57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
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import streamlit as st
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import os
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from dotenv import load_dotenv
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from DeepseekAi import get_response_stream # 导入流式函数
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# 第一步：加载.env配置（必须放在最前面）
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load_dotenv()
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API_KEY = os.getenv('api_key')
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BASE_URL = os.getenv('base_url')
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# 检查配置完整性
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if not API_KEY or not BASE_URL:
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st.error("❌ 配置缺失：请检查.env文件中的 api_key 和 base_url 是否填写正确！")
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st.stop()
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# 页面标题
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st.title("智聊机器人")
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# 初始化会话状态
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if "messages" not in st.session_state:
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st.session_state["messages"] = [{"role": "assistant", "content": "你好，我是智聊机器人，有什么可以帮助你的么？"}]
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# 显示历史对话
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.write(message["content"])
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# 处理用户输入
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user_input = st.chat_input("请输入你的问题：")
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if user_input:
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# 1. 添加用户消息到会话状态并显示
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st.session_state.messages.append({"role": "user", "content": user_input})
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with st.chat_message("user"):
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st.write(user_input)
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# 2. 流式输出AI回复
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full_response = "" # 存储完整回复
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with st.chat_message("assistant"):
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# 初始化思考状态（赋值给status变量，用于后续修改）
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with st.status(label="🤔 正在思考中...", expanded=True) as status:
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response_placeholder = st.empty() # 只创建一次占位符
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# 逐段获取流式内容
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for chunk in get_response_stream(st.session_state.messages, api_key=API_KEY, base_url=BASE_URL):
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full_response += chunk
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# 实时更新占位符，加光标模拟打字效果
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response_placeholder.markdown(full_response + "|")
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# 核心修改：思考完成后更新状态文本
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status.update(label="✅ 思考结束", state="complete", expanded=False)
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# 3. 最终展示完整回复（保留你原有逻辑，未修改DOM渲染）
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st.markdown(full_response)
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# 4. 把完整回复存入会话状态
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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