src/integration_example.py

# integration_example.py
# 这个文件展示如何将你的MsPatient类集成到Streamlit应用中

import streamlit as st
import json
import pandas as pd
from datetime import datetime
import time
from pathlib import Path

# 导入你的AnnaAgent类 - 请根据实际路径调整
try:
    from ms_patient import MsPatient  # 假设你的类在anna_agent.py文件中
    ANNA_AGENT_AVAILABLE = True
except ImportError:
    ANNA_AGENT_AVAILABLE = False
    st.warning("⚠️ 未找到AnnaAgent类，使用模拟模式")

def load_dataset(uploaded_file):
    """
    加载数据集文件
    支持JSON和JSONL格式
    """
    try:
        if uploaded_file.name.endswith('.json'):
            data = json.load(uploaded_file)
        elif uploaded_file.name.endswith('.jsonl'):
            data = []
            for line in uploaded_file:
                data.append(json.loads(line.decode('utf-8')))
        else:
            raise ValueError("不支持的文件格式")
        return data
    except Exception as e:
        st.error(f"数据集加载失败: {str(e)}")
        return None

def validate_patient_data(patient_data):
    """
    验证患者数据格式是否正确
    """
    required_keys = ['id', 'portrait', 'report']
    
    for key in required_keys:
        if key not in patient_data:
            return False, f"缺少必需字段: {key}"
    
    # 验证portrait字段
    portrait_required = ['age', 'gender', 'occupation', 'marital_status']
    for key in portrait_required:
        if key not in patient_data['portrait']:
            return False, f"portrait中缺少字段: {key}"
    
    return True, "数据格式正确"

def initialize_patient_agent(patient_data, language="Chinese"):
    """
    初始化患者智能体
    """
    try:
        if not ANNA_AGENT_AVAILABLE:
            return None, "AnnaAgent类不可用"
        
        # 验证数据格式
        is_valid, message = validate_patient_data(patient_data)
        if not is_valid:
            return None, message
        
        # 初始化智能体
        agent = MsPatient(
            portrait=patient_data["portrait"],
            report=patient_data["report"],
            previous_conversations=patient_data.get("conversation", []),
            language=language
        )
        
        return agent, "初始化成功"
        
    except Exception as e:
        return None, f"初始化失败: {str(e)}"

def simulate_response(user_input, patient_data=None):
    """
    模拟智能体回复（当AnnaAgent不可用时使用）
    """
    responses = [
        f"我理解您提到的'{user_input}'。这确实是一个需要深入探讨的话题。",
        f"谢谢您的耐心。关于您说的'{user_input}'，我想分享一下我的感受...",
        f"您的话让我思考了很多。'{user_input}'这个观点很有意思。",
        "我需要一些时间来消化您刚才说的话。这对我来说很重要。",
        "我觉得我们之间的对话很有帮助。您能再详细说说吗？"
    ]
    
    import random
    return random.choice(responses)

def export_chat_history(messages, patient_id):
    """
    导出聊天记录
    """
    chat_history = {
        "patient_id": patient_id,
        "timestamp": datetime.now().isoformat(),
        "session_info": {
            "total_messages": len(messages),
            "counselor_messages": len([m for m in messages if m["role"] == "user"]),
            "patient_responses": len([m for m in messages if m["role"] == "assistant"])
        },
        "messages": messages
    }
    
    return json.dumps(chat_history, ensure_ascii=False, indent=2)

def get_patient_summary(patient_data):
    """
    生成患者信息摘要
    """
    if not patient_data or 'portrait' not in patient_data:
        return "无患者信息"
    
    portrait = patient_data['portrait']
    summary = f"""
    **患者ID**: {patient_data.get('id', 'N/A')}
    **基本信息**: {portrait.get('age', 'N/A')}岁 {portrait.get('gender', 'N/A')}性
    **职业**: {portrait.get('occupation', 'N/A')}
    **婚姻状态**: {portrait.get('marital_status', 'N/A')}
    **主要症状**: {portrait.get('symptom', 'N/A')}
    """
    
    if 'report' in patient_data:
        report = patient_data['report']
        summary += f"""
    **主诉**: {report.get('chief_complaint', 'N/A')}
    """
    
    return summary

# 示例配置文件内容
CONFIG_EXAMPLE = {
    "openai": {
        "api_key": "your-api-key-here",
        "base_url": "https://api.openai.com/v1",
        "model_name": "gpt-3.5-turbo"
    },
    "ui_settings": {
        "language": "Chinese",  # or "English"
        "theme": "default",
        "max_messages": 100
    },
    "patient_defaults": {
        "language": "Chinese",
        "enable_memory": True,
        "enable_emotion_modulation": True
    }
}

def save_config(config, path="config.json"):
    """保存配置文件"""
    with open(path, 'w', encoding='utf-8') as f:
        json.dump(config, f, ensure_ascii=False, indent=2)

def load_config(path="config.json"):
    """加载配置文件"""
    try:
        with open(path, 'r', encoding='utf-8') as f:
            return json.load(f)
    except FileNotFoundError:
        return CONFIG_EXAMPLE

# 使用示例：
if __name__ == "__main__":
    print("这是AnnaAgent Streamlit集成的辅助文件")
    print("请运行：streamlit run your_streamlit_app.py")