Update app.py

app.py

@@ -1,84 +1,4 @@
 import streamlit as st
-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
-import os
 
-# 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.getenv('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")
-
-# 修改初始化模型函数
-@st.cache_resource
-def init_models():
-    # 使用 Hugging Face Hub 上的模型
-    embed_model = HuggingFaceEmbedding(
-        model_name="sentence-transformers/all-MiniLM-L6-v2"
-    )
-    Settings.embed_model = embed_model
-    Settings.llm = llm
-
-    # 使用相对路径加载数据
-    documents = SimpleDirectoryReader("data").load_data()
-    index = VectorStoreIndex.from_documents(documents)
-    query_engine = index.as_query_engine()
-
-    return query_engine
-
-# 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):
-    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？"}]    
-
-    # Display or clear chat messages
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.write(message["content"])
-
-def clear_chat_history():
-    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.sidebar.button('Clear Chat History', on_click=clear_chat_history)
-
-# Function for generating LLaMA2 response
-def generate_llama_index_response(prompt_input):
-    return greet2(prompt_input)
-
-# User-provided prompt
-if prompt := st.chat_input():
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    with st.chat_message("user"):
-        st.write(prompt)
-
-# Gegenerate_llama_index_response last message is not from assistant
-if st.session_state.messages[-1]["role"] != "assistant":
-    with st.chat_message("assistant"):
-        with st.spinner("Thinking..."):
-            response = generate_llama_index_response(prompt)
-            placeholder = st.empty()
-            placeholder.markdown(response)
-    message = {"role": "assistant", "content": response}
-    st.session_state.messages.append(message)
+st.title("Test App")
+st.write("Hello World!")