"""Streamlit 演示应用 学生成绩预测 AI 助手 - 支持成绩预测分析和心理咨询对话。 """ import asyncio import os import sys import streamlit as st # Ensure project root is in path sys.path.append(os.getcwd()) from dotenv import load_dotenv from pydantic_ai import FunctionToolCallEvent, FunctionToolResultEvent, PartDeltaEvent from pydantic_ai.messages import ( ModelRequest, ModelResponse, TextPart, TextPartDelta, UserPromptPart, ) from src.agent_app import AgentDeps, counselor_agent, study_advisor from src.features import StudentFeatures # Load env variables load_dotenv() st.set_page_config(page_title="学生成绩预测 AI 助手", page_icon="🎓", layout="wide") # Sidebar Configuration st.sidebar.header("🔧 配置") api_key = st.sidebar.text_input( "DeepSeek API Key", type="password", value=os.getenv("DEEPSEEK_API_KEY", "") ) if api_key: os.environ["DEEPSEEK_API_KEY"] = api_key st.sidebar.markdown("---") # Mode Selection mode = st.sidebar.radio("功能选择", ["📊 成绩预测", "💬 心理咨询"]) # --- Helper Functions --- async def run_analysis( study_hours: float, sleep_hours: float, attendance_rate: float, stress_level: int, study_type: str, ): """运行成绩预测分析""" try: if not os.getenv("DEEPSEEK_API_KEY"): st.error("请在侧边栏提供 DeepSeek API Key。") return None # 创建学生特征 student = StudentFeatures( study_hours=study_hours, sleep_hours=sleep_hours, attendance_rate=attendance_rate, stress_level=stress_level, study_type=study_type, ) # 创建依赖 deps = AgentDeps(student=student) # 构建查询 query = ( f"请分析这位学生的通过率并给出建议。" f"学生信息已通过工具获取，请调用 predict_pass_probability 和 get_model_explanation 工具。" ) with st.spinner("🤖 Agent 正在思考... (调用 DeepSeek + 随机森林模型)"): result = await study_advisor.run(query, deps=deps) return result.output except Exception as e: st.error(f"分析失败: {e!s}") return None async def run_counselor_stream( query: str, history: list, placeholder, student: StudentFeatures, ): """ 运行咨询师对话流，手动处理流式响应和工具调用事件。 """ try: if not os.getenv("DEEPSEEK_API_KEY"): placeholder.error("❌ 错误: 请在侧边栏提供 DeepSeek API Key。") return None # 创建依赖 deps = AgentDeps(student=student) full_response = "" # Status container for tool calls status_placeholder = st.empty() # Call Counselor Agent with streaming async for event in counselor_agent.run_stream_events(query, deps=deps, message_history=history): # Handle Text Delta (Wrapped in PartDeltaEvent) if isinstance(event, PartDeltaEvent) and isinstance(event.delta, TextPartDelta): full_response += event.delta.content_delta placeholder.markdown(full_response + "▌") # Handle Tool Call Start elif isinstance(event, FunctionToolCallEvent): status_placeholder.info(f"🛠️ 咨询师正在使用工具: `{event.part.tool_name}` ...") # Handle Tool Result elif isinstance(event, FunctionToolResultEvent): status_placeholder.empty() placeholder.markdown(full_response) status_placeholder.empty() # Ensure clear return full_response except Exception as e: placeholder.error(f"❌ 咨询失败: {e!s}") return None # --- Main Views --- if mode == "📊 成绩预测": st.title("🎓 学生成绩预测助手") st.markdown("在下方输入学生详细信息，获取 AI 驱动的成绩分析。") with st.form("student_data_form"): col1, col2 = st.columns(2) with col1: study_hours = st.slider("每周学习时长 (小时)", 0.0, 20.0, 10.0, 0.5) sleep_hours = st.slider("日均睡眠时长 (小时)", 0.0, 12.0, 7.0, 0.5) with col2: attendance_rate = st.slider("出勤率", 0.0, 1.0, 0.9, 0.05) stress_level = st.select_slider( "压力等级 (1=低, 5=高)", options=[1, 2, 3, 4, 5], value=3 ) study_type = st.radio("主要学习方式", ["Self", "Group", "Online"], horizontal=True) submitted = st.form_submit_button("🚀 分析通过率") if submitted: loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) guidance = loop.run_until_complete( run_analysis(study_hours, sleep_hours, attendance_rate, stress_level, study_type) ) if guidance: st.divider() st.subheader("📊 分析结果") m1, m2, m3 = st.columns(3) m1.metric("预测通过率", f"{guidance.pass_probability:.1%}") m2.metric( "风险评估", "高风险" if guidance.pass_probability < 0.6 else "低风险", delta="-高风险" if guidance.pass_probability < 0.6 else "+安全", ) st.info(f"**风险评估:** {guidance.risk_assessment}") # 显示关键因素 st.subheader("🔍 关键因素") for factor in guidance.key_factors: st.write(f"- {factor}") st.subheader("✅ 行动计划") actions = [ {"优先级": item.priority, "建议行动": item.action} for item in guidance.action_plan ] st.table(actions) st.subheader("💡 分析依据") st.write(guidance.rationale) elif mode == "💬 心理咨询": st.title("🧩 AI 心理咨询室") 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。") # Sidebar for student info (optional for counselor context) with st.sidebar.expander("📝 学生信息 (可选)", expanded=False): c_study_hours = st.slider("每周学习时长", 0.0, 20.0, 10.0, 0.5, key="c_study") c_sleep_hours = st.slider("日均睡眠时长", 0.0, 12.0, 7.0, 0.5, key="c_sleep") c_attendance = st.slider("出勤率", 0.0, 1.0, 0.9, 0.05, key="c_att") c_stress = st.select_slider("压力等级", options=[1, 2, 3, 4, 5], value=3, key="c_stress") c_study_type = st.radio("学习方式", ["Self", "Group", "Online"], key="c_type") # Initialize chat history if "messages" not in st.session_state: st.session_state.messages = [] # Display chat messages from history on app rerun for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # React to user input if prompt := st.chat_input("想聊聊什么？"): # Display user message with st.chat_message("user"): st.markdown(prompt) # Add user message to history st.session_state.messages.append({"role": "user", "content": prompt}) # Prepare history for pydantic-ai api_history = [] for msg in st.session_state.messages[:-1]: if msg["role"] == "user": api_history.append(ModelRequest(parts=[UserPromptPart(content=msg["content"])])) elif msg["role"] == "assistant": api_history.append(ModelResponse(parts=[TextPart(content=msg["content"])])) # Create student features for counselor context student = StudentFeatures( study_hours=c_study_hours, sleep_hours=c_sleep_hours, attendance_rate=c_attendance, stress_level=c_stress, study_type=c_study_type, ) # Generate response with st.chat_message("assistant"): placeholder = st.empty() with st.spinner("咨询师正在倾听..."): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) # Run the manual streaming function response_text = loop.run_until_complete( run_counselor_stream(prompt, api_history, placeholder, student) ) if response_text: st.session_state.messages.append( {"role": "assistant", "content": response_text} )