from typing import Dict, Any, Optional
import json
from app.ai.base import AIProvider


SYSTEM_PROMPTS = {
    "translate": "You are a professional translator specialized in foreign trade. "
                 "Translate business terms accurately. Return ONLY the translated text.",
    "reply": "You are an experienced foreign trade sales expert. Write professional, "
             "clear business replies. Return ONLY the reply text.",
    "marketing": "You are a creative copywriter for international trade. "
                 "Return ONLY the marketing copy, no explanations.",
    "extract": "Extract structured data from text. Return ONLY valid JSON.",
}


class SparkProvider(AIProvider):
    def __init__(self, api_key: str, model: str = "astron-code-latest", base_url: str = None):
        from app.config import settings
        try:
            from openai import AsyncOpenAI
        except ImportError:
            raise ImportError("openai>=1.0 is required for SparkProvider")
        self.client = AsyncOpenAI(
            api_key=api_key,
            base_url=base_url or settings.IFLYTEK_API_BASE,
        )
        self.model = model
        self._name = f"spark-{model}"

    async def translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
        system = SYSTEM_PROMPTS["translate"]
        if context:
            system += f"\nContext: {context}"
        prompt = f"Translate {f'from {source_lang} ' if source_lang and source_lang != 'auto' else ''}to {target_lang}:\n\n{text}"
        content = await self._call(system, prompt)
        return {"translated_text": content, "provider": self.name}

    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
        system = SYSTEM_PROMPTS["reply"] + f"\nTone: {tone}"
        if preference_context:
            system += f"\nUser preference: {preference_context}"
        ctx = ""
        if context:
            ctx = "\n".join(f"{k}: {v}" for k, v in context.items() if v)
        prompt = f"{ctx}\nCustomer inquiry:\n{inquiry}\n\nWrite a reply:"
        content = await self._call(system, prompt)
        return {"reply": content, "provider": self.name}

    async def generate_marketing(self, product_info: Dict[str, Any], target: str, style: str = "professional", language: str = "en", preference_context: Optional[str] = None) -> Dict[str, Any]:
        system = SYSTEM_PROMPTS["marketing"] + f"\nStyle: {style}\nAudience: {target}\nLanguage: {language}"
        if preference_context:
            system += f"\nUser preference: {preference_context}"
        info = json.dumps(product_info, ensure_ascii=False)
        prompt = f"Product:\n{info}\n\nGenerate marketing copy:"
        content = await self._call(system, prompt, max_tokens=1500)
        return {"content": content, "provider": self.name}

    async def extract_info(self, text: str, schema: Dict[str, Any]) -> Dict[str, Any]:
        system = SYSTEM_PROMPTS["extract"]
        prompt = f"Schema:\n{json.dumps(schema, indent=2)}\n\nText:\n{text}\n\nJSON:"
        content = await self._call(system, prompt, response_format={"type": "json_object"})
        try:
            data = json.loads(content)
            return {"data": data, "confidence": 0.9, "provider": self.name}
        except json.JSONDecodeError:
            return {"data": {}, "confidence": 0.0, "provider": self.name}

    async def _call(self, system: str, prompt: str, max_tokens: int = 1000, response_format: Optional[Dict] = None) -> str:
        kwargs = {
            "model": self.model,
            "messages": [
                {"role": "system", "content": system},
                {"role": "user", "content": prompt},
            ],
            "max_tokens": max_tokens,
            "temperature": 0.7,
        }
        if response_format:
            kwargs["response_format"] = response_format
        resp = await self.client.chat.completions.create(**kwargs)
        return resp.choices[0].message.content

    @property
    def name(self) -> str:
        return self._name

    @property
    def cost_per_1k_tokens(self) -> float:
        return 0.0