# 定義一個天氣查詢函數
def get_weather(location, date):
    """查詢指定地點和日期的天氣


    參數:
        location (str): 城市名稱
        date (str): 日期，格式為YYYY-MM-DD


    返回:
        dict: 包含天氣信息的字典
    """
    # 實際應用中，這里會調用天氣API
    import requests


    api_url = f"https://weather-api.example.com/forecast?location={location}&date={date}"
    response = requests.get(api_url)


    if response.status_code == 200:
        return response.json()
    else:
        return {"error": "無法獲取天氣信息"}


# DeepSeek如何使用Function Call
def deepseek_with_function_call(user_query):
    # 1. 模型理解用戶查詢天氣的意圖
    if "天氣" in user_query:
        # 2. 提取地點和時間信息
        location = extract_location(user_query)  # 假設這個函數能提取位置
        date = extract_date(user_query)  # 假設這個函數能提取日期


        # 3. 調用天氣函數
        weather_data = get_weather(location, date)


        # 4. 格式化回復
        response = f"{location}{date}天氣為{weather_data['condition']}，"
        response += f"溫度在{weather_data['min_temp']}°C至{weather_data['max_temp']}°C之間，"
        response += f"降水概率{weather_data['precipitation_chance']}%。"


        return response


    # 其他類型的查詢...
    return "我無法理解您的問題。"

# 商品搜索函數
def search_products(product_type, color=None, max_price=None, min_price=None):
    """搜索符合條件的商品


    參數:
        product_type (str): 商品類型
        color (str, optional): 顏色
        max_price (float, optional): 最高價格
        min_price (float, optional): 最低價格


    返回:
        list: 符合條件的商品列表
    """
    # 實際中會調用電商平臺API
    # 這里用模擬數據演示
    all_products = [
        {"id": 1, "name": "夏季紅色連衣裙", "color": "紅色", "price": 259.00},
        {"id": 2, "name": "優雅紅色長裙", "color": "紅色", "price": 329.00},
        {"id": 3, "name": "簡約紅色連衣裙", "color": "紅色", "price": 199.00}
    ]


    results = []
    for product in all_products:
        if product_type in product["name"] and \
           (color is None or product["color"] == color) and \
           (max_price is None or product["price"] <= max_price) and \
           (min_price is None or product["price"] >= min_price):
            results.append(product)


    return results


# DeepSeek處理購物查詢
def handle_shopping_query(query):
    # 提取查詢信息
    if "連衣裙" in query and "紅色" in query:
        # 解析價格條件
        max_price = 300  # 從查詢中提取"低于300元"


        # 調用商品搜索函數
        products = search_products("連衣裙", color="紅色", max_price=max_price)


        # 格式化回復
        if products:
            response = f"我找到了{len(products)}款符合條件的紅色連衣裙：\n"
            for p in products:
                response += f"- {p['name']}，售價{p['price']}元\n"
        else:
            response = "抱歉，沒有找到符合條件的商品。"


        return response

# 注冊可用函數
available_functions = {
    "get_weather": {
        "description": "獲取指定地點和日期的天氣信息",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市名稱，如'北京'、'上海'"
                },
                "date": {
                    "type": "string",
                    "description": "日期，格式為YYYY-MM-DD"
                }
            },
            "required": ["location"]
        }
    },
    "search_products": {
        "description": "搜索符合條件的商品",
        "parameters": {
            "type": "object",
            "properties": {
                "product_type": {
                    "type": "string",
                    "description": "商品類型，如'連衣裙'、'手機'"
                },
                "color": {
                    "type": "string",
                    "description": "商品顏色"
                },
                "max_price": {
                    "type": "number",
                    "description": "最高價格"
                }
            },
            "required": ["product_type"]
        }
    }
}

def process_user_query(user_query):
    # 模擬DeepSeek分析用戶意圖的過程


    # 假設這是模型的思考過程
    """
    用戶問"上海明天天氣怎么樣"，明顯是查詢天氣信息。
    需要的信息：城市=上海，日期=明天(2025-02-27)
    應該調用get_weather函數
    """


    # 模型決定調用get_weather函數
    function_call = {
        "name": "get_weather",
        "arguments": {
            "location": "上海",
            "date": "2025-02-27"
        }
    }


    return function_call

def deepseek_complete_response(user_query):
# 1. 確定需要調用的函數
    function_call = process_user_query(user_query)


# 2. 執行函數調用
if function_call["name"] == "get_weather":
        args = function_call["arguments"]
        weather_data = get_weather(args["location"], args["date"])


# 3. 將函數結果融入自然語言回答
        response = f"{args['location']}在{args['date']}的天氣預報：\n"
        response += f"天氣狀況: {weather_data['condition']}\n"
        response += f"溫度范圍: {weather_data['min_temp']}°C - {weather_data['max_temp']}°C\n"
        response += f"降水概率: {weather_data['precipitation_chance']}%\n"


if weather_data['precipitation_chance'] > 50:
            response += "建議您出門攜帶雨傘！"


return response


# 其他函數調用類似處理...

# 傳統API調用 - 開發者必須硬編碼邏輯
def traditional_weather_app(user_input):
    if "天氣" in user_input and extract_location(user_input):
        location = extract_location(user_input)
        date = extract_date(user_input) or "today"
        try:
            weather_data = weather_api.get_forecast(location, date)
            return format_weather_response(weather_data)
        except ApiError as e:
            return f"獲取天氣失敗: {e}"
    else:
        return "我不理解您的問題"


# Function Call - 模型自主決策
def function_call_assistant(user_input):
    # 模型分析輸入，自主決定是否需要天氣信息
    function_decision = model.analyze(user_input)


    if function_decision.needs_function_call:
        # 模型自動生成結構化參數
        function_name = function_decision.function_name  # 可能是"get_weather"或其他
        function_args = function_decision.arguments  # 模型提取的參數


        try:
            result = call_function(function_name, function_args)
        except Exception as e:
            # 模型能理解錯誤并嘗試修正參數后重試
            corrected_args = model.correct_arguments(function_args, error=str(e))
            result = call_function(function_name, corrected_args)


        # 模型融合函數結果生成回答
        return model.generate_response(user_input, function_result=result)
    else:
        # 模型判斷不需要調用外部函數
        return model.generate_response(user_input)