from __future__ import annotations

import json
import os
from typing import Any

from openai import OpenAI

from .config import AppConfig
from .types import Scene


def _system_prompt(scene_count: int, min_chars: int, max_chars: int) -> str:
    return f"""你是一个专业短视频编剧与分镜师。
请把用户的创意扩展为 {scene_count} 个分镜(Scene) 的 JSON。

硬性约束：
1) 三个分镜的主角描述（Character Description）必须保持一致：姓名/外观/服饰/风格不可前后矛盾。
2) 每个分镜必须包含字段：image_prompt, video_motion, narration。
3) narration 为中文旁白，每段严格控制在约 {min_chars}-{max_chars} 字左右（宁可略短，不要超过太多）。
4) 画面描述要具体可视化，video_motion 描述镜头运动/人物动作。
5) 只输出 JSON，不要输出任何解释、markdown、代码块。

输出 JSON Schema（示例结构）：
{{
  "character_description": "...一致的主角设定...",
  "scenes": [
    {{"image_prompt":"...","video_motion":"...","narration":"..."}},
    {{"image_prompt":"...","video_motion":"...","narration":"..."}},
    {{"image_prompt":"...","video_motion":"...","narration":"..."}}
  ]
}}
"""


def generate_scenes(user_prompt: str, cfg: AppConfig) -> list[Scene]:
    scene_count = int(cfg.get("script_gen.scene_count", 3))
    min_chars = int(cfg.get("script_gen.narration_min_chars", 15))
    max_chars = int(cfg.get("script_gen.narration_max_chars", 20))

    api_key_env = str(cfg.get("openai.api_key_env", "OPENAI_API_KEY"))
    base_url_env = str(cfg.get("openai.base_url_env", "OPENAI_BASE_URL"))
    model = str(cfg.get("openai.model", "gpt-4o-mini"))

    api_key = os.environ.get(api_key_env)
    if not api_key:
        raise RuntimeError(f"Missing env var {api_key_env} for OpenAI API key")

    client = OpenAI(
        api_key=api_key,
        base_url=os.environ.get(base_url_env) or None,
    )

    resp = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": _system_prompt(scene_count, min_chars, max_chars)},
            {"role": "user", "content": user_prompt},
        ],
        response_format={"type": "json_object"},
        temperature=0.6,
    )

    content = resp.choices[0].message.content or "{}"
    data: Any = json.loads(content)
    scenes_raw = data.get("scenes")
    if not isinstance(scenes_raw, list) or len(scenes_raw) != scene_count:
        raise ValueError(f"Model returned invalid scenes length: {type(scenes_raw)}")

    scenes: list[Scene] = []
    for i, s in enumerate(scenes_raw):
        if not isinstance(s, dict):
            raise ValueError(f"Scene[{i}] must be object, got {type(s)}")
        image_prompt = str(s.get("image_prompt", "")).strip()
        video_motion = str(s.get("video_motion", "")).strip()
        narration = str(s.get("narration", "")).strip()
        if not image_prompt or not narration:
            raise ValueError(f"Scene[{i}] missing required fields")
        scenes.append(Scene(image_prompt=image_prompt, video_motion=video_motion, narration=narration))
    return scenes