Update app.py

app.py

@@ -1,14 +1,25 @@
 import streamlit as st
-import os
-from llama_index.core import VectorStoreIndex, Settings
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.legacy.callbacks import CallbackManager
 from llama_index.llms.openai_like import OpenAILike
 
+# Create an instance of CallbackManager
+callback_manager = CallbackManager()
+
+api_base_url =  "https://internlm-chat.intern-ai.org.cn/puyu/api/v1/"
+model = "internlm2.5-latest"
+api_key = os.environ['API_KEY']
+
+
+llm =OpenAILike(model=model, api_base=api_base_url, api_key=api_key, is_chat_model=True,callback_manager=callback_manager)
+
+
+
 st.set_page_config(page_title="llama_index_demo", page_icon="🦜🔗")
 st.title("llama_index_demo")
 
-# Initialize models
+# 初始化模型
 @st.cache_resource
 def init_models():
     embed_model = HuggingFaceEmbedding(
@@ -16,12 +27,7 @@ def init_models():
     )
     Settings.embed_model = embed_model
 
-    llm = HuggingFaceLLM(
-        model_name="internlm/internlm2-chat-1_8b",
-        tokenizer_name="internlm/internlm2-chat-1_8b",
-        model_kwargs={"trust_remote_code": True},
-        tokenizer_kwargs={"trust_remote_code": True}
-    )
+    #用初始化llm
     Settings.llm = llm
 
     documents = SimpleDirectoryReader("./").load_data()
@@ -30,23 +36,20 @@ def init_models():
 
     return query_engine
 
-
-# Check if models need initialization
+# 检查是否需要初始化模型
 if 'query_engine' not in st.session_state:
     st.session_state['query_engine'] = init_models()
-    
+
 def greet2(question):
-    if st.session_state['query_engine'] is None:
-      return "The models failed to initialize, please check your environment"
     response = st.session_state['query_engine'].query(question)
     return response
 
-
+      
 # Store LLM generated responses
 if "messages" not in st.session_state.keys():
-    st.session_state.messages = [{"role": "assistant", "content": "你好，我是你的助手，有什么我可以帮助你的吗？"}]
+    st.session_state.messages = [{"role": "assistant", "content": "你好，我是你的助手，有什么我可以帮助你的吗？"}]    
 
-# Display or clear chat messages
+    # Display or clear chat messages
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.write(message["content"])