增加打印用户信息

__main__.py

@@ -126,8 +126,8 @@ with gr.Blocks(title="ChatGPT 学术优化", theme=set_theme, analytics_enabled=
                                             label="Temperature", )
                     max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="MaxLength",)
 
-                    models_box = gr.CheckboxGroup(["input加密", "prompt提示"],
+                    models_box = gr.CheckboxGroup(["input加密"],
-                                                  value=["input加密", "prompt提示"], label="对话模式")
+                                                  value=["input加密"], label="对话模式")
                     checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区"],
                                                   value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
                     md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(

request_llm/bridge_chatgpt.py

@@ -60,7 +60,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     while True:
         try:
             # make a POST request to the API endpoint, stream=False
-            from bridge_all import model_info
+            from request_llm.bridge_all import model_info
             endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
             response = requests.post(endpoint, headers=headers, proxies=proxies,
                                     json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
@@ -134,7 +134,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
 
     raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
+    logging.info(f'[raw_input]_{llm_kwargs["ipaddr"]} {raw_input}')
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
@@ -144,19 +144,17 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。")
         yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
         return
-        
+
     history.append(inputs); history.append(" ")
 
     retry = 0
     while True:
         try:
             # make a POST request to the API endpoint, stream=True
-            from .bridge_all import model_info
+            from request_llm.bridge_all import model_info
             endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
             response = requests.post(endpoint, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);
+                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
-            print(response)
-            break
         except:
             retry += 1
             chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
@@ -165,7 +163,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             if retry > MAX_RETRY: raise TimeoutError
 
     gpt_replying_buffer = ""
-    
+
     is_head_of_the_stream = True
     if stream:
         stream_response =  response.iter_lines()
@@ -175,14 +173,14 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             if is_head_of_the_stream and (r'"object":"error"' not in chunk.decode()):
                 # 数据流的第一帧不携带content
                 is_head_of_the_stream = False; continue
-            
+
             if chunk:
                 try:
                     chunk_decoded = chunk.decode()
                     # 前者API2D的
                     if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        logging.info(f'[response] {gpt_replying_buffer}')
+                        logging.info(f'[response]_{llm_kwargs["ipaddr"]} {gpt_replying_buffer}')
                         break
                     # 处理数据流的主体
                     chunkjson = json.loads(chunk_decoded[6:])
@@ -266,14 +264,14 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
         "frequency_penalty": 0,
     }
     try:
-        print("\033[1;35m", f"{llm_kwargs['llm_model']} :", "\033[0m", f"{conversation_cnt} : {inputs[:100]} ..........")
+        print("\033[1;35m", f"{llm_kwargs['llm_model']}_{llm_kwargs['ipaddr']} :", "\033[0m", f"{conversation_cnt} : {inputs[:100]} ..........")
     except:
         print('输入中可能存在乱码。')
     return headers, payload
 
 if __name__ == '__main__':
     llm_kwargs = {
-        'api_key': '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',
+        'api_key': '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',
         'llm_model': 'gpt-3.5-turbo',
         'top_p': 1,
         'max_length': 512,

toolbox.py

@@ -27,7 +27,9 @@ def ArgsGeneralWrapper(f):
     """
     装饰器函数，用于重组输入参数，改变输入参数的顺序与结构。
     """
-    def decorated(cookies, max_length, llm_model, txt, top_p, temperature, chatbot, history, system_prompt, models, ipaddr:gr.Request,  *args):
+    def decorated(cookies, max_length, llm_model, txt, top_p, temperature,
+                  chatbot, history, system_prompt, models, ipaddr:gr.Request,  *args):
+        """"""
         txt_passon = txt
         if 'input加密' in models: txt_passon = func_box.encryption_str(txt)
         # 引入一个有cookie的chatbot