import base64
import json
from openai import AsyncOpenAI

from app.ai.base import VisionProvider, TextProvider, VisionResult, DateResult, LeafletResult
from app.config import settings


class OpenAIVisionProvider(VisionProvider):
    def __init__(self):
        self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
        self.model = settings.OPENAI_MODEL

    async def recognize_medicine(self, image_bytes: bytes) -> VisionResult:
        base64_image = base64.b64encode(image_bytes).decode('utf-8')

        response = await self.client.chat.completions.create(
            model=self.model,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": """请识别这张药品包装图片中的信息，返回JSON格式：
{
    "generic_name": "通用名称",
    "brand_name": "商品名称",
    "manufacturer": "生产厂家",
    "specification": "规格"
}
只提取图片中真实出现的内容，不要猜测。"""
                        },
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{base64_image}"
                            }
                        }
                    ]
                }
            ],
            response_format={"type": "json_object"}
        )

        result = json.loads(response.choices[0].message.content)
        return VisionResult(**result)

    async def recognize_dates(self, image_bytes: bytes) -> DateResult:
        base64_image = base64.b64encode(image_bytes).decode('utf-8')

        response = await self.client.chat.completions.create(
            model=self.model,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": """请识别这张图片中的日期信息，返回JSON格式：
{
    "production_date": "生产日期(YYYY-MM-DD格式，如果无法识别则为null)",
    "expiry_date": "有效期/过期日期(YYYY-MM-DD格式，如果无法识别则为null)"
}
只提取图片中真实出现的日期，不要猜测或推理。"""
                        },
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{base64_image}"
                            }
                        }
                    ]
                }
            ],
            response_format={"type": "json_object"}
        )

        result = json.loads(response.choices[0].message.content)
        return DateResult(**result)


class OpenAITextProvider(TextProvider):
    def __init__(self):
        self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
        self.model = "gpt-4"

    async def summarize_leaflet(self, text: str) -> LeafletResult:
        response = await self.client.chat.completions.create(
            model=self.model,
            messages=[
                {
                    "role": "system",
                    "content": "你是一个医疗信息提取助手。请从药品说明书中提取关键信息。"
                },
                {
                    "role": "user",
                    "content": f"""请从以下药品说明书中提取关键信息，返回JSON格式：
{{
    "indications": "适应症",
    "adult_dose": "成人用法用量",
    "child_dose": "儿童用法用量(如果没有则为null)",
    "contraindications": "禁忌",
    "notes": "注意事项(如果有)"
}}

说明书内容：
{text}"""
                }
            ],
            response_format={"type": "json_object"}
        )

        result = json.loads(response.choices[0].message.content)
        return LeafletResult(**result)

    async def natural_language_search(self, query: str, medicines: list) -> list:
        medicines_text = "\n".join([
            f"- {m['name']}: {m.get('indications', '')}"
            for m in medicines
        ])

        response = await self.client.chat.completions.create(
            model=self.model,
            messages=[
                {
                    "role": "system",
                    "content": "你是一个药品搜索助手。根据用户描述的症状，从药品列表中找出可能适用的药品。"
                },
                {
                    "role": "user",
                    "content": f"""用户描述：{query}

可用药品列表：
{medicines_text}

请返回JSON格式的搜索结果：
{{
    "results": [
        {{
            "medicine_id": 药品ID,
            "name": "药品名称",
            "reason": "匹配原因"
        }}
    ]
}}"""
                }
            ],
            response_format={"type": "json_object"}
        )

        result = json.loads(response.choices[0].message.content)
        return result.get('results', [])