import gradio as gr
import asyncio
import os
import sys

# 添加项目路径到 sys.path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

# 导入天气助手模块
from weather_agent import get_weather, get_datetime, ToolContext, ToolResult

# 创建一个简单的聊天机器人接口
class WeatherChatbot:
    def __init__(self):
        self.history = []
    
    async def respond(self, message, history):
        """处理用户消息并返回响应"""
        # 添加用户消息到历史记录
        history.append((message, None))
        
        # 创建工具上下文
        context = ToolContext()
        
        # 检查消息是否与天气相关
        if any(keyword in message.lower() for keyword in ["天气", "weather", "温度", "气温"]):
            # 提取城市名称（简化版）
            cities = ["北京", "上海", "广州", "深圳", "纽约", "伦敦", "东京", "巴黎"]
            city = "北京"  # 默认城市
            
            # 简单的城市识别
            for c in cities:
                if c in message:
                    city = c
                    break
            
            # 调用天气工具
            try:
                weather_result = await get_weather(context, city)
                response = f"🌤️ {weather_result.content}"
                
                # 添加一些建议
                if "晴" in response or "Sunny" in response:
                    response += "\n\n👕 今天天气很好，适合户外活动！记得涂防晒霜。"
                elif "雨" in response or "Rainy" in response:
                    response += "\n\n☔ 今天有雨，出门记得带伞。"
                elif "云" in response or "Cloudy" in response:
                    response += "\n\n☁️ 今天多云，温度适中，适合外出。"
                    
            except Exception as e:
                response = f"抱歉，获取天气信息时出现错误: {str(e)}"
                
        elif any(keyword in message.lower() for keyword in ["时间", "日期", "datetime", "time"]):
            # 调用时间工具
            try:
                time_result = await get_datetime(context)
                response = f"⏰ 当前时间: {time_result.content}"
            except Exception as e:
                response = f"抱歉，获取时间信息时出现错误: {str(e)}"
                
        else:
            # 默认响应
            responses = [
                "你好！我可以帮你查询天气和时间信息。你可以问我'北京的天气怎么样？'或'现在几点了？'",
                "我是天气助手，可以为你提供天气和时间信息。有什么我可以帮你的吗？",
                "想了解某个城市的天气情况吗？或者需要知道当前时间？尽管问我吧！"
            ]
            import random
            response = random.choice(responses)
        
        # 更新历史记录
        history[-1] = (history[-1][0], response)
        
        return "", history

# 创建 Gradio 界面
def create_weather_interface():
    chatbot = WeatherChatbot()
    
    with gr.Blocks(title="天气助手") as demo:
        gr.Markdown("# 🌤️ 天气助手")
        gr.Markdown("我可以帮你查询天气和时间信息。试试问我：'北京的天气怎么样？' 或 '现在几点了？'")
        
        # 聊天历史显示区域
        chat_history = gr.Chatbot(
            label="对话记录"
        )
        
        # 输入区域
        with gr.Row():
            user_input = gr.Textbox(
                label="输入消息",
                placeholder="例如：北京的天气怎么样？",
                lines=2
            )
            send_btn = gr.Button("发送 📤", variant="primary")
        
        # 功能按钮
        with gr.Row():
            clear_btn = gr.Button("清空聊天记录 🗑️")
            greet_btn = gr.Button("打招呼 👋")
        
        # 状态显示
        status = gr.Textbox(label="状态", interactive=False)
        
        # 事件处理函数
        async def respond_wrapper(message, history):
            return await chatbot.respond(message, history)
        
        def clear_history():
            return [], "聊天记录已清空"
        
        def greet():
            greeting = "你好！我是天气助手，可以帮你查询天气和时间信息。有什么我可以帮你的吗？"
            return "", [("助手", greeting)], f"助手说: {greeting}"
        
        # 绑定事件
        send_btn.click(
            respond_wrapper,
            inputs=[user_input, chat_history],
            outputs=[user_input, chat_history]
        )
        
        user_input.submit(
            respond_wrapper,
            inputs=[user_input, chat_history],
            outputs=[user_input, chat_history]
        )
        
        clear_btn.click(
            clear_history,
            outputs=[chat_history, status]
        )
        
        greet_btn.click(
            greet,
            outputs=[user_input, chat_history, status]
        )
    
    return demo

# 主函数
# 注意：在Hugging Face Space中，这个模块不应该独立启动