mirror of https://github.com/THUDM/ChatGLM-6B
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50 lines
1.3 KiB
50 lines
1.3 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, 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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response, history = model.chat(tokenizer, input, history) |
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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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return history |
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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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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, st.session_state["state"]) |
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st.balloons() |