import os
from src.clients.llm_client import LLMClient
import json 
import pandas as pd
from pydantic import BaseModel, Field
from enum import Enum
import base64
from io import BytesIO
from PIL import Image
from typing import List, Optional
from functools import cache
from datetime import datetime
import pytz
from src.utils.tracer import customtracer
from src.models.common import model

def _ask_raw_hf(messages, model, response_format=None):
    """Compatibility wrapper: routes OpenAI-style messages through HF LLMClient."""
    from src.clients.llm_client import LLMClient
    import json as _json

    client = LLMClient()
    system_prompt = None
    user_text = ""
    images = []
    for msg in messages:
        role = msg.get("role", "")
        c = msg.get("content", "")
        if role == "system":
            if isinstance(c, str):
                system_prompt = c
        elif role == "user":
            if isinstance(c, str):
                user_text = c
            elif isinstance(c, list):
                for part in c:
                    if isinstance(part, dict):
                        if part.get("type") == "text":
                            user_text += part.get("text", "")
                        elif part.get("type") == "image_url":
                            url = part.get("image_url", {}).get("url", "")
                            if url.startswith("data:"):
                                images.append(url.split(",", 1)[1] if "," in url else url)
                            else:
                                images.append(url)

    if response_format is not None and hasattr(response_format, "model_json_schema"):
        result = client.call(
            prompt=user_text,
            schema=response_format,
            model=model,
            system_prompt=system_prompt,
            images=images if images else None,
            temperature=0,
        )
        return _json.dumps(result.model_dump(), ensure_ascii=False)
    else:
        return client.call_raw(
            prompt=user_text,
            model=model,
            system_prompt=system_prompt,
            images=images if images else None,
        )


"""
CN(コンチE��チE用の褁E��バリアント生成API�E�Eormat2cninfo.pyに依存しなぁE��立実裁E��E
"""

# スキーマ定義�E�Eormat2cninfo.pyから独立！E
class UIoption(str, Enum):
    element1 = "バナー/動画"
    element2 = "CTA"
    element3 = "チE��スチE
    element4 = "フォーム"

class Component(BaseModel):
    component_large: str
    component_middle: str
    component_small: list[str]
    UIelement: UIoption

class CNinfo(BaseModel):
    components: list[Component]


def get_openai_request(messages, format, n=1):
    """
    OpenAI API呼び出し！Eパラメータ対応、常にリストを返す�E�E
    
    Args:
        messages: メチE��ージリスチE
        format: レスポンスフォーマッチE
        n: 生�Eする候補数�E�デフォルチE 1�E�E
    
    Returns:
        list[str]: 常にリストで返却�E�E=1でも長ぁEのリスト！E
    """
    client = LLMClient())
    response = _ask_raw_hf([{"role":"user","content":p}], model,
        model="meta-llama/Llama-3.3-70B-Instruct",
        messages=messages,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
        response_format=format,
        temperature=0,
        n=n
    )
    
    # 常にリストで返す�E�E=1でも統一�E�E
    return [choice.message.content for choice in response.choices]


@customtracer
def format2cninfos(p, openai_key=os.environ.get('OPENAI_KEY'), n=1):
    """
    input1 (text): prompt text
    input2 (text): default
    input3 (number): 1
    output1 (json): CNinfo variants list
    """
    print(datetime.now(pytz.timezone('Asia/Tokyo')).strftime("%Y-%m-%d %H:%M:%S"), __name__, f"n={n}")
    
    if openai_key == "default" or not openai_key:
        openai_key = os.environ.get('OPENAI_KEY', '')
    
    if openai_key:
        os.environ['OPENAI_API_KEY'] = openai_key
    
    # n を整数に変換し、篁E��チェチE��
    try:
        n = int(n)
        if n < 1:
            print(f"Warning: n={n} is invalid, using n=1")
            n = 1
        elif n > 10:
            print(f"Warning: n={n} is too large, capping at 10")
            n = 10
    except (TypeError, ValueError):
        print(f"Warning: n={n} is invalid, using n=1")
        n = 1
    
    messages=[
        {
        "role": "system",
        "content": """提供したフォーマットデータから、忁E��なコンチE��チE�E要素を生成してください、E"",
        },
        {
        "role": "user",
        "content":  [{"type": "text", "text":p}]
        },
    ]
    
    # get_openai_requestは常にリストを返すので、そのまま使用
    result = get_openai_request(messages, CNinfo, n=n)
    
    print(f"Generated {len(result)} CN variants")
    
    # リストをJSON斁E���Eとして返す
    return json.dumps(result, ensure_ascii=False)