完成文生文AI接口，并将消息处理从同步改为异步

.gitignore

@@ -5,3 +5,4 @@ latest*.png
 .DS_Store
 anything-v4.0
 chatglm2-6b
+/.idea/

ai.py

@@ -0,0 +1,47 @@
+import json
+
+from zhipuai import ZhipuAI
+import flask
+
+
+
+# client = ZhipuAI(api_key="73bdeed728677bc80efc6956478a2315.VerNWJMCwN9L5gTi")  # 请填写您自己的APIKey
+# response = client.chat.completions.create(
+#     model="glm-4",  # 请填写您要调用的模型名称
+#     messages=[
+#         {"role": "user", "content": "你好"},
+#     ],
+# )
+# print(response.choices[0].message)
+app = flask.Flask(__name__)
+
+
+@app.route("/chat", methods=['POST'])
+def app_chat():
+    data = json.loads(flask.globals.request.get_data())
+    # print(data)
+    uid = data["user_id"]
+
+    if not data["text"][-1] in ['?', '？', '.', '。', ',', '，', '!', '！']:
+        data["text"] += "。"
+
+    # 使用ZhipuAI库调用模型生成回复
+    client = ZhipuAI(api_key="73bdeed728677bc80efc6956478a2315.VerNWJMCwN9L5gTi")  # 请填写您自己的APIKey
+    response = client.chat.completions.create(
+        model="glm-4-flash",  # 请填写您要调用的模型名称
+        messages=[
+            {"role": "user", "content": data["text"]},
+        ],
+    )
+
+    # 获取模型的回复
+    resp = response.choices[0].message.content
+
+    if resp:
+        return json.dumps({"user_id": data["user_id"], "text": resp, "error": False, "error_msg": ""})
+    else:
+        return json.dumps({"user_id": data["user_id"], "text": "", "error": True, "error_msg": "模型未返回回复"})
+
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0", port=11111)

bot.py

@@ -1,5 +1,6 @@
 import json
-import openai
+
+from zhipuai import ZhipuAI
 import re
 from diffusers import DiffusionPipeline, StableDiffusionPipeline, DPMSolverMultistepScheduler
 from transformers import AutoTokenizer, AutoModel
@@ -16,6 +17,7 @@ args = ps.parse_args()
 with open(args.config) as f:
     config_json = json.load(f)
 
+
 class GlobalData:
     # OPENAI_ORGID = config_json[""]
     OPENAI_APIKEY = config_json["OpenAI-GPT"]["OpenAI-Key"]
@@ -31,7 +33,8 @@ class GlobalData:
     GENERATE_PICTURE_ARG_PAT = re.compile("(\(|（)([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)(\)|）)")
     GENERATE_PICTURE_ARG_PAT2 = re.compile("(\(|（)([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)[ \n\t]+([0-9]+)(\)|）)")
     GENERATE_PICTURE_NEG_PROMPT_DELIMETER = re.compile("\n+")
-    GENERATE_PICTURE_MAX_ITS = 200 #最大迭代次数
+    GENERATE_PICTURE_MAX_ITS = 200  # 最大迭代次数
+
 
 USE_OPENAIGPT = False
 USE_CHATGLM = False
@@ -52,14 +55,16 @@ elif config_json["ChatGLM"]["Enable"]:
 
 app = flask.Flask(__name__)
 
+
 # 这个用于放行生成的任何图片，替换掉默认的NSFW检查器，公共场合慎重使用
 def run_safety_nochecker(image, device, dtype):
     print("警告：屏蔽了内容安全性检查，可能会产生有害内容")
     return image, None
 
+
 sd_args = {
-    "pretrained_model_name_or_path" : config_json["Diffusion"]["Diffusion-Model"],
+    "pretrained_model_name_or_path": config_json["Diffusion"]["Diffusion-Model"],
-    "torch_dtype" : (torch.float16 if config_json["Diffusion"].get("UseFP16", True) else torch.float32)
+    "torch_dtype": (torch.float16 if config_json["Diffusion"].get("UseFP16", True) else torch.float32)
 }
 
 sd_pipe = StableDiffusionPipeline.from_pretrained(**sd_args)
@@ -76,7 +81,8 @@ GPT_SUCCESS = 0
 GPT_NORESULT = 1
 GPT_ERROR = 2
 
-def CallOpenAIGPT(prompts : typing.List[str]):
+
+def CallOpenAIGPT(prompts: typing.List[str]):
     try:
         res = openai.ChatCompletion.create(
             model=config_json["OpenAI-GPT"]["GPT-Model"],
@@ -92,7 +98,8 @@ def CallOpenAIGPT(prompts : typing.List[str]):
         traceback.print_exception(e)
         return (GPT_ERROR, str(e))
 
-def CallChatGLM(msg, history : typing.List[str]):
+
+def CallChatGLM(msg, history: typing.List[str]):
     try:
         resp, hist = chatglm_model.chat(chatglm_tokenizer, msg, history=history)
         if isinstance(resp, tuple):
@@ -101,19 +108,21 @@ def CallChatGLM(msg, history : typing.List[str]):
     except Exception as e:
         return (GPT_ERROR, str(e))
 
-def add_context(uid : str, is_user : bool, msg : str):
+
+def add_context(uid: str, is_user: bool, msg: str):
     if not uid in GlobalData.context_for_users:
         GlobalData.context_for_users[uid] = []
     if USE_OPENAIGPT:
         GlobalData.context_for_users[uid].append({
-            "role" : "system",
+            "role": "system",
-            "content" : msg   
+            "content": msg
         }
         )
     elif USE_CHATGLM:
         GlobalData.context_for_users[uid].append(msg)
 
-def get_context(uid : str): 
+
+def get_context(uid: str):
     if not uid in GlobalData.context_for_users:
         GlobalData.context_for_users[uid] = []
     return GlobalData.context_for_users[uid]
@@ -126,35 +135,33 @@ def app_chat_clear():
     print(f"Cleared context for {data['user_id']}")
     return ""
 
+
 @app.route("/chat", methods=['POST'])
 def app_chat():
     data = json.loads(flask.globals.request.get_data())
-    #print(data)
+    # print(data)
     uid = data["user_id"]
 
     if not data["text"][-1] in ['?', '？', '.', '。', ',', '，', '!', '！']:
         data["text"] += "。"
 
-    if USE_OPENAIGPT:
+    # 使用ZhipuAI库调用模型生成回复
-        add_context(uid, True, data["text"])
+    client = ZhipuAI(api_key="73bdeed728677bc80efc6956478a2315.VerNWJMCwN9L5gTi")  # 请填写您自己的APIKey
-        #prompt = GlobalData.context_for_users[uid]
+    response = client.chat.completions.create(
-        prompt = get_context(uid)
+        model="glm-4",  # 请填写您要调用的模型名称
-        resp = CallOpenAIGPT(prompt=prompt)
+        messages=[
-        #GlobalData.context_for_users[data["user_id"]] = (prompt + resp)
+            {"role": "user", "content": data["text"]},
-        add_context(uid, False, resp[1])
+        ],
-        #print(f"Prompt = {prompt}\nResponse = {resp[1]}")
+    )
-    elif USE_CHATGLM:
-        #prompt = GlobalData.context_for_users[uid]
-        prompt = get_context(uid)
-        resp = CallChatGLM(msg=data["text"], history=prompt)
-        add_context(uid, True, (data["text"], resp[1]))
-    else:
-        pass
 
-    if resp[0] == GPT_SUCCESS:
+    # 获取模型的回复
-        return json.dumps({"user_id" : data["user_id"], "text" : resp[1], "error" : False, "error_msg" : ""})
+    resp = response.choices[0].message.content
+
+    if resp:
+        return json.dumps({"user_id": data["user_id"], "text": resp, "error": False, "error_msg": ""})
     else:
-        return json.dumps({"user_id" : data["user_id"], "text" : "", "error" : True, "error_msg" : resp[1]})
+        return json.dumps({"user_id": data["user_id"], "text": "", "error": True, "error_msg": "模型未返回回复"})
+
 
 @app.route("/draw", methods=['POST'])
 def app_draw():
@@ -167,8 +174,8 @@ def app_draw():
         if prompt[i] == ':' or prompt[i] == '：':
             break
     if i == len(prompt):
-        return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : "格式不对，正确的格式是：生成图片：Prompt 或者 生成图片(宽 高 迭代次数 [图片最大数量(缺省1)])：Prompt"})
+        return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
-    
+                           "error_msg": "格式不对，正确的格式是：生成图片：Prompt 或者 生成图片(宽 高 迭代次数 [图片最大数量(缺省1)])：Prompt"})
 
     match_args = re.match(GlobalData.GENERATE_PICTURE_ARG_PAT2, prompt[:i])
     if not match_args is None:
@@ -185,7 +192,8 @@ def app_draw():
             NUM_PIC = 1
         else:
             if len(prompt[:i].strip()) != 0:
-                return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : "格式不对，正确的格式是：生成图片：Prompt 或者 生成图片(宽 高 迭代次数 [图片最大数量(缺省1)])：Prompt"})
+                return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
+                                   "error_msg": "格式不对，正确的格式是：生成图片：Prompt 或者 生成图片(宽 高 迭代次数 [图片最大数量(缺省1)])：Prompt"})
             else:
                 W = 768
                 H = 768
@@ -193,12 +201,14 @@ def app_draw():
                 NUM_PIC = 1
 
     if W > 2500 or H > 2500:
-        return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : "你要求的图片太大了，我不干了～"})
+        return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
+                           "error_msg": "你要求的图片太大了，我不干了～"})
 
     if ITS > GlobalData.GENERATE_PICTURE_MAX_ITS:
-        return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : f"迭代次数太多了，不要超过{GlobalData.GENERATE_PICTURE_MAX_ITS}次"})
+        return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
+                           "error_msg": f"迭代次数太多了，不要超过{GlobalData.GENERATE_PICTURE_MAX_ITS}次"})
 
-    prompt = prompt[(i+1):].strip()
+    prompt = prompt[(i + 1):].strip()
 
     prompts = re.split(GlobalData.GENERATE_PICTURE_NEG_PROMPT_DELIMETER, prompt)
     prompt = prompts[0]
@@ -210,31 +220,37 @@ def app_draw():
     print(f"Generating {NUM_PIC} picture(s) with prompt = {prompt} , negative prompt = {neg_prompt}")
 
     try:
-        if NUM_PIC > 1 and torch.backends.mps.is_available():  #Apple silicon上的bug：https://github.com/huggingface/diffusers/issues/363
+        if NUM_PIC > 1 and torch.backends.mps.is_available():  # Apple silicon上的bug：https://github.com/huggingface/diffusers/issues/363
-            return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, 
+            return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True,
-                "error_msg" : "单prompt生成多张图像在Apple silicon上无法实现，相关讨论参考https://github.com/huggingface/diffusers/issues/363"})
+                               "error_msg": "单prompt生成多张图像在Apple silicon上无法实现，相关讨论参考https://github.com/huggingface/diffusers/issues/363"})
 
-        images = sd_pipe(prompt=prompt, negative_prompt=neg_prompt, width=W, height=H, num_inference_steps=ITS, num_images_per_prompt=NUM_PIC).images[:NUM_PIC]
+        images = sd_pipe(prompt=prompt, negative_prompt=neg_prompt, width=W, height=H, num_inference_steps=ITS,
+                         num_images_per_prompt=NUM_PIC).images[:NUM_PIC]
         if len(images) == 0:
-            return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : "没有产生任何图像"})
+            return json.dumps(
+                {"user_name": data["user_name"], "filenames": [], "error": True, "error_msg": "没有产生任何图像"})
         filenames = []
         for i, img in enumerate(images):
             img.save(f"latest-{i}.png")
             filenames.append(f"latest-{i}.png")
-        return json.dumps({"user_name" : data["user_name"], "filenames" : filenames, "error" : False, "error_msg" : ""})        
+        return json.dumps({"user_name": data["user_name"], "filenames": filenames, "error": False, "error_msg": ""})
 
     except Exception as e:
-        return json.dumps({"user_name" : data["user_name"], "filenames" : [], "error" : True, "error_msg" : str(e)})
+        return json.dumps({"user_name": data["user_name"], "filenames": [], "error": True, "error_msg": str(e)})
+
 
 @app.route("/info", methods=['POST', 'GET'])
 def app_info():
-    return "\n".join([f"GPT模型：{config_json['OpenAI-GPT']['GPT-Model'] if USE_OPENAIGPT else config_json['ChatGLM']['GPT-Model']}", f"Diffusion模型：{config_json['Diffusion']['Diffusion-Model']}",
+    return "\n".join(
+        [f"GPT模型：{config_json['OpenAI-GPT']['GPT-Model'] if USE_OPENAIGPT else config_json['ChatGLM']['GPT-Model']}",
+         f"Diffusion模型：{config_json['Diffusion']['Diffusion-Model']}",
          "默认图片规格：768x768 RGB三通道", "Diffusion默认迭代轮数：20",
          f"使用半精度浮点数 : {'是' if config_json['Diffusion'].get('UseFP16', True) else '否'}",
          f"屏蔽NSFW检查：{'是' if config_json['Diffusion']['NoNSFWChecker'] else '否'}",
          "清空上下文指令：重置上下文",
          "生成图片指令：生成图片(宽 高 迭代次数)：正面提示 换行写负面提示，其中(宽 高 迭代次数)和换行写的负面提示都是可以省略的"])
 
+
 if __name__ == "__main__":
 
     if USE_OPENAIGPT:

wechat_client.go

@@ -86,6 +86,13 @@ func main() {
 
 	// 注册消息处理函数
 	bot.MessageHandler = func(msg *openwechat.Message) {
+		go sendMessage(msg, self)
+	}
+
+	bot.Block()
+}
+
+func sendMessage(msg *openwechat.Message, self *openwechat.Self) {
 	if msg.IsTickledMe() {
 		msg.ReplyText("别拍了，机器人是会被拍坏掉的。")
 		return
@@ -199,7 +206,4 @@ func main() {
 		}
 
 	}
-	}
-
-	bot.Block()
 }