Update app.py

app.py

@@ -970,223 +970,220 @@ def handsome_chat_completions():
     }
 
     if model_name in image_models:
-       user_content = ""
-       messages = data.get("messages", [])
-       for message in messages:
-           if message["role"] == "user":
-               if isinstance(message["content"], str):
-                   user_content += message["content"] + " "
-               elif isinstance(message["content"], list):
-                   for item in message["content"]:
-                       if (
-                           isinstance(item, dict) and
-                           item.get("type") == "text"
-                       ):
-                           user_content += (
-                               item.get("text", "") +
-                               " "
-                           )
-       user_content = user_content.strip()
-
-       siliconflow_data = {
+        user_content = ""
+        messages = data.get("messages", [])
+        for message in messages:
+            if message["role"] == "user":
+                if isinstance(message["content"], str):
+                    user_content += message["content"] + " "
+                elif isinstance(message["content"], list):
+                    for item in message["content"]:
+                        if (
+                            isinstance(item, dict) and
+                            item.get("type") == "text"
+                        ):
+                            user_content += (
+                                item.get("text", "") +
+                                " "
+                            )
+        user_content = user_content.strip()
+
+        siliconflow_data = {
             "model": model_name,
             "prompt": user_content,
-          
         }
-       if model_name == "black-forest-labs/FLUX.1-pro":
-           siliconflow_data["width"] = data.get("width", 1024)
-           siliconflow_data["height"] = data.get("height", 768)
-           siliconflow_data["prompt_upsampling"] = data.get("prompt_upsampling", False)
-           siliconflow_data["image_prompt"] = data.get("image_prompt")
-           siliconflow_data["steps"] = data.get("steps", 20)
-           siliconflow_data["guidance"] = data.get("guidance", 3)
-           siliconflow_data["safety_tolerance"] = data.get("safety_tolerance", 2)
-           siliconflow_data["interval"] = data.get("interval", 2)
-           siliconflow_data["output_format"] = data.get("output_format", "png")
-           seed = data.get("seed")
-           if isinstance(seed, int) and 0 < seed < 9999999999:
+        if model_name == "black-forest-labs/FLUX.1-pro":
+            siliconflow_data["width"] = data.get("width", 1024)
+            siliconflow_data["height"] = data.get("height", 768)
+            siliconflow_data["prompt_upsampling"] = data.get("prompt_upsampling", False)
+            siliconflow_data["image_prompt"] = data.get("image_prompt")
+            siliconflow_data["steps"] = data.get("steps", 20)
+            siliconflow_data["guidance"] = data.get("guidance", 3)
+            siliconflow_data["safety_tolerance"] = data.get("safety_tolerance", 2)
+            siliconflow_data["interval"] = data.get("interval", 2)
+            siliconflow_data["output_format"] = data.get("output_format", "png")
+            seed = data.get("seed")
+            if isinstance(seed, int) and 0 < seed < 9999999999:
                siliconflow_data["seed"] = seed
-           if siliconflow_data["width"] < 256 or siliconflow_data["width"] > 1440 or siliconflow_data["width"] % 32 != 0:
-               siliconflow_data["width"] = 1024
-           if siliconflow_data["height"] < 256 or siliconflow_data["height"] > 1440 or siliconflow_data["height"] % 32 != 0:
-                siliconflow_data["height"] = 768
-
-           if siliconflow_data["steps"] < 1 or siliconflow_data["steps"] > 50:
+            if siliconflow_data["width"] < 256 or siliconflow_data["width"] > 1440 or siliconflow_data["width"] % 32 != 0:
+                 siliconflow_data["width"] = 1024
+            if siliconflow_data["height"] < 256 or siliconflow_data["height"] > 1440 or siliconflow_data["height"] % 32 != 0:
+                 siliconflow_data["height"] = 768
+            if siliconflow_data["steps"] < 1 or siliconflow_data["steps"] > 50:
                siliconflow_data["steps"] = 20
-           if siliconflow_data["guidance"] < 1.5 or siliconflow_data["guidance"] > 5:
+            if siliconflow_data["guidance"] < 1.5 or siliconflow_data["guidance"] > 5:
                 siliconflow_data["guidance"] = 3
-           if siliconflow_data["safety_tolerance"] < 0 or siliconflow_data["safety_tolerance"] > 6:
-               siliconflow_data["safety_tolerance"] = 2
-           if siliconflow_data["interval"] < 1 or siliconflow_data["interval"] > 4 :
+            if siliconflow_data["safety_tolerance"] < 0 or siliconflow_data["safety_tolerance"] > 6:
+                siliconflow_data["safety_tolerance"] = 2
+            if siliconflow_data["interval"] < 1 or siliconflow_data["interval"] > 4 :
                 siliconflow_data["interval"] = 2
-       else:
-           siliconflow_data["image_size"] = "1024x1024"
-           siliconflow_data["batch_size"] = 1
-           siliconflow_data["num_inference_steps"] = 20
-           siliconflow_data["guidance_scale"] = 7.5
-           siliconflow_data["prompt_enhancement"] = False
-     
-           if data.get("size"):
-               siliconflow_data["image_size"] = data.get("size")
-           if data.get("n"):
-               siliconflow_data["batch_size"] = data.get("n")
-           if data.get("steps"):
-               siliconflow_data["num_inference_steps"] = data.get("steps")
-           if data.get("guidance_scale"):
-              siliconflow_data["guidance_scale"] = data.get("guidance_scale")
-           if data.get("negative_prompt"):
-              siliconflow_data["negative_prompt"] = data.get("negative_prompt")
-           if data.get("seed"):
-              siliconflow_data["seed"] = data.get("seed")
-           if data.get("prompt_enhancement"):
-              siliconflow_data["prompt_enhancement"] = data.get("prompt_enhancement")
-
-           if siliconflow_data["batch_size"] < 1:
+        else:
+            siliconflow_data["image_size"] = "1024x1024"
+            siliconflow_data["batch_size"] = 1
+            siliconflow_data["num_inference_steps"] = 20
+            siliconflow_data["guidance_scale"] = 7.5
+            siliconflow_data["prompt_enhancement"] = False
+      
+            if data.get("size"):
+                siliconflow_data["image_size"] = data.get("size")
+            if data.get("n"):
+                siliconflow_data["batch_size"] = data.get("n")
+            if data.get("steps"):
+                siliconflow_data["num_inference_steps"] = data.get("steps")
+            if data.get("guidance_scale"):
+                siliconflow_data["guidance_scale"] = data.get("guidance_scale")
+            if data.get("negative_prompt"):
+                siliconflow_data["negative_prompt"] = data.get("negative_prompt")
+            if data.get("seed"):
+                siliconflow_data["seed"] = data.get("seed")
+            if data.get("prompt_enhancement"):
+                siliconflow_data["prompt_enhancement"] = data.get("prompt_enhancement")
+            if siliconflow_data["batch_size"] < 1:
                siliconflow_data["batch_size"] = 1
-           if siliconflow_data["batch_size"] > 4:
+            if siliconflow_data["batch_size"] > 4:
                siliconflow_data["batch_size"] = 4
 
-           if siliconflow_data["num_inference_steps"] < 1:
-               siliconflow_data["num_inference_steps"] = 1
-           if siliconflow_data["num_inference_steps"] > 50:
+            if siliconflow_data["num_inference_steps"] < 1:
+                siliconflow_data["num_inference_steps"] = 1
+            if siliconflow_data["num_inference_steps"] > 50:
                siliconflow_data["num_inference_steps"] = 50
-             
-           if siliconflow_data["guidance_scale"] < 0:
+           
+            if siliconflow_data["guidance_scale"] < 0:
                siliconflow_data["guidance_scale"] = 0
-           if siliconflow_data["guidance_scale"] > 100:
+            if siliconflow_data["guidance_scale"] > 100:
                siliconflow_data["guidance_scale"] = 100
          
-           if siliconflow_data["image_size"] not in ["1024x1024", "512x1024", "768x512", "768x1024", "1024x576", "576x1024", "960x1280", "720x1440", "720x1280"]:
-               siliconflow_data["image_size"] = "1024x1024"
-       try:
+            if siliconflow_data["image_size"] not in ["1024x1024", "512x1024", "768x512", "768x1024", "1024x576", "576x1024", "960x1280", "720x1440", "720x1280"]:
+                siliconflow_data["image_size"] = "1024x1024"
+
+        try:
            start_time = time.time()
            response = requests.post(
-               "https://api-st.siliconflow.cn/v1/images/generations",
-               headers=headers,
-               json=siliconflow_data,
-               timeout=120,
-               stream=data.get("stream", False)
+                "https://api-st.siliconflow.cn/v1/images/generations",
+                headers=headers,
+                json=siliconflow_data,
+                timeout=120,
+                stream=data.get("stream", False)
            )
-        
            if response.status_code == 429:
-               return jsonify(response.json()), 429
+              return jsonify(response.json()), 429
 
            if data.get("stream", False):
-               def generate():
-                   first_chunk_time = None
-                   full_response_content = ""
-                   try:
-                       response.raise_for_status()
-                       end_time = time.time()
-                       response_json = response.json()
-                       total_time = end_time - start_time
+                def generate():
+                  first_chunk_time = None
+                  full_response_content = ""
+                  try:
+                    response.raise_for_status()
+                    end_time = time.time()
+                    response_json = response.json()
+                    total_time = end_time - start_time
                  
-                       images = response_json.get("images", [])
-                     
-                       image_url = ""
-                       if images and isinstance(images[0], dict) and "url" in images[0]:
-                           image_url = images[0]["url"]
-                           logging.info(f"Extracted image URL: {image_url}")
-                       elif images and isinstance(images[0], str):
-                           image_url = images[0]
-                           logging.info(f"Extracted image URL: {image_url}")
-                     
-                       markdown_image_link = f"![image]({image_url})"
-                       if image_url:
-                           chunk_data = {
-                               "id": f"chatcmpl-{uuid.uuid4()}",
-                               "object": "chat.completion.chunk",
-                               "created": int(time.time()),
-                               "model": model_name,
-                               "choices": [
-                                   {
-                                       "index": 0,
-                                       "delta": {
-                                           "role": "assistant",
-                                           "content": markdown_image_link
-                                       },
-                                       "finish_reason": None
-                                   }
-                               ]
-                           }
-                           yield f"data: {json.dumps(chunk_data)}\n\n".encode('utf-8')
-                           full_response_content = markdown_image_link
-                       else:
-                           chunk_data = {
-                               "id": f"chatcmpl-{uuid.uuid4()}",
-                               "object": "chat.completion.chunk",
-                               "created": int(time.time()),
-                               "model": model_name,
-                               "choices": [
-                                   {
-                                       "index": 0,
-                                       "delta": {
-                                           "role": "assistant",
-                                           "content": "Failed to generate image"
-                                       },
-                                       "finish_reason": None
-                                   }
-                               ]
-                           }
-                           yield f"data: {json.dumps(chunk_data)}\n\n".encode('utf-8')
-                           full_response_content = "Failed to generate image"
+                    images = response_json.get("images", [])
+                   
+                    image_url = ""
+                    if images and isinstance(images[0], dict) and "url" in images[0]:
+                        image_url = images[0]["url"]
+                        logging.info(f"Extracted image URL: {image_url}")
+                    elif images and isinstance(images[0], str):
+                        image_url = images[0]
+                        logging.info(f"Extracted image URL: {image_url}")
                      
-                       end_chunk_data = {
-                           "id": f"chatcmpl-{uuid.uuid4()}",
-                           "object": "chat.completion.chunk",
-                           "created": int(time.time()),
-                           "model": model_name,
-                           "choices": [
-                               {
-                                   "index": 0,
-                                   "delta": {},
-                                   "finish_reason": "stop"
-                               }
-                           ]
-                       }
-                       yield f"data: {json.dumps(end_chunk_data)}\n\n".encode('utf-8')
-                       with data_lock:
-                           request_timestamps.append(time.time())
-                           token_counts.append(0)
-                   except requests.exceptions.RequestException as e:
-                       logging.error(f"请求转发异常: {e}")
-                       error_chunk_data = {
-                           "id": f"chatcmpl-{uuid.uuid4()}",
-                           "object": "chat.completion.chunk",
-                           "created": int(time.time()),
-                           "model": model_name,
-                           "choices": [
-                               {
-                                   "index": 0,
-                                   "delta": {
-                                       "role": "assistant",
-                                       "content": f"Error: {str(e)}"
-                                   },
-                                   "finish_reason": None
-                               }
-                           ]
-                       }
-                       yield f"data: {json.dumps(error_chunk_data)}\n\n".encode('utf-8')
-                       end_chunk_data = {
-                               "id": f"chatcmpl-{uuid.uuid4()}",
-                               "object": "chat.completion.chunk",
-                               "created": int(time.time()),
-                               "model": model_name,
-                               "choices": [
-                                   {
-                                       "index": 0,
-                                       "delta": {},
-                                       "finish_reason": "stop"
-                                   }
-                               ]
-                           }
-                       yield f"data: {json.dumps(end_chunk_data)}\n\n".encode('utf-8')
-                   logging.info(
-                       f"使用的key: {api_key}, "
-                       f"使用的模型: {model_name}"
-                   )
-                   yield "data: [DONE]\n\n".encode('utf-8')
-               return Response(stream_with_context(generate()), content_type='text/event-stream')
+                    markdown_image_link = f"![image]({image_url})"
+                    if image_url:
+                        chunk_data = {
+                            "id": f"chatcmpl-{uuid.uuid4()}",
+                            "object": "chat.completion.chunk",
+                            "created": int(time.time()),
+                            "model": model_name,
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {
+                                        "role": "assistant",
+                                        "content": markdown_image_link
+                                    },
+                                    "finish_reason": None
+                                }
+                            ]
+                        }
+                        yield f"data: {json.dumps(chunk_data)}\n\n".encode('utf-8')
+                        full_response_content = markdown_image_link
+                    else:
+                        chunk_data = {
+                            "id": f"chatcmpl-{uuid.uuid4()}",
+                            "object": "chat.completion.chunk",
+                            "created": int(time.time()),
+                            "model": model_name,
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {
+                                        "role": "assistant",
+                                        "content": "Failed to generate image"
+                                    },
+                                    "finish_reason": None
+                                }
+                            ]
+                        }
+                        yield f"data: {json.dumps(chunk_data)}\n\n".encode('utf-8')
+                        full_response_content = "Failed to generate image"
+                   
+                    end_chunk_data = {
+                        "id": f"chatcmpl-{uuid.uuid4()}",
+                        "object": "chat.completion.chunk",
+                        "created": int(time.time()),
+                        "model": model_name,
+                        "choices": [
+                            {
+                                "index": 0,
+                                "delta": {},
+                                "finish_reason": "stop"
+                            }
+                        ]
+                    }
+                    yield f"data: {json.dumps(end_chunk_data)}\n\n".encode('utf-8')
+                    with data_lock:
+                        request_timestamps.append(time.time())
+                        token_counts.append(0)
+                  except requests.exceptions.RequestException as e:
+                      logging.error(f"请求转发异常: {e}")
+                      error_chunk_data = {
+                            "id": f"chatcmpl-{uuid.uuid4()}",
+                            "object": "chat.completion.chunk",
+                            "created": int(time.time()),
+                            "model": model_name,
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {
+                                        "role": "assistant",
+                                        "content": f"Error: {str(e)}"
+                                    },
+                                    "finish_reason": None
+                                }
+                            ]
+                        }
+                      yield f"data: {json.dumps(error_chunk_data)}\n\n".encode('utf-8')
+                      end_chunk_data = {
+                            "id": f"chatcmpl-{uuid.uuid4()}",
+                            "object": "chat.completion.chunk",
+                            "created": int(time.time()),
+                            "model": model_name,
+                            "choices": [
+                                {
+                                    "index": 0,
+                                    "delta": {},
+                                    "finish_reason": "stop"
+                                }
+                            ]
+                        }
+                      yield f"data: {json.dumps(end_chunk_data)}\n\n".encode('utf-8')
+                  logging.info(
+                     f"使用的key: {api_key}, "
+                      f"使用的模型: {model_name}"
+                  )
+                  yield "data: [DONE]\n\n".encode('utf-8')
+                return Response(stream_with_context(generate()), content_type='text/event-stream')
 
            else:
                response.raise_for_status()
@@ -1245,22 +1242,22 @@ def handsome_chat_completions():
                    }
 
                logging.info(
-                   f"使用的key: {api_key}, "
-                   f"总共用时: {total_time:.4f}秒, "
-                   f"使用的模型: {model_name}"
+                    f"使用的key: {api_key}, "
+                    f"总共用时: {total_time:.4f}秒, "
+                    f"使用的模型: {model_name}"
                )
                with data_lock:
-                   request_timestamps.append(time.time())
-                   token_counts.append(0)
+                    request_timestamps.append(time.time())
+                    token_counts.append(0)
                return jsonify(response_data)
 
-       except requests.exceptions.RequestException as e:
+        except requests.exceptions.RequestException as e:
            logging.error(f"请求转发异常: {e}")
            return jsonify({"error": str(e)}), 500
     else:
         tools = data.get("tools")
         tool_choice = data.get("tool_choice")
-        
+
         siliconflow_data = {
             "model": model_name,
             "messages": data.get("messages", []),
@@ -1312,15 +1309,16 @@ def handsome_chat_completions():
                     prompt_tokens = 0
                     completion_tokens = 0
                     response_content = ""
+                    function_call = None
                     tool_calls = []
+
                     for line in full_response_content.splitlines():
                         if line.startswith("data:"):
-                            line = line[5:].strip()
-                            if line == "[DONE]":
+                           line = line[5:].strip()
+                           if line == "[DONE]":
                                 continue
-                            try:
+                           try:
                                 response_json = json.loads(line)
-
                                 if (
                                     "usage" in response_json and
                                     "completion_tokens" in response_json["usage"]
@@ -1347,7 +1345,6 @@ def handsome_chat_completions():
                                         if "tool_calls" in message:
                                             tool_calls.extend(message["tool_calls"])
 
-
                                 if (
                                     "usage" in response_json and
                                     "prompt_tokens" in response_json["usage"]
@@ -1356,23 +1353,24 @@ def handsome_chat_completions():
                                         "usage"
                                     ]["prompt_tokens"]
 
-                            except (
+
+                           except (
                                 KeyError,
                                 ValueError,
                                 IndexError
-                            ) as e:
-                                logging.error(
+                           ) as e:
+                               logging.error(
                                     f"解析流式响应单行 JSON 失败: {e}, "
                                     f"行内容: {line}"
-                                )
+                               )
 
                     user_content = ""
                     messages = data.get("messages", [])
                     for message in messages:
-                        if message["role"] == "user":
-                            if isinstance(message["content"], str):
+                       if message["role"] == "user":
+                          if isinstance(message["content"], str):
                                 user_content += message["content"] + " "
-                            elif isinstance(message["content"], list):
+                          elif isinstance(message["content"], list):
                                 for item in message["content"]:
                                     if (
                                         isinstance(item, dict) and
@@ -1390,7 +1388,7 @@ def handsome_chat_completions():
                     response_content_replaced = response_content.replace(
                         '\n', '\\n'
                     ).replace('\r', '\\n')
-
+                    
                     log_message = (
                         f"使用的key: {api_key}, "
                         f"提示token: {prompt_tokens}, "
@@ -1401,17 +1399,16 @@ def handsome_chat_completions():
                         f"用户的内容: {user_content_replaced}, "
                         f"输出的内容: {response_content_replaced}"
                     )
+                  
                     if tool_calls:
-                        log_message += f", tool_calls: {tool_calls}"
-
+                       log_message += f", tool_calls: {tool_calls}"
+                    
                     logging.info(log_message)
 
                     with data_lock:
                         request_timestamps.append(time.time())
                         token_counts.append(prompt_tokens+completion_tokens)
-                    
-                    
-                    
+                                            
                     # 构造 OpenAI 格式的响应数据
                     response_data = {
                         "id": f"chatcmpl-{uuid.uuid4()}",
@@ -1428,13 +1425,31 @@ def handsome_chat_completions():
                             }
                         ]
                     }
-
-                    if response_content:
-                        response_data["choices"][0]["delta"]["content"] = response_content
                     
                     if tool_calls:
-                        response_data["choices"][0]["delta"]["tool_calls"] = tool_calls
-                        
+                         if isinstance(tool_calls, list) and len(tool_calls) > 0:
+                                 
+                             first_tool_call = tool_calls[0]
+                             if isinstance(first_tool_call, dict) and "function" in first_tool_call:
+                                 function_call_data = first_tool_call.get("function")
+                                 if isinstance(function_call_data, dict) and "name" in function_call_data and "arguments" in function_call_data:
+                                        function_call = {
+                                                "name": function_call_data["name"],
+                                                "arguments": json.dumps(function_call_data["arguments"]) if isinstance(function_call_data.get("arguments"), dict) else function_call_data["arguments"]
+                                         }
+                                        response_data["choices"][0]["delta"]["function_call"] = function_call
+                                        response_data["choices"][0]["delta"]["content"] = None
+                                        response_data["choices"][0]["finish_reason"] = "function_call"
+                                 else:
+                                        response_data["choices"][0]["delta"]["tool_calls"] = tool_calls
+                                        response_data["choices"][0]["delta"]["content"] = None
+                         else:
+                                response_data["choices"][0]["delta"]["tool_calls"] = tool_calls
+                                response_data["choices"][0]["delta"]["content"] = None
+                    elif response_content:
+                           response_data["choices"][0]["delta"]["content"] = response_content
+
+
                     yield f"data: {json.dumps(response_data)}\n\n".encode('utf-8')
 
                     end_chunk_data = {
@@ -1469,11 +1484,10 @@ def handsome_chat_completions():
                     response_content = response_json[
                         "choices"
                     ][0]["message"]["content"]
-                    
                     if "tool_calls" in response_json["choices"][0]["message"]:
                         tool_calls = response_json["choices"][0]["message"]["tool_calls"]
                     else:
-                       tool_calls = []
+                        tool_calls = []
                 except (KeyError, ValueError, IndexError) as e:
                     logging.error(
                         f"解析非流式响应 JSON 失败: {e}, "
@@ -1497,7 +1511,8 @@ def handsome_chat_completions():
                                     item.get("type") == "text"
                                 ):
                                     user_content += (
-                                        item.get("text", "") + " "
+                                        item.get("text", "") +
+                                        " "
                                     )
 
                 user_content = user_content.strip()
@@ -1508,9 +1523,9 @@ def handsome_chat_completions():
                 response_content_replaced = response_content.replace(
                     '\n', '\\n'
                 ).replace('\r', '\\n')
-                
+
                 log_message = (
-                    f"使用的key: {api_key}, "
+                     f"使用的key: {api_key}, "
                     f"提示token: {prompt_tokens}, "
                     f"输出token: {completion_tokens}, "
                     f"首字用时: 0, "
@@ -1519,11 +1534,11 @@ def handsome_chat_completions():
                     f"用户的内容: {user_content_replaced}, "
                     f"输出的内容: {response_content_replaced}"
                 )
-
                 if tool_calls:
                     log_message += f", tool_calls: {tool_calls}"
-                  
+
                 logging.info(log_message)
+
                 with data_lock:
                     request_timestamps.append(time.time())
                     if "prompt_tokens" in response_json["usage"] and "completion_tokens" in response_json["usage"]:
@@ -1542,17 +1557,37 @@ def handsome_chat_completions():
                         "index": 0,
                         "message": {
                             "role": "assistant",
-                            "content": response_content,
+                             "content": response_content,
+                            
                         },
                         "finish_reason": "stop",
                         }
                     ],
                 }
-                
                 if tool_calls:
-                   response_data["choices"][0]["message"]["tool_calls"] = tool_calls
+                    if isinstance(tool_calls, list) and len(tool_calls) > 0:
+                         first_tool_call = tool_calls[0]
+                         if isinstance(first_tool_call, dict) and "function" in first_tool_call:
+                             function_call_data = first_tool_call.get("function")
+                             if isinstance(function_call_data, dict) and "name" in function_call_data and "arguments" in function_call_data:
+                                    function_call = {
+                                            "name": function_call_data["name"],
+                                            "arguments": json.dumps(function_call_data["arguments"]) if isinstance(function_call_data.get("arguments"), dict) else function_call_data["arguments"]
+                                    }
+                                    response_data["choices"][0]["message"]["function_call"] = function_call
+                                    response_data["choices"][0]["message"]["content"] = None
+                                    response_data["choices"][0]["finish_reason"] = "function_call"
+                             else:
+                                   response_data["choices"][0]["message"]["tool_calls"] = tool_calls
+                                   response_data["choices"][0]["message"]["content"] = None
+                    else:
+                         response_data["choices"][0]["message"]["tool_calls"] = tool_calls
+                         response_data["choices"][0]["message"]["content"] = None
+                     
 
                 return jsonify(response_data)
+
+
         except requests.exceptions.RequestException as e:
             logging.error(f"请求转发异常: {e}")
             return jsonify({"error": str(e)}), 500