mirror of https://github.com/THUDM/ChatGLM-6B
60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
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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with container:
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if len(history) > 0:
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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):
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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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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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