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Running
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·
efecdf0
1
Parent(s):
10392b4
update huggingface space
Browse files- index.html +0 -34
- local_app.py +72 -0
index.html
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<!doctype html>
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<html>
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width" />
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<title>My static Space</title>
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<style>
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html,
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body {
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margin: 0;
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padding: 0;
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height: 100%;
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}
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body {
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display: flex;
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justify-content: center;
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align-items: center;
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}
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iframe {
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width: 430px;
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height: 932px;
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border: none;
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}
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</style>
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</head>
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<body>
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<iframe src="https://colearn.intern-ai.org.cn/cobuild" title="description"></iframe>
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</body>
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</html>
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local_app.py
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import streamlit as st
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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
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from llama_index.embeddings.huggingface import HuggingFaceEmbedding
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from llama_index.llms.huggingface import HuggingFaceLLM
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st.set_page_config(page_title="llama_index_demo", page_icon="🦜🔗")
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st.title("llama_index_demo")
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# 初始化模型
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@st.cache_resource
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def init_models():
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embed_model = HuggingFaceEmbedding(
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model_name="/root/model/sentence-transformer"
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)
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Settings.embed_model = embed_model
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llm = HuggingFaceLLM(
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model_name="/root/model/internlm2-chat-1_8b",
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tokenizer_name="/root/model/internlm2-chat-1_8b",
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model_kwargs={"trust_remote_code": True},
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tokenizer_kwargs={"trust_remote_code": True}
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)
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Settings.llm = llm
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documents = SimpleDirectoryReader("/root/llamaindex_demo/data").load_data()
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index = VectorStoreIndex.from_documents(documents)
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query_engine = index.as_query_engine()
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return query_engine
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# 检查是否需要初始化模型
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if 'query_engine' not in st.session_state:
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st.session_state['query_engine'] = init_models()
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def greet2(question):
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response = st.session_state['query_engine'].query(question)
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return response
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# Store LLM generated responses
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if "messages" not in st.session_state.keys():
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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？"}]
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# Display or clear chat messages
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.write(message["content"])
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def clear_chat_history():
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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？"}]
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st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
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# Function for generating LLaMA2 response
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def generate_llama_index_response(prompt_input):
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return greet2(prompt_input)
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# User-provided prompt
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if prompt := st.chat_input():
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("user"):
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st.write(prompt)
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# Gegenerate_llama_index_response last message is not from assistant
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if st.session_state.messages[-1]["role"] != "assistant":
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with st.chat_message("assistant"):
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with st.spinner("Thinking..."):
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response = generate_llama_index_response(prompt)
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placeholder = st.empty()
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placeholder.markdown(response)
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message = {"role": "assistant", "content": response}
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st.session_state.messages.append(message)
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