模仿临时key增加临时chatgpt格式api接入方法。在输入框输入如下json可以临时切换使用该api，页面刷新即失效。

request_llms/bridge_chatgpt.py

@@ -91,7 +91,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
         try:
             # make a POST request to the API endpoint, stream=False
             from .bridge_all import model_info
-            endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
+            endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint']) if not llm_kwargs['tmp_endpoint'] else llm_kwargs['tmp_endpoint']
             response = requests.post(endpoint, headers=headers, proxies=proxies,
                                     json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
         except requests.exceptions.ReadTimeout as e:
@@ -147,7 +147,12 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
         raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
     return result
 
-
+def is_any_tmp_model(inputs):
+    try:
+        tmp_model_info=json.loads(inputs).keys()
+        return "tmp_key" in tmp_model_info and "tmp_model" in tmp_model_info  and "tmp_endpoint" in tmp_model_info
+    except:
+        return False 
 def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
             history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
     """
@@ -164,8 +169,15 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         chatbot.append(("输入已识别为openai的api_key", what_keys(inputs)))
         yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
         return
-    elif not is_any_api_key(chatbot._cookies['api_key']):
-        chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
+    elif is_any_tmp_model(inputs):
+        chatbot._cookies['tmp_key'] = json.loads(inputs)['tmp_key']
+        chatbot._cookies['tmp_model'] = json.loads(inputs)['tmp_model']
+        chatbot._cookies['tmp_endpoint'] = json.loads(inputs)['tmp_endpoint']
+        chatbot.append(("输入已识别为临时openai格式的模型，页面刷新后将失效", '临时模型：'+json.loads(inputs)['tmp_model']))
+        yield from update_ui(chatbot=chatbot, history=history, msg="临时模型已导入") # 刷新界面
+        return
+    elif not is_any_api_key(chatbot._cookies['api_key']) and not chatbot._cookies['tmp_key']:
+        chatbot.append((inputs, '缺少API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。\n\n3.接入临时模型：在输入区键入以下格式临时模型信息{"tmp_key":"xxx","tmp_endpoint":"https://xxxx.xxx","tmp_model":"gpt-3.5-turbo-16k"}，然后回车提交'))
         yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
         return
 
@@ -195,7 +207,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     # 检查endpoint是否合法
     try:
         from .bridge_all import model_info
-        endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
+        endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint']) if not llm_kwargs['tmp_endpoint'] else llm_kwargs['tmp_endpoint']
     except:
         tb_str = '```\n' + trimmed_format_exc() + '```'
         chatbot[-1] = (inputs, tb_str)
@@ -320,11 +332,13 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     """
     整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
     """
-    if not is_any_api_key(llm_kwargs['api_key']):
-        raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
+    if not is_any_api_key(llm_kwargs['api_key']) and not llm_kwargs['tmp_key']:
+        raise AssertionError('你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。\n\n3.接入临时模型：在输入区键入以下格式临时模型信息{"tmp_key":"xxx","tmp_endpoint":"https://xxxx.xxx","tmp_model":"gpt-3.5-turbo-16k"}，然后回车提交')
 
     if llm_kwargs['llm_model'].startswith('vllm-'):
         api_key = 'no-api-key'
+    elif llm_kwargs['tmp_key']:
+        api_key = llm_kwargs['tmp_key']
     else:
         api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
 
@@ -383,7 +397,7 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
         logging.info("Random select model:" + model)
 
     payload = {
-        "model": model,
+        "model": model if not llm_kwargs['tmp_model'] else llm_kwargs['tmp_model'] ,
         "messages": messages,
         "temperature": llm_kwargs['temperature'],  # 1.0,
         "top_p": llm_kwargs['top_p'],  # 1.0,

toolbox.py

@@ -101,12 +101,18 @@ def decorated(request: gradio.Request, cookies:dict, max_length:int, llm_model:s
         cookies.update({
             'top_p': top_p,
             'api_key': cookies['api_key'],
+            'tmp_key': cookies['tmp_key'],
+            'tmp_model': cookies['tmp_model'],
+            'tmp_endpoint': cookies['tmp_endpoint'],
             'llm_model': llm_model,
             'temperature': temperature,
             'user_name': user_name,
         })
         llm_kwargs = {
             'api_key': cookies['api_key'],
+            'tmp_key': cookies['tmp_key'],
+            'tmp_model': cookies['tmp_model'],
+            'tmp_endpoint': cookies['tmp_endpoint'],
             'llm_model': llm_model,
             'top_p': top_p,
             'max_length': max_length,
@@ -607,6 +613,9 @@ def load_chat_cookies():
         "api_key": API_KEY,
         "llm_model": LLM_MODEL,
         "customize_fn_overwrite": customize_fn_overwrite_,
+        "tmp_key":'',
+        "tmp_model":'',
+        "tmp_endpoint":'',
     }