xiaoxia-saas/apps/worker/worker_app/tasks/ai_tasks.py

"""AI 相关异步任务 — 智能推荐 & 封面生成.

提供两个 Celery 任务:
- ai_recommend_clips: 分析素材并推荐片段编排方案
- generate_cover: 从视频中选帧或生成封面图

当前为 stub 实现（返回模拟数据），后续接入真实 AI 服务时
只需替换 _call_ai_recommend_service / _call_ai_cover_service 内部逻辑。
"""

from __future__ import annotations

import logging
import random
import time
from typing import Any, Dict, List

from packages.domain.config_schemas import DEFAULT_EDIT_PLAN_CONFIG

logger = logging.getLogger(__name__)


# ── AI 推荐片段方案 ──────────────────────────────────────────────────────────


def _call_ai_recommend_service(
    plan_id: str,
    template_id: str,
    asset_ids: List[str],
    editing_mode: str,
    target_duration: float,
) -> Dict[str, Any]:
    """调用 AI 推荐服务（stub）

    TODO: 接入真实 AI 服务，分析素材内容并生成推荐方案。
    当前返回基于模板规则的模拟推荐数据。
    """
    # 模拟 AI 分析耗时
    time.sleep(0.5)

    # 根据素材数量生成推荐片段
    clips: List[Dict[str, Any]] = []
    order = 0

    # 开场片段
    clips.append(
        {
            "clip_type": "intro",
            "order": order,
            "text_content": "精彩看点",
            "duration": 3.0,
            "transition_effect": "fade",
            "asset_id": asset_ids[0] if asset_ids else "",
            "start_time": 0.0,
            "config": {},
        }
    )
    order += 1

    # 为每个素材生成展示片段
    per_clip_duration = max(2.0, (target_duration - 6.0) / max(len(asset_ids), 1))
    for i, asset_id in enumerate(asset_ids):
        clips.append(
            {
                "clip_type": "showcase",
                "order": order,
                "text_content": f"展示片段 {i + 1}",
                "duration": round(per_clip_duration, 1),
                "transition_effect": "cut",
                "asset_id": asset_id,
                "start_time": 0.0,
                "config": {},
            }
        )
        order += 1

    # 结尾 CTA
    clips.append(
        {
            "clip_type": "outro",
            "order": order,
            "text_content": "感谢观看",
            "duration": 3.0,
            "transition_effect": "fade",
            "asset_id": "",
            "start_time": 0.0,
            "config": {},
        }
    )

    # 生成推荐 config
    config = DEFAULT_EDIT_PLAN_CONFIG.copy()
    config["title"]["text"] = f"精选视频 — {len(asset_ids)} 个片段"
    config["title"]["ai_auto"] = True

    return {
        "clips": clips,
        "config": config,
        "total_duration": round(sum(c["duration"] for c in clips), 1),
        "confidence": round(random.uniform(0.75, 0.95), 2),
    }


# ── AI 封面生成 ──────────────────────────────────────────────────────────────


def _call_ai_cover_service(
    plan_id: str,
    asset_ids: List[str],
    cover_type: str,
    frame_time: float | None = None,
) -> Dict[str, Any]:
    """调用 AI 封面生成服务（stub）

    TODO: 接入真实 AI 服务，从视频中选帧或生成封面。
    当前返回模拟封面数据。
    """
    # 模拟 AI 处理耗时
    time.sleep(0.3)

    if cover_type == "upload":
        return {
            "type": "upload",
            "image_url": "",
            "message": "请上传封面图片",
        }

    if cover_type == "manual" and frame_time is not None:
        return {
            "type": "manual",
            "image_url": f"/api/v1/assets/placeholder/cover?time={frame_time}",
            "frame_time": frame_time,
        }

    # ai_frame / ai_regenerate
    return {
        "type": "ai_frame",
        "image_url": f"/api/v1/assets/placeholder/cover?plan={plan_id}",
        "frame_time": round(random.uniform(1.0, 10.0), 1),
        "confidence": round(random.uniform(0.80, 0.98), 2),
    }


# ── 任务入口（供 Celery 调度或路由直接调用） ─────────────────────────────────


def run_ai_recommend(
    plan_id: str,
    template_id: str,
    asset_ids: List[str],
    editing_mode: str = "one_take",
    target_duration: float = 30.0,
) -> Dict[str, Any]:
    """执行 AI 推荐片段方案

    Args:
        plan_id: 剪辑计划 ID
        template_id: 模板 ID
        asset_ids: 素材 ID 列表
        editing_mode: 剪辑模式 (one_take / pip / voice_over / voice_pip)
        target_duration: 目标时长（秒）

    Returns:
        推荐方案 dict，包含 clips / config / total_duration / confidence
    """
    logger.info(
        "AI 推荐片段方案: plan_id=%s template_id=%s assets=%d mode=%s duration=%.1f",
        plan_id,
        template_id,
        len(asset_ids),
        editing_mode,
        target_duration,
    )
    result = _call_ai_recommend_service(
        plan_id=plan_id,
        template_id=template_id,
        asset_ids=asset_ids,
        editing_mode=editing_mode,
        target_duration=target_duration,
    )
    logger.info(
        "AI 推荐完成: plan_id=%s clips=%d duration=%.1f confidence=%.2f",
        plan_id,
        len(result["clips"]),
        result["total_duration"],
        result["confidence"],
    )
    return result


def run_generate_cover(
    plan_id: str,
    asset_ids: List[str],
    cover_type: str = "ai_frame",
    frame_time: float | None = None,
) -> Dict[str, Any]:
    """执行 AI 封面生成

    Args:
        plan_id: 剪辑计划 ID
        asset_ids: 素材 ID 列表（用于确定视频来源）
        cover_type: 封面类型 (ai_frame / manual / upload / ai_regenerate)
        frame_time: 手动选帧时间点（仅 manual 模式使用）

    Returns:
        封面数据 dict，包含 type / image_url / frame_time
    """
    logger.info(
        "AI 封面生成: plan_id=%s type=%s assets=%d",
        plan_id,
        cover_type,
        len(asset_ids),
    )
    result = _call_ai_cover_service(
        plan_id=plan_id,
        asset_ids=asset_ids,
        cover_type=cover_type,
        frame_time=frame_time,
    )
    logger.info(
        "AI 封面生成完成: plan_id=%s type=%s url=%s",
        plan_id,
        result.get("type"),
        result.get("image_url", "")[:60],
    )
    return result