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potaser
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#!/usr/bin/env python3
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"""
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方法学提取 API - FastAPI 版本
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提供接口：
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- POST /extract - 从 PDF 提取方法学参数
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- GET /result/{job_id} - 获取提取结果
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"""
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import os
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import sys
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import json
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import uuid
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from pathlib import Path
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from fastapi import FastAPI, File, UploadFile, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import Dict, List, Optional
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from src.rag.extractors.methodology.simple_extractor import SimpleExtractor
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app = FastAPI(title="Methodology Extraction API")
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# CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# 存储提取结果
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results = {}
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class ExtractionResult(BaseModel):
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job_id: str
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status: str
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data: Optional[Dict] = None
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error: Optional[str] = None
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@app.post("/extract")
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async def extract_methodology(pdf: UploadFile = File(...)):
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"""从 PDF 提取方法学参数"""
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# 生成 job_id
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job_id = str(uuid.uuid4())[:8]
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# 保存 PDF
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upload_dir = Path(__file__).parent.parent / 'uploads'
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upload_dir.mkdir(exist_ok=True)
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pdf_path = upload_dir / f"{job_id}_{pdf.filename}"
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with open(pdf_path, "wb") as f:
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content = await pdf.read()
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f.write(content)
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# 提取
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try:
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extractor = SimpleExtractor()
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result_text = extractor.extract(pdf_path)
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# 解析为结构化数据
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structured = parse_extraction_result(result_text)
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# 保存结果
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results[job_id] = {
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'status': 'completed',
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'pdf_path': str(pdf_path),
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'raw_text': result_text,
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'data': structured
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}
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return {
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'job_id': job_id,
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'status': 'completed',
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'data': structured
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}
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except Exception as e:
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results[job_id] = {
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'status': 'failed',
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'error': str(e)
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}
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/result/{job_id}")
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async def get_result(job_id: str):
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"""获取提取结果"""
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if job_id not in results:
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raise HTTPException(status_code=404, detail="Job not found")
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return results[job_id]
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def parse_extraction_result(text: str) -> Dict:
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"""解析提取结果为结构化数据"""
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data = {
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'species': {
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'target': '',
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'references': []
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},
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'gene_family': {
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'name': '',
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'pfam_id': ''
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},
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'stages': []
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}
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# 简单解析
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lines = text.split('\n')
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for line in lines:
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line = line.strip()
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# 物种
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if '目标物种' in line or 'target' in line.lower():
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if '：' in line:
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data['species']['target'] = line.split('：', 1)[-1].strip()
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elif '参考物种' in line or 'references' in line.lower():
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if '：' in line:
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refs = line.split('：', 1)[-1].strip()
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data['species']['references'] = [r.strip() for r in refs.split('、') if r.strip()]
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elif '基因家族' in line:
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if '：' in line:
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parts = line.split('：', 1)[-1].strip()
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if '(' in parts and 'Pfam:' in parts:
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name = parts.split('(')[0].strip()
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pfam = parts.split('Pfam:')[-1].split(')')[0].strip()
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data['gene_family']['name'] = name
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data['gene_family']['pfam_id'] = pfam
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# 默认阶段
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data['stages'] = [
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{
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'id': 'identify',
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'name': '1. 基因家族鉴定',
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'params': [
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{'name': 'tool', 'label': '工具', 'value': 'BLASTp + HMMER'},
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{'name': 'pfam_id', 'label': 'Pfam ID', 'value': 'PF03110'},
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{'name': 'e_value', 'label': 'E-value', 'value': ''},
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{'name': 'identity', 'label': 'Identity', 'value': ''}
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]
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},
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{
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'id': 'phylogeny',
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'name': '2. 系统发育分析',
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'params': [
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{'name': 'software', 'label': '软件', 'value': 'MEGA 6.0'},
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{'name': 'method', 'label': '方法', 'value': 'Neighbor-Joining'},
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{'name': 'bootstrap', 'label': 'Bootstrap', 'value': '1000'}
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]
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},
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{
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'id': 'motif',
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'name': '3. 保守基序分析',
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'params': [
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{'name': 'tool', 'label': '工具', 'value': 'MEME'},
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{'name': 'max_motifs', 'label': 'Max motifs', 'value': '10'}
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]
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},
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{
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'id': 'structure',
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'name': '4. 基因结构分析',
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'params': [
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{'name': 'tool', 'label': '工具', 'value': 'GSDS'}
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]
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},
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{
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'id': 'synteny',
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'name': '5. 共线性分析',
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'params': [
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{'name': 'tool', 'label': '工具', 'value': 'MCScanX'}
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]
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},
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{
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'id': 'expression',
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'name': '6. 表达分析',
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'params': [
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{'name': 'method', 'label': '方法', 'value': 'qRT-PCR'}
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]
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}
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]
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return data
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if __name__ == '__main__':
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=5001)
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