xiamuceer
141 lines
4.8 KiB
Python
141 lines
4.8 KiB
Python
"""AI去味API - 核心特色功能"""
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from fastapi import APIRouter, Depends, HTTPException, Request
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.database import get_db
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from app.models.generation_history import GenerationHistory
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from app.schemas.polish import PolishRequest, PolishResponse
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from app.services.ai_service import AIService
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from app.services.prompt_service import prompt_service, PromptService
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from app.logger import get_logger
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from app.api.settings import get_user_ai_service
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router = APIRouter(prefix="/polish", tags=["AI去味"])
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logger = get_logger(__name__)
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@router.post("", response_model=PolishResponse, summary="AI去味")
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async def polish_text(
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request: PolishRequest,
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http_request: Request,
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db: AsyncSession = Depends(get_db),
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user_ai_service: AIService = Depends(get_user_ai_service)
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):
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"""
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AI去味 - 将AI生成的文本改写得更像人类作家的手笔
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核心功能：
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- 去除AI痕迹（工整排比、重复修辞、机械总结）
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- 增加人性化（口语化、不完美细节、真实情感）
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- 优化叙事（自然节奏、简单词汇、松弛感）
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- 让对话更生活化
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这是本项目的核心特色功能！
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"""
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try:
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# 获取用户ID
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user_id = getattr(http_request.state, 'user_id', None)
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# 获取自定义提示词模板
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template = await PromptService.get_template("DENOISING", user_id, db)
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# 格式化提示词
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prompt = PromptService.format_prompt(
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template,
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original_text=request.original_text
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)
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logger.info(f"开始AI去味处理，原文长度: {len(request.original_text)}")
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# 调用AI进行去味处理
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polished_text = await ai_service.generate_text(
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prompt=prompt,
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provider=request.provider,
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model=request.model,
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temperature=request.temperature,
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max_tokens=len(request.original_text) * 2 # 预留足够token
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)
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# 计算字数
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word_count_before = len(request.original_text)
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word_count_after = len(polished_text)
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logger.info(f"AI去味完成，处理后长度: {word_count_after}")
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# 如果提供了项目ID，记录到历史
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if request.project_id:
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history = GenerationHistory(
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project_id=request.project_id,
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generation_type="polish",
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prompt=f"原文: {request.original_text[:100]}...",
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result=polished_text,
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provider=request.provider or "default",
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model=request.model or "default"
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)
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db.add(history)
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await db.commit()
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return PolishResponse(
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original_text=request.original_text,
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polished_text=polished_text,
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word_count_before=word_count_before,
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word_count_after=word_count_after
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)
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except Exception as e:
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logger.error(f"AI去味失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"AI去味失败: {str(e)}")
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@router.post("/batch", summary="批量AI去味")
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async def polish_batch(
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texts: list[str],
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project_id: int = None,
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provider: str = None,
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model: str = None,
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http_request: Request = None,
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db: AsyncSession = Depends(get_db),
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user_ai_service: AIService = Depends(get_user_ai_service)
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):
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"""
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批量处理多个文本的AI去味
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适用于一次性处理多个章节或段落
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"""
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try:
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# 获取用户ID
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user_id = getattr(http_request.state, 'user_id', None) if http_request else None
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results = []
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for idx, text in enumerate(texts):
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logger.info(f"处理第 {idx+1}/{len(texts)} 个文本")
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# 获取自定义提示词模板
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template = await PromptService.get_template("DENOISING", user_id, db)
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# 格式化提示词
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prompt = PromptService.format_prompt(template, original_text=text)
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polished_text = await user_ai_service.generate_text(
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prompt=prompt,
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provider=provider,
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model=model
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)
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results.append({
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"index": idx,
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"original": text,
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"polished": polished_text,
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"word_count_before": len(text),
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"word_count_after": len(polished_text)
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})
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logger.info(f"批量AI去味完成，共处理 {len(results)} 个文本")
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return {
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"total": len(results),
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"results": results
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}
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except Exception as e:
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logger.error(f"批量AI去味失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"批量AI去味失败: {str(e)}") |