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


class LocalProvider(AIProvider):
    def __init__(self, model_url: str = "http://localhost:8001", model_name: str = "gemma-3-8b"):
        self.model_url = model_url.rstrip("/")
        self.model_name = model_name
        self._name = f"local-{model_name}"

    async def translate(self, text: str, source_lang: Optional[str], target_lang: str, context: Optional[str] = None) -> Dict[str, Any]:
        prompt = f"Translate{ f' from {source_lang}' if source_lang else ''} to {target_lang}:\n{text}\n\nTranslation:"
        result = await self._generate(prompt)
        return {"translated_text": result, "provider": self.name, "cost": 0.0}

    async def reply(self, inquiry: str, context: Optional[Dict[str, Any]] = None, tone: str = "professional", preference_context: Optional[str] = None) -> Dict[str, Any]:
        prompt = ""
        if preference_context:
            prompt += f"[User prefers: {preference_context}]\n"
        if context:
            prompt += "\n".join(f"{k}: {v}" for k, v in context.items() if v) + "\n"
        prompt += f"Customer: {inquiry}\n\nWrite a {tone} reply:"
        result = await self._generate(prompt)
        return {"reply": result, "provider": self.name, "cost": 0.0}

    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]:
        info = json.dumps(product_info, ensure_ascii=False)
        prompt = ""
        if preference_context:
            prompt += f"[User prefers: {preference_context}]\n"
        prompt += f"Product: {info}\nTarget: {target}\nStyle: {style}\nLanguage: {language}\n\nMarketing copy:"
        result = await self._generate(prompt, max_tokens=800)
        return {"content": result, "provider": self.name, "cost": 0.0}

    async def extract_info(self, text: str, schema: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"Extract JSON from text matching schema:\nSchema: {json.dumps(schema)}\n\nText: {text}\n\nJSON:"
        result = await self._generate(prompt, max_tokens=500)
        try:
            return {"data": json.loads(result), "confidence": 0.7, "provider": self.name, "cost": 0.0}
        except json.JSONDecodeError:
            return {"data": {}, "confidence": 0.0, "provider": self.name, "cost": 0.0, "error": "parse_failed"}

    async def _generate(self, prompt: str, max_tokens: int = 500) -> str:
        async with httpx.AsyncClient() as client:
            resp = await client.post(
                f"{self.model_url}/v1/completions",
                json={"model": self.model_name, "prompt": prompt, "max_tokens": max_tokens, "temperature": 0.7, "stream": False},
                timeout=60,
            )
            resp.raise_for_status()
            return resp.json()["choices"][0]["text"].strip()

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

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