已使用金额优化

ChuanhuChatbot.py

@@ -266,8 +266,8 @@ def create_new_model():
                         changeProxyBtn = gr.Button(i18n("🔄 设置代理地址"))
                         default_btn = gr.Button(i18n("🔙 恢复默认设置"))
 
-    gr.Markdown(CHUANHU_DESCRIPTION, elem_id="description")
-    gr.HTML(FOOTER.format(versions=versions_html()), elem_id="footer")
+    # gr.Markdown(CHUANHU_DESCRIPTION, elem_id="description")
+    # gr.HTML(FOOTER.format(versions=versions_html()), elem_id="footer")
 
     # https://github.com/gradio-app/gradio/pull/3296
     def create_greeting(request: gr.Request):

modules/models/models.py

@@ -20,6 +20,7 @@
 import aiohttp
 from enum import Enum
 import uuid
+import timedelta
 
 from ..presets import *
 from ..llama_func import *
@@ -32,13 +33,13 @@
 
 class OpenAIClient(BaseLLMModel):
     def __init__(
-        self,
-        model_name,
-        api_key,
-        system_prompt=INITIAL_SYSTEM_PROMPT,
-        temperature=1.0,
-        top_p=1.0,
-        user_name=""
+            self,
+            model_name,
+            api_key,
+            system_prompt=INITIAL_SYSTEM_PROMPT,
+            temperature=1.0,
+            top_p=1.0,
+            user_name=""
     ) -> None:
         super().__init__(
             model_name=model_name,
@@ -81,10 +82,11 @@ def count_token(self, user_input):
     def billing_info(self):
         try:
             curr_time = datetime.datetime.now()
+            curr_time_f = (curr_time + datetime.timedelta(days=1)).strftime("%Y-%m-%d")
             last_day_of_month = get_last_day_of_month(
                 curr_time).strftime("%Y-%m-%d")
-            first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d")
-            usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}"
+            first_day_of_month = (curr_time.replace(day=1) - datetime.timedelta(weeks=11)).strftime("%Y-%m-%d")
+            usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={curr_time_f}"
             try:
                 usage_data = self._get_billing_data(usage_url)
             except Exception as e:
@@ -95,7 +97,7 @@ def billing_info(self):
             usage_percent = round(usage_data["total_usage"] / usage_limit, 2)
             # return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
             return """\
-                <b>""" + i18n("本月使用金额") + f"""</b>
+                <b>""" + i18n("已使用金额") + f"""</b>
                 <div class="progress-bar">
                     <div class="progress" style="width: {usage_percent}%;">
                         <span class="progress-text">{usage_percent}%</span>
@@ -105,7 +107,7 @@ def billing_info(self):
                 """
         except requests.exceptions.ConnectTimeout:
             status_text = (
-                STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
+                    STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
             )
             return status_text
         except requests.exceptions.ReadTimeout:
@@ -275,7 +277,7 @@ def _get_glm_style_input(self):
         logging.debug(colorama.Fore.YELLOW +
                       f"{history}" + colorama.Fore.RESET)
         assert (
-            len(history) % 2 == 0
+                len(history) % 2 == 0
         ), f"History should be even length. current history is: {history}"
         history = [[history[i], history[i + 1]]
                    for i in range(0, len(history), 2)]
@@ -290,22 +292,22 @@ def get_answer_at_once(self):
     def get_answer_stream_iter(self):
         history, query = self._get_glm_style_input()
         for response, history in CHATGLM_MODEL.stream_chat(
-            CHATGLM_TOKENIZER,
-            query,
-            history,
-            max_length=self.token_upper_limit,
-            top_p=self.top_p,
-            temperature=self.temperature,
+                CHATGLM_TOKENIZER,
+                query,
+                history,
+                max_length=self.token_upper_limit,
+                top_p=self.top_p,
+                temperature=self.temperature,
         ):
             yield response
 
 
 class LLaMA_Client(BaseLLMModel):
     def __init__(
-        self,
-        model_name,
-        lora_path=None,
-        user_name=""
+            self,
+            model_name,
+            lora_path=None,
+            user_name=""
     ) -> None:
         super().__init__(model_name=model_name, user=user_name)
         from lmflow.datasets.dataset import Dataset
@@ -334,8 +336,11 @@ def __init__(
                 # raise Exception(f"models目录下没有这个模型: {model_name}")
             if lora_path is not None:
                 lora_path = f"lora/{lora_path}"
-            model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
-                                        use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True)
+            model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None,
+                                        config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
+                                        use_fast_tokenizer=True, model_revision='main', use_auth_token=False,
+                                        torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1,
+                                        use_ram_optimized_load=True)
             pipeline_args = InferencerArguments(
                 local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16')
 
@@ -540,13 +545,13 @@ def get_answer_at_once(self):
 
 
 def get_model(
-    model_name,
-    lora_model_path=None,
-    access_key=None,
-    temperature=None,
-    top_p=None,
-    system_prompt=None,
-    user_name=""
+        model_name,
+        lora_model_path=None,
+        access_key=None,
+        temperature=None,
+        top_p=None,
+        system_prompt=None,
+        user_name=""
 ) -> BaseLLMModel:
     msg = i18n("模型设置为了：") + f" {model_name}"
     model_type = ModelType.get_type(model_name)