fix: return 参数数量 及 返回类型考虑 (#2129)

request_llms/oai_std_model_template.py

@@ -1,16 +1,13 @@
 import json
 import time
 import traceback
+
 import requests
 from loguru import logger
 
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-from toolbox import (
-    get_conf,
-    update_ui,
-    is_the_upload_folder,
-)
+from toolbox import get_conf, is_the_upload_folder, update_ui
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
     "proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
@@ -39,27 +36,35 @@ def decode_chunk(chunk):
     用于解读"content"和"finish_reason"的内容（如果支持思维链也会返回"reasoning_content"内容）
     """
     chunk = chunk.decode()
-    respose = ""
+    response = ""
     reasoning_content = ""
     finish_reason = "False"
+
+    # 考虑返回类型是 text/json 和 text/event-stream 两种
+    if chunk.startswith("data: "):
+        chunk = chunk[6:]
+    else:
+        chunk = chunk
+    
     try:
-        chunk = json.loads(chunk[6:])
+        chunk = json.loads(chunk)
     except:
-        respose = ""
+        response = ""
         finish_reason = chunk
+
     # 错误处理部分
     if "error" in chunk:
-        respose = "API_ERROR"
+        response = "API_ERROR"
         try:
             chunk = json.loads(chunk)
             finish_reason = chunk["error"]["code"]
         except:
             finish_reason = "API_ERROR"
-        return respose, finish_reason
+        return response, reasoning_content, finish_reason
 
     try:
         if chunk["choices"][0]["delta"]["content"] is not None:
-            respose = chunk["choices"][0]["delta"]["content"]
+            response = chunk["choices"][0]["delta"]["content"]
     except:
         pass
     try:
@@ -71,7 +76,7 @@ def decode_chunk(chunk):
         finish_reason = chunk["choices"][0]["finish_reason"]
     except:
         pass
-    return respose, reasoning_content, finish_reason
+    return response, reasoning_content, finish_reason
 
 
 def generate_message(input, model, key, history, max_output_token, system_prompt, temperature):
@@ -106,7 +111,7 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
     what_i_ask_now["role"] = "user"
     what_i_ask_now["content"] = input
     messages.append(what_i_ask_now)
-    playload = {
+    payload = {
         "model": model,
         "messages": messages,
         "temperature": temperature,
@@ -114,7 +119,7 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
         "max_tokens": max_output_token,
     }
 
-    return headers, playload
+    return headers, payload
 
 
 def get_predict_function(
@@ -141,7 +146,7 @@ def get_predict_function(
         history=[],
         sys_prompt="",
         observe_window=None,
-        console_slience=False,
+        console_silence=False,
     ):
         """
         发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
@@ -162,7 +167,7 @@ def get_predict_function(
             raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
         if inputs == "":
             inputs = "你好👋"
-        headers, playload = generate_message(
+        headers, payload = generate_message(
             input=inputs,
             model=llm_kwargs["llm_model"],
             key=APIKEY,
@@ -182,7 +187,7 @@ def get_predict_function(
                     endpoint,
                     headers=headers,
                     proxies=None if disable_proxy else proxies,
-                    json=playload,
+                    json=payload,
                     stream=True,
                     timeout=TIMEOUT_SECONDS,
                 )
@@ -198,7 +203,7 @@ def get_predict_function(
         result = ""
         finish_reason = ""
         if reasoning:
-            resoning_buffer = ""
+            reasoning_buffer = ""
         
         stream_response = response.iter_lines()
         while True:
@@ -226,12 +231,12 @@ def get_predict_function(
             if chunk:
                 try:
                     if finish_reason == "stop":
-                        if not console_slience:
+                        if not console_silence:
                             print(f"[response] {result}")
                         break
                     result += response_text
                     if reasoning:
-                        resoning_buffer += reasoning_content
+                        reasoning_buffer += reasoning_content
                     if observe_window is not None:
                         # 观测窗，把已经获取的数据显示出去
                         if len(observe_window) >= 1:
@@ -247,9 +252,9 @@ def get_predict_function(
                     logger.error(error_msg)
                     raise RuntimeError("Json解析不合常规")
         if reasoning:
-            # reasoning 的部分加上框 (>)
-            return '\n'.join(map(lambda x: '> ' + x, resoning_buffer.split('\n'))) + \
-                   '\n\n' + result
+            return f'''<div style="padding: 1em; line-height: 1.5; text-wrap: wrap; opacity: 0.8">
+            {''.join([f'<p style="margin: 1.25em 0;">{line}</p>' for line in reasoning_buffer.split('\n')])}
+            </div>\n\n''' + result
         return result
 
     def predict(
@@ -268,7 +273,7 @@ def get_predict_function(
         inputs 是本次问询的输入
         top_p, temperature是chatGPT的内部调优参数
         history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-        chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+        chatbot 为WebUI中显示的对话列表，修改它，然后yield出去，可以直接修改对话界面内容
         additional_fn代表点击的哪个按钮，按钮见functional.py
         """
         from .bridge_all import model_info
@@ -299,7 +304,7 @@ def get_predict_function(
             )  # 刷新界面
             time.sleep(2)
 
-        headers, playload = generate_message(
+        headers, payload = generate_message(
             input=inputs,
             model=llm_kwargs["llm_model"],
             key=APIKEY,
@@ -321,7 +326,7 @@ def get_predict_function(
                     endpoint,
                     headers=headers,
                     proxies=None if disable_proxy else proxies,
-                    json=playload,
+                    json=payload,
                     stream=True,
                     timeout=TIMEOUT_SECONDS,
                 )
@@ -367,7 +372,7 @@ def get_predict_function(
                         chunk_decoded = chunk.decode()
                         chatbot[-1] = (
                             chatbot[-1][0],
-                            "[Local Message] {finish_reason},获得以下报错信息：\n"
+                            f"[Local Message] {finish_reason},获得以下报错信息：\n"
                             + chunk_decoded,
                         )
                         yield from update_ui(
@@ -385,7 +390,9 @@ def get_predict_function(
                     if reasoning:
                         gpt_replying_buffer += response_text
                         gpt_reasoning_buffer += reasoning_content
-                        history[-1] = '\n'.join(map(lambda x: '> ' + x, gpt_reasoning_buffer.split('\n'))) + '\n\n' + gpt_replying_buffer
+                        history[-1] = f'''<div style="padding: 1em; line-height: 1.5; text-wrap: wrap; opacity: 0.8">
+                        {''.join([f'<p style="margin: 1.25em 0;">{line}</p>' for line in gpt_reasoning_buffer.split('\n')])}
+                        </div>\n\n''' + gpt_replying_buffer
                     else:
                         gpt_replying_buffer += response_text
                         # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出