Create app.py

app.py

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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,
+it is recommended to use 'composite_demo'.
+
+Usage:
+- Run the script using Streamlit: `streamlit run web_demo_streamlit.py`
+- Adjust the model parameters from the sidebar.
+- Enter questions in the chat input box and interact with the ChatGLM3-6B model.
+
+Note: Ensure 'streamlit' and 'transformers' libraries are installed and the required model checkpoints are available.
+"""
+
+import os
+import streamlit as st
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
+TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
+
+st.set_page_config(
+    page_title="ChatGLM3-6B Streamlit Simple Demo",
+    page_icon=":robot:",
+    layout="wide"
+)
+
+
+@st.cache_resource
+def get_model():
+
+    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
+    model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True, device_map="auto").eval()
+    return tokenizer, model
+
+
+# 加载Chatglm3的model和tokenizer
+tokenizer, model = get_model()
+
+if "history" not in st.session_state:
+    st.session_state.history = []
+if "past_key_values" not in st.session_state:
+    st.session_state.past_key_values = None
+
+max_length = st.sidebar.slider("max_length", 0, 32768, 8192, step=1)
+top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
+temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.6, step=0.01)
+
+buttonClean = st.sidebar.button("清理会话历史", key="clean")
+if buttonClean:
+    st.session_state.history = []
+    st.session_state.past_key_values = None
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    st.rerun()
+
+for i, message in enumerate(st.session_state.history):
+    if message["role"] == "user":
+        with st.chat_message(name="user", avatar="user"):
+            st.markdown(message["content"])
+    else:
+        with st.chat_message(name="assistant", avatar="assistant"):
+            st.markdown(message["content"])
+
+with st.chat_message(name="user", avatar="user"):
+    input_placeholder = st.empty()
+with st.chat_message(name="assistant", avatar="assistant"):
+    message_placeholder = st.empty()
+
+prompt_text = st.chat_input("请输入您的问题")
+if prompt_text:
+    input_placeholder.markdown(prompt_text)
+    history = st.session_state.history
+    past_key_values = st.session_state.past_key_values
+    for response, history, past_key_values in model.stream_chat(
+            tokenizer,
+            prompt_text,
+            history,
+            past_key_values=past_key_values,
+            max_length=max_length,
+            top_p=top_p,
+            temperature=temperature,
+            return_past_key_values=True,
+    ):
+        message_placeholder.markdown(response)
+    st.session_state.history = history
+    st.session_state.past_key_values = past_key_values