mirror of https://github.com/THUDM/ChatGLM-6B
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71 lines
2.1 KiB
71 lines
2.1 KiB
from transformers import AutoModel, AutoTokenizer |
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import streamlit as st |
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from streamlit_chat import message |
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st.set_page_config( |
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page_title="ChatGLM-6b 演示", |
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page_icon=":robot:" |
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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("THUDM/chatglm-6b", trust_remote_code=True) |
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda() |
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model = model.eval() |
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return tokenizer, model |
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MAX_TURNS = 20 |
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MAX_BOXES = MAX_TURNS * 2 |
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def predict(input, max_length, top_p, temperature, history=None): |
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tokenizer, model = get_model() |
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if history is None: |
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history = [] |
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with container: |
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if len(history) > 0: |
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if len(history)>MAX_BOXES: |
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history = history[-MAX_TURNS:] |
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for i, (query, response) in enumerate(history): |
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message(query, avatar_style="big-smile", key=str(i) + "_user") |
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message(response, avatar_style="bottts", key=str(i)) |
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message(input, avatar_style="big-smile", key=str(len(history)) + "_user") |
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st.write("AI正在回复:") |
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with st.empty(): |
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for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p, |
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temperature=temperature): |
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query, response = history[-1] |
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st.write(response) |
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return history |
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container = st.container() |
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# create a prompt text for the text generation |
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prompt_text = st.text_area(label="用户命令输入", |
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height = 100, |
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placeholder="请在这儿输入您的命令") |
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max_length = st.sidebar.slider( |
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'max_length', 0, 4096, 2048, step=1 |
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) |
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top_p = st.sidebar.slider( |
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'top_p', 0.0, 1.0, 0.6, step=0.01 |
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) |
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temperature = st.sidebar.slider( |
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'temperature', 0.0, 1.0, 0.95, step=0.01 |
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) |
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if 'state' not in st.session_state: |
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st.session_state['state'] = [] |
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if st.button("发送", key="predict"): |
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with st.spinner("AI正在思考，请稍等........"): |
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# text generation |
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st.session_state["state"] = predict(prompt_text, max_length, top_p, temperature, st.session_state["state"])
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