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@ -1,6 +1,5 @@ |
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from transformers import AutoModel, AutoTokenizer |
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from transformers import AutoModel, AutoTokenizer |
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import streamlit as st |
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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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st.set_page_config( |
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@ -21,40 +20,9 @@ def get_model(): |
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return tokenizer, model |
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return tokenizer, model |
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MAX_TURNS = 20 |
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tokenizer, model = get_model() |
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MAX_BOXES = MAX_TURNS * 2 |
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st.title("ChatGLM2-6B") |
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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 = st.sidebar.slider( |
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'max_length', 0, 32768, 8192, step=1 |
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'max_length', 0, 32768, 8192, step=1 |
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@ -63,13 +31,40 @@ top_p = st.sidebar.slider( |
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'top_p', 0.0, 1.0, 0.8, step=0.01 |
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'top_p', 0.0, 1.0, 0.8, step=0.01 |
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) |
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) |
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temperature = st.sidebar.slider( |
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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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'temperature', 0.0, 1.0, 0.8, step=0.01 |
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) |
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) |
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if 'state' not in st.session_state: |
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if 'history' not in st.session_state: |
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st.session_state['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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for i, (query, response) in enumerate(st.session_state.history): |
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with st.chat_message(name="user", avatar="user"): |
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st.markdown(query) |
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with st.chat_message(name="assistant", avatar="assistant"): |
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st.markdown(response) |
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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.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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button = st.button("发送", key="predict") |
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if button: |
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input_placeholder.markdown(prompt_text) |
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history, past_key_values = st.session_state.history, st.session_state.past_key_values |
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for response, history, past_key_values in model.stream_chat(tokenizer, prompt_text, history, |
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past_key_values=past_key_values, |
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max_length=max_length, top_p=top_p, |
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temperature=temperature, |
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return_past_key_values=True): |
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message_placeholder.markdown(response) |
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if st.button("发送", key="predict"): |
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st.session_state.history = history |
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with st.spinner("AI正在思考,请稍等........"): |
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st.session_state.past_key_values = past_key_values |
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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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