145 lines
4.2 KiB
Python
145 lines
4.2 KiB
Python
"""
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AI 分析 API 接口
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"""
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from fastapi import APIRouter, UploadFile, File, HTTPException, Query, Body
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from typing import Optional
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import logging
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from app.services.excel_ai_service import excel_ai_service
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/ai", tags=["AI 分析"])
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@router.post("/analyze/excel")
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async def analyze_excel(
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file: UploadFile = File(...),
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user_prompt: str = Query("", description="用户自定义提示词"),
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analysis_type: str = Query("general", description="分析类型: general, summary, statistics, insights"),
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parse_all_sheets: bool = Query(False, description="是否分析所有工作表")
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):
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"""
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上传并使用 AI 分析 Excel 文件
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Args:
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file: 上传的 Excel 文件
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user_prompt: 用户自定义提示词
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analysis_type: 分析类型
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parse_all_sheets: 是否分析所有工作表
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Returns:
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dict: 分析结果，包含 Excel 数据和 AI 分析结果
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"""
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# 检查文件类型
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if not file.filename:
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raise HTTPException(status_code=400, detail="文件名为空")
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file_ext = file.filename.split('.')[-1].lower()
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if file_ext not in ['xlsx', 'xls']:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的文件类型: {file_ext}，仅支持 .xlsx 和 .xls"
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)
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# 验证分析类型
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supported_types = ['general', 'summary', 'statistics', 'insights']
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if analysis_type not in supported_types:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的分析类型: {analysis_type}，支持的类型: {', '.join(supported_types)}"
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)
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try:
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# 读取文件内容
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content = await file.read()
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logger.info(f"开始分析文件: {file.filename}, 分析类型: {analysis_type}")
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# 调用 AI 分析服务
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if parse_all_sheets:
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result = await excel_ai_service.batch_analyze_sheets(
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content,
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file.filename,
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user_prompt=user_prompt,
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analysis_type=analysis_type
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)
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else:
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# 解析选项
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parse_options = {"header_row": 0}
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result = await excel_ai_service.analyze_excel_file(
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content,
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file.filename,
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user_prompt=user_prompt,
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analysis_type=analysis_type,
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parse_options=parse_options
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)
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logger.info(f"文件分析完成: {file.filename}, 成功: {result['success']}")
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return result
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"AI 分析过程中出错: {str(e)}")
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raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
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@router.get("/analysis/types")
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async def get_analysis_types():
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"""
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获取支持的分析类型列表
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Returns:
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list: 支持的分析类型
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"""
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return {
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"types": excel_ai_service.get_supported_analysis_types()
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}
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@router.post("/analyze/text")
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async def analyze_text(
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excel_data: dict = Body(..., description="Excel 解析后的数据"),
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user_prompt: str = Body("", description="用户提示词"),
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analysis_type: str = Body("general", description="分析类型")
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):
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"""
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对已解析的 Excel 数据进行 AI 分析
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Args:
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excel_data: Excel 数据
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user_prompt: 用户提示词
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analysis_type: 分析类型
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Returns:
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dict: 分析结果
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"""
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try:
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logger.info(f"开始文本分析, 分析类型: {analysis_type}")
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# 调用 LLM 服务
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from app.services.llm_service import llm_service
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if user_prompt and user_prompt.strip():
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result = await llm_service.analyze_with_template(
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excel_data,
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user_prompt
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)
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else:
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result = await llm_service.analyze_excel_data(
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excel_data,
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user_prompt,
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analysis_type
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)
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logger.info(f"文本分析完成, 成功: {result['success']}")
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return result
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except Exception as e:
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logger.error(f"文本分析失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
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