Upload folder using huggingface_hub

app.py

@@ -9,6 +9,10 @@ chatWorld = ChatWorld()
 role_name_list_global = None
 role_name_dict_global = None
 
+Meta = {
+    "uuid":"111"
+}
+
 
 def getContent(input_file):
     # 读取文件内容
@@ -28,8 +32,8 @@ def getContent(input_file):
     role_name_dict_global = role_name_dict
 
     return (
-        gr.Radio(choices=role_name_list, interactive=True, value=role_name_list[0]),
-        gr.Radio(choices=role_name_list, interactive=True, value=role_name_list[-1]),
+        gr.Radio(choices=role_name_list, interactive=True),
+        gr.Radio(choices=role_name_list, interactive=True),
     )
 
 

run_gradio.sh

@@ -1,4 +1,4 @@
-export CUDA_VISIBLE_DEVICES=3
+export CUDA_VISIBLE_DEVICES=1
 export HF_ENDPOINT="https://hf-mirror.com"
 
 # Start the gradio server

src/ChatWorld.py

@@ -17,8 +17,6 @@ class ChatWorld:
     ) -> None:
         self.model_name = pretrained_model_name_or_path
 
-        self.global_batch_size = global_batch_size
-
         self.client = GLM_api()
 
         if model_load:

src/DataBase/BaseDB.py

@@ -5,6 +5,7 @@ from langchain_community.embeddings import HuggingFaceEmbeddings
 from transformers import AutoTokenizer
 from langchain.text_splitter import TokenTextSplitter
 from langchain_core.documents import Document
+from torch.cuda import is_available
 
 
 class BaseDB(metaclass=ABCMeta):
@@ -21,7 +22,12 @@ class BaseDB(metaclass=ABCMeta):
         if not embedding_name:
             embedding_name = "BAAI/bge-small-zh-v1.5"
 
-        self.embedding = HuggingFaceEmbeddings(model_name=embedding_name)
+        if is_available():
+            model_kwargs = {"device": "cuda"}
+        else:
+            model_kwargs = {"device": "cpu"}
+
+        self.embedding = HuggingFaceEmbeddings(model_name=embedding_name,model_kwargs=model_kwargs)
         self.tokenizer = AutoTokenizer.from_pretrained(embedding_name)
 
         self.init_db()