()
                 json_line = (
                     response
                     if hasattr(async_gen, "model_dump_json")