186 lines
7.3 KiB
Python
186 lines
7.3 KiB
Python
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import streamlit as st
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import asyncio
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import os
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import sys
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# Ensure project root is in path
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sys.path.append(os.getcwd())
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from src.agent_app import agent, counselor_agent, StudyGuidance
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from pydantic_ai.messages import (
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ModelMessage, ModelRequest, ModelResponse, UserPromptPart, TextPart,
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TextPartDelta, ToolCallPart, ToolReturnPart
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)
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from pydantic_ai import (
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AgentStreamEvent, PartDeltaEvent, FunctionToolCallEvent, FunctionToolResultEvent
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)
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from dotenv import load_dotenv
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# Load env variables
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load_dotenv()
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st.set_page_config(
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page_title="学生成绩预测 AI 助手",
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page_icon="🎓",
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layout="wide"
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)
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# Sidebar Configuration
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st.sidebar.header("🔧 配置")
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api_key = st.sidebar.text_input("DeepSeek API Key", type="password", value=os.getenv("DEEPSEEK_API_KEY", ""))
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if api_key:
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os.environ["DEEPSEEK_API_KEY"] = api_key
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st.sidebar.markdown("---")
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# Mode Selection
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mode = st.sidebar.radio("功能选择", ["📊 成绩预测", "💬 心理咨询"])
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# --- Helper Functions ---
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async def run_analysis(query):
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try:
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if not os.getenv("DEEPSEEK_API_KEY"):
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st.error("请在侧边栏提供 DeepSeek API Key。")
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return None
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with st.spinner("🤖 Agent 正在思考... (调用 DeepSeek + 随机森林模型)"):
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result = await agent.run(query)
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return result.output
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except Exception as e:
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st.error(f"分析失败: {str(e)}")
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return None
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async def run_counselor_stream(query, history, placeholder):
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"""
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Manually stream the response to a placeholder, handling tool events for visibility.
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"""
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try:
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if not os.getenv("DEEPSEEK_API_KEY"):
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placeholder.error("❌ 错误: 请在侧边栏提供 DeepSeek API Key。")
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return None
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full_response = ""
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# Status container for tool calls
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status_placeholder = st.empty()
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# Call Counselor Agent with streaming
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# Call Counselor Agent with streaming using run_stream_events which is the modern way to get events
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async for event in counselor_agent.run_stream_events(query, message_history=history):
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# Handle Text Delta (Wrapped in PartDeltaEvent)
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if isinstance(event, PartDeltaEvent) and isinstance(event.delta, TextPartDelta):
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full_response += event.delta.content_delta
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placeholder.markdown(full_response + "▌")
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# Handle Tool Call Start
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elif isinstance(event, FunctionToolCallEvent):
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# event.part is ToolCallPart usually, or event.tool_call
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# Check pydantic-ai docs structure: FunctionToolCallEvent has 'part' which is ToolCallPart
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status_placeholder.info(f"🛠️ 咨询师正在使用工具: `{event.part.tool_name}` ...")
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# Handle Tool Result
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elif isinstance(event, FunctionToolResultEvent):
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status_placeholder.empty()
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placeholder.markdown(full_response)
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status_placeholder.empty() # Ensure clear
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return full_response
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except Exception as e:
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placeholder.error(f"❌ 咨询失败: {str(e)}")
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return None
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# --- Main Views ---
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if mode == "📊 成绩预测":
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st.title("🎓 学生成绩预测助手")
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st.markdown("在下方输入学生详细信息，获取 AI 驱动的成绩分析。")
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with st.form("student_data_form"):
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col1, col2 = st.columns(2)
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with col1:
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study_hours = st.slider("每周学习时长 (小时)", 0.0, 20.0, 10.0, 0.5)
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sleep_hours = st.slider("日均睡眠时长 (小时)", 0.0, 12.0, 7.0, 0.5)
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with col2:
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attendance_rate = st.slider("出勤率", 0.0, 1.0, 0.9, 0.05)
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stress_level = st.select_slider("压力等级 (1=低, 5=高)", options=[1, 2, 3, 4, 5], value=3)
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study_type = st.radio("主要学习方式", ["Self", "Group", "Online"], horizontal=True)
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submitted = st.form_submit_button("🚀 分析通过率")
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if submitted:
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user_query = (
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f"我是一名学生，情况如下："
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f"每周学习时间: {study_hours} 小时；"
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f"平均睡眠时间: {sleep_hours} 小时；"
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f"出勤率: {attendance_rate:.2f}；"
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f"压力等级: {stress_level} (1-5)；"
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f"主要学习方式: {study_type}。"
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f"请调用 `predict_student` 预测我的通过率，并调用 `explain_model` 分析关键因素，最后给出针对性的建议。"
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)
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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guidance = loop.run_until_complete(run_analysis(user_query))
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if guidance:
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st.divider()
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st.subheader("📊 分析结果")
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m1, m2, m3 = st.columns(3)
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m1.metric("预测通过率", f"{guidance.pass_probability:.1%}")
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m2.metric("风险评估", "高风险" if guidance.pass_probability < 0.6 else "低风险",
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delta="-高风险" if guidance.pass_probability < 0.6 else "+安全")
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st.info(f"**风险评估:** {guidance.risk_assessment}")
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st.write(f"**关键因素:** {guidance.key_drivers}")
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st.subheader("✅ 行动计划")
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actions = [{"优先级": item.priority, "建议行动": item.action} for item in guidance.action_plan]
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st.table(actions)
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elif mode == "💬 心理咨询":
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st.title("🧩 AI 心理咨询室")
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st.markdown("这里是安全且私密的空间。有些压力如果你愿意说，我愿意听。")
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat messages from history on app rerun
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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.markdown(message["content"])
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# React to user input
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if prompt := st.chat_input("想聊聊什么？"):
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# Display user message
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with st.chat_message("user"):
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st.markdown(prompt)
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# Add user message to history
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st.session_state.messages.append({"role": "user", "content": prompt})
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# Prepare history for pydantic-ai
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# Convert Streamlit history to pydantic-ai ModelMessages
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# Note: We exclude the last message because `agent.run` takes the new prompt as argument
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api_history = []
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for msg in st.session_state.messages[:-1]:
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if msg["role"] == "user":
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api_history.append(ModelRequest(parts=[UserPromptPart(content=msg["content"])]))
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elif msg["role"] == "assistant":
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api_history.append(ModelResponse(parts=[TextPart(content=msg["content"])]))
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# Generate response
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with st.chat_message("assistant"):
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placeholder = st.empty()
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with st.spinner("咨询师正在倾听..."):
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# Run the manual streaming function
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response_text = loop.run_until_complete(run_counselor_stream(prompt, api_history, placeholder))
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if response_text:
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st.session_state.messages.append({"role": "assistant", "content": response_text})
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