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""" |
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This script is a simple web demo based on Streamlit, showcasing the use of the ChatGLM3-6B model. For a more comprehensive web demo, |
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it is recommended to use 'composite_demo'. |
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Usage: |
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- Run the script using Streamlit: `streamlit run web_demo_streamlit.py` |
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- Adjust the model parameters from the sidebar. |
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- Enter questions in the chat input box and interact with the ChatGLM3-6B model. |
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Note: Ensure 'streamlit' and 'transformers' libraries are installed and the required model checkpoints are available. |
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""" |
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import os |
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import streamlit as st |
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import torch |
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from transformers import AutoModel, AutoTokenizer |
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MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b') |
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TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH) |
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st.set_page_config( |
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page_title="ChatGLM3-6B Streamlit Simple Demo", |
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page_icon=":robot:", |
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layout="wide" |
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) |
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@st.cache_resource |
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def get_model(): |
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tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True) |
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model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True, device_map="auto").eval() |
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return tokenizer, model |
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tokenizer, model = get_model() |
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if "history" not in st.session_state: |
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st.session_state.history = [] |
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if "past_key_values" not in st.session_state: |
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st.session_state.past_key_values = None |
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max_length = st.sidebar.slider("max_length", 0, 32768, 8192, step=1) |
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top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01) |
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temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.6, step=0.01) |
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buttonClean = st.sidebar.button("清理会话历史", key="clean") |
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if buttonClean: |
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st.session_state.history = [] |
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st.session_state.past_key_values = None |
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if torch.cuda.is_available(): |
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torch.cuda.empty_cache() |
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st.rerun() |
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for i, message in enumerate(st.session_state.history): |
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if message["role"] == "user": |
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with st.chat_message(name="user", avatar="user"): |
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st.markdown(message["content"]) |
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else: |
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with st.chat_message(name="assistant", avatar="assistant"): |
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st.markdown(message["content"]) |
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with st.chat_message(name="user", avatar="user"): |
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input_placeholder = st.empty() |
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with st.chat_message(name="assistant", avatar="assistant"): |
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message_placeholder = st.empty() |
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prompt_text = st.chat_input("请输入您的问题") |
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if prompt_text: |
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input_placeholder.markdown(prompt_text) |
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history = st.session_state.history |
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past_key_values = st.session_state.past_key_values |
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for response, history, past_key_values in model.stream_chat( |
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tokenizer, |
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prompt_text, |
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history, |
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past_key_values=past_key_values, |
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max_length=max_length, |
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top_p=top_p, |
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temperature=temperature, |
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return_past_key_values=True, |
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): |
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message_placeholder.markdown(response) |
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st.session_state.history = history |
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st.session_state.past_key_values = past_key_values |
