5cacc5dbb5
- mcp-server: 하드코딩된 모델명을 llm-model.json 기반 config.py로 외부화 - C#: AppendPointsAsync로 기존 데이터 유지하면서 포인트 추가 - C#: LlmConfigController로 LLM 모델명 조회/저장 API - Frontend: LLM 설정 UI 카드 + 포인트 빌더에서 추가하기 버튼
611 lines
25 KiB
Python
611 lines
25 KiB
Python
#!/usr/bin/env python3
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"""P&ID 파싱 전용 워커 프로세스 (테스트용 - 절대 경로 지원)
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Usage: python pid_worker_test.py <port>
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담당 도구:
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extract_pid_tags, match_pid_tags,
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parse_pid_dxf, parse_pid_pdf, parse_pid_drawing,
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build_pid_graph_parallel, analyze_pid_impact
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"""
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from __future__ import annotations
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import sys
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import os
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# 프로젝트 루트를 Python 경로에 추가
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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sys.path.insert(0, PROJECT_ROOT)
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import io
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import json
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import asyncio
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import signal
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import logging
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import re
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from functools import lru_cache
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from fastapi import FastAPI, Request, BackgroundTasks
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import uvicorn
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# ── 설정 ─────────────────────────────────────────────────────────────────────
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VLLM_BASE_URL = os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1")
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from config import get_vllm_model
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VLLM_MODEL = get_vllm_model()
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DB_CONNECTION_STRING = os.environ.get("DB_CONNECTION_STRING", "postgresql://postgres:postgres@localhost:5432/iiot_platform")
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DB_TIMEOUT = int(os.environ.get("DB_TIMEOUT", "10"))
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_SERVER_DIR = PROJECT_ROOT
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STORAGE_DIR = os.path.join(_SERVER_DIR, "storage")
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logging.basicConfig(
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level=logging.INFO,
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stream=sys.stderr,
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format="%(asctime)s [pid_worker] %(levelname)s %(message)s",
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)
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app = FastAPI()
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# ── 싱글톤 ───────────────────────────────────────────────────────────────────
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@lru_cache(maxsize=1)
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def _llm():
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from openai import OpenAI
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return OpenAI(base_url=VLLM_BASE_URL, api_key="dummy")
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@lru_cache(maxsize=1)
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def _ocr():
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from paddleocr import PaddleOCR
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use_gpu = os.environ.get("PADDLE_USE_GPU", "true").lower() == "true"
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try:
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return PaddleOCR(use_angle_cls=True, lang="korean", use_gpu=use_gpu, show_log=False)
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except Exception:
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if use_gpu:
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os.environ["PADDLE_USE_GPU"] = "false"
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return _ocr()
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raise
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# ── DB ───────────────────────────────────────────────────────────────────────
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def _get_db_connection():
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import psycopg
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return psycopg.connect(DB_CONNECTION_STRING, connect_timeout=DB_TIMEOUT)
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# ── 텍스트 추출 ──────────────────────────────────────────────────────────────
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def _extract_text_from_dxf(filepath: str) -> str:
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import ezdxf
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from ezdxf.tools.text import plain_mtext
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doc = ezdxf.readfile(filepath)
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msp = doc.modelspace()
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texts = []
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for entity in msp:
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if entity.dxftype() == "TEXT":
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texts.append(entity.dxf.text)
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elif entity.dxftype() == "MTEXT":
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try:
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plain = plain_mtext(entity.dxf.text)
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if plain.strip():
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texts.append(plain)
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except Exception:
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pass
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return "\n".join(texts)
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def _extract_text_from_pdf(filepath: str) -> str:
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import fitz
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doc = fitz.open(filepath)
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return "\n".join(page.get_text() for page in doc)
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def _extract_text_from_pdf_ocr(filepath: str) -> str:
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import fitz
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from PIL import Image
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import numpy as np
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doc = fitz.open(filepath)
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all_texts = []
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for page in doc:
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mat = fitz.Matrix(300 / 72)
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pix = page.get_pixmap(matrix=mat)
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img = Image.open(io.BytesIO(pix.tobytes("png")))
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result = _ocr().ocr(np.array(img), cls=True)
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if result and result[0]:
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all_texts.extend(line[1][0] for line in result[0])
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return "\n".join(all_texts)
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# ── JSON 배열 파싱 유틸 ───────────────────────────────────────────────────────
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def _parse_json_array(raw: str, finish_reason: str = "") -> list:
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"""LLM 출력에서 JSON 배열 추출. finish_reason=length 잘림 복구 포함."""
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if raw.startswith("```"):
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lines = raw.splitlines()
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raw = "\n".join(lines[1:-1] if lines and lines[-1].strip() == "```" else lines[1:]).strip()
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if finish_reason == "length":
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last_close = raw.rfind("}")
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if last_close != -1:
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raw = raw[:last_close + 1] + "]"
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# 가장 긴 균형 잡힌 [...] 추출
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depth = 0; start = -1; best = ""
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for i, c in enumerate(raw):
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if c == "[":
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if depth == 0:
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start = i
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depth += 1
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elif c == "]":
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depth -= 1
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if depth == 0 and start >= 0:
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cand = raw[start:i + 1]
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if len(cand) > len(best):
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best = cand
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raw = best if best else "[]"
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try:
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return json.loads(raw)
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except json.JSONDecodeError:
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data = []
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for obj in re.findall(r"\{[^{}]*\}", raw, re.DOTALL):
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try:
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data.append(json.loads(obj))
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except json.JSONDecodeError:
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pass
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return data
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# ── 태그 추출/매핑 도구 ───────────────────────────────────────────────────────
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def _extract_pid_tags(text: str, source_type: str) -> str:
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system = (
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"You are a P&ID (Piping and Instrumentation Diagram) expert.\n"
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"Extract all instrument and equipment tags from the provided text.\n"
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"Return ONLY a valid JSON array. Each element must have exactly these fields:\n"
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'{"tagNo":"FCV-101","equipmentName":null,"instrumentType":"FCV",'
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'"lineNumber":null,"pidDrawingNo":null,"confidence":0.95}\n'
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"Rules:\n"
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"- tagNo: any token matching [LETTERS]-[DIGITS] or [LETTERS]-[DIGITS]-[SUFFIX]\n"
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" Examples: FCV-101, P-10101, T-10100, VG-6203-15A-F1A-n, BT-6200, DP-10101\n"
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"- instrumentType: leading letters of tagNo\n"
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"- equipmentName: descriptive name if present near tag, else null\n"
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"- lineNumber/pidDrawingNo: null unless explicitly associated\n"
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"- confidence: 0.95 for clear tags, lower for ambiguous\n"
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"- Output ONLY the JSON array, no markdown, no explanation.\n"
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"- If no tags found, return: []\n"
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)
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truncated = text[:100000]
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resp = _llm().chat.completions.create(
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model=VLLM_MODEL,
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messages=[
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{"role": "system", "content": system},
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{"role": "user", "content": f"Source: {source_type}\n\nText:\n{truncated}"},
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],
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max_tokens=32768,
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temperature=0.1,
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extra_body={"chat_template_kwargs": {"enable_thinking": False}},
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)
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raw = (resp.choices[0].message.content or "").strip()
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data = _parse_json_array(raw, resp.choices[0].finish_reason)
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logging.info(f"extract_pid_tags source={source_type} count={len(data)}")
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return json.dumps({
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"success": True,
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"data": {"count": len(data), "tags": data},
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"message": "태그 추출 완료"
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}, ensure_ascii=False, indent=2)
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def _match_pid_tags(pid_tags: list, experion_tags: list) -> str:
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system = (
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"You are a P&ID to Experion tag matching expert.\n"
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"Match P&ID tags to Experion tags based on similarity.\n"
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"Return ONLY a JSON array:\n"
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'[{"pidTag":"FT-101","experionTag":"ft-101.pv","confidence":0.92},...]\n'
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"- If no good match: confidence < 0.5, experionTag null\n"
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"- Output ONLY the JSON array.\n"
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)
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resp = _llm().chat.completions.create(
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model=VLLM_MODEL,
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messages=[
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{"role": "system", "content": system},
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{"role": "user", "content": (
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f"P&ID Tags:\n{chr(10).join(pid_tags)}\n\n"
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f"Experion Tags:\n{chr(10).join(experion_tags)}"
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)},
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],
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max_tokens=16384,
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temperature=0.1,
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extra_body={"chat_template_kwargs": {"enable_thinking": False}},
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)
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raw = (resp.choices[0].message.content or "").strip()
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data = _parse_json_array(raw, resp.choices[0].finish_reason)
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return json.dumps({
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"success": True,
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"data": {"count": len(data), "mappings": data},
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"message": "태그 매핑 완료"
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}, ensure_ascii=False, indent=2)
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# ── 도면 파싱 도구 ────────────────────────────────────────────────────────────
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_TAG_EXTRACT_SYSTEM = (
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"You are a P&ID (Piping and Instrumentation Diagram) expert.\n"
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"Extract instrument and equipment tags from the provided text.\n"
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"Return ONLY a JSON array:\n"
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'[{"tagNo":"FIT-10115","equipmentName":"Flow Transmitter","instrumentType":"FIT",'
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'"lineNumber":"L-101","pidDrawingNo":"P&ID-001","confidence":0.95},...]\n'
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"Rules:\n"
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"- tagNo: Instrument [Function]-[Number], Equipment [Type]-[Number]\n"
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"- instrumentType: first 2-4 letters of tagNo\n"
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"- equipmentName/lineNumber/pidDrawingNo: null if not present\n"
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"- confidence: 0.0 to 1.0\n"
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"- Output ONLY the JSON array, no markdown.\n"
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"- If no tags found, return: []\n"
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)
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def _parse_pid_dxf(filepath: str) -> str:
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text = _extract_text_from_dxf(filepath)
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if not text.strip():
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return json.dumps({"success": True, "text": "", "count": 0, "tags": []},
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ensure_ascii=False, indent=2)
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resp = _llm().chat.completions.create(
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model=VLLM_MODEL,
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messages=[
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{"role": "system", "content": _TAG_EXTRACT_SYSTEM},
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{"role": "user", "content": f"Source: dxf\n\nText:\n{text[:8000]}"},
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],
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max_tokens=8192,
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temperature=0.1,
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)
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raw = (resp.choices[0].message.content or "").strip()
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data = _parse_json_array(raw, resp.choices[0].finish_reason)
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if not isinstance(data, list):
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data = []
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return json.dumps({
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"success": True,
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"data": {"text": text[:10000], "count": len(data), "tags": data},
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"message": "DXF 파싱 완료"
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}, ensure_ascii=False, indent=2)
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def _parse_pid_pdf(filepath: str, use_ocr: bool = True) -> str:
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text = _extract_text_from_pdf_ocr(filepath) if use_ocr else _extract_text_from_pdf(filepath)
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if not text.strip():
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return json.dumps({"success": True, "text": "", "count": 0, "tags": []},
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ensure_ascii=False, indent=2)
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resp = _llm().chat.completions.create(
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model=VLLM_MODEL,
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messages=[
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{"role": "system", "content": _TAG_EXTRACT_SYSTEM},
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{"role": "user", "content": f"Source: pdf\n\nText:\n{text[:12000]}"},
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],
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max_tokens=4096,
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temperature=0.1,
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)
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raw = (resp.choices[0].message.content or "").strip()
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data = _parse_json_array(raw, resp.choices[0].finish_reason)
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if not isinstance(data, list):
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data = []
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return json.dumps({
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"success": True,
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"data": {"text": text[:10000], "count": len(data), "tags": data},
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"message": "PDF 파싱 완료"
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}, ensure_ascii=False, indent=2)
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def _parse_pid_drawing(filepath: str) -> str:
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ext = os.path.splitext(filepath)[1].lower()
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if ext == ".dxf":
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return _parse_pid_dxf(filepath)
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elif ext == ".pdf":
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return _parse_pid_pdf(filepath)
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elif ext == ".dwg":
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return json.dumps({
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"success": False,
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"data": None,
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"error": "DWG 파일은 직접 파싱할 수 없습니다. DXF로 변환 후 사용하세요.",
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"message": "지원하지 않는 파일 형식"
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}, ensure_ascii=False)
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else:
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return json.dumps({
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"success": False,
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"error": f"지원하지 않는 형식: {ext}. 지원: .dxf, .pdf",
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}, ensure_ascii=False)
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# ── 그래프 도구 ───────────────────────────────────────────────────────────────
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import time
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async def _build_pid_graph_parallel(filepath: str) -> str:
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"""
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P&ID 그래프 빌드 — 독립 프로세스 병렬 아키텍처.
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Phase 1: 도면 분할 + 기하 추출
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Phase 2: 전체 텍스트 1회 추출
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Phase 3: 5개 독립 추출 프로세스 병렬 실행 (subprocess.Popen)
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Phase 4: 결과 통합 + tagNo 기준 중복 제거
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Phase 5: 위상 그래프 빌드 + 저장
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"""
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from pipeline.extractor import PidGeometricExtractor
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from pipeline.topology import PidTopologyBuilder
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import subprocess
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import tempfile
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import shutil
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os.makedirs(STORAGE_DIR, exist_ok=True)
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t0 = time.time()
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# 절대 경로로 변환하여 파일 경로 문제 해결
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if not os.path.isabs(filepath):
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filepath = os.path.join(PROJECT_ROOT, filepath)
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filepath = os.path.abspath(filepath)
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basename = os.path.basename(filepath)
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worker_dir = os.path.join(PROJECT_ROOT, "mcp-server", "worker")
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logging.info(f"[{basename}] === 독립 프로세스 병렬 아키텍처 시작 ===")
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# ── Phase 1: 도면 분할 + 기하 추출 ──────────────────────────────
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logging.info(f"[{basename}] Phase 1: 도면 분할 + 기하 추출 시작")
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extractor = PidGeometricExtractor(filepath)
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regions = extractor.split_drawings()
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logging.info(f"[{basename}] 도면 분할: {len(regions)}개 영역")
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geo_data_path = os.path.join(STORAGE_DIR, basename + "_geo.json")
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extractor.extract_and_save(geo_data_path)
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with open(geo_data_path, "r", encoding="utf-8") as f:
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geo_data = json.load(f)
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logging.info(f"[{basename}] Phase 1 완료 ({time.time()-t0:.1f}s) - {len(geo_data)}개 엔티티")
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# ── Phase 2: 전체 텍스트 1회 추출 ───────────────────────────────
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t2 = time.time()
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logging.info(f"[{basename}] Phase 2: 전체 텍스트 1회 추출")
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full_text = _extract_text_from_dxf(filepath)
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# 임시 디렉토리 생성 (프로세스 간 통신용)
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temp_dir = tempfile.mkdtemp(prefix=f"pid_{basename.replace('.dxf', '')}_")
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text_path = os.path.join(temp_dir, "full_text.txt")
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with open(text_path, "w", encoding="utf-8") as f:
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f.write(full_text)
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logging.info(f"[{basename}] Phase 2 완료 ({time.time()-t2:.1f}s) - {len(full_text)}자")
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# ── Phase 3: 5개 독립 추출 프로세스 병렬 실행 ───────────────────
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t3 = time.time()
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logging.info(f"[{basename}] Phase 3: 5개 독립 추출 프로세스 병렬 실행")
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extractors = [
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("sensor", "pid_extract_sensor.py"),
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("valve", "pid_extract_valve.py"),
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("system", "pid_extract_system.py"),
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("gauge", "pid_extract_gauge.py"),
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("pump", "pid_extract_pump.py"),
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]
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results_dir = os.path.join(temp_dir, "results")
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os.makedirs(results_dir, exist_ok=True)
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processes = []
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for name, script in extractors:
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output_path = os.path.join(results_dir, f"{name}.json")
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script_path = os.path.join(worker_dir, script)
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cmd = [
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sys.executable, script_path,
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"--input", text_path,
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"--output", output_path,
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]
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try:
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proc = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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)
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processes.append((name, proc, output_path))
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logging.info(f"[{basename}] 시작: {name} (pid={proc.pid})")
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except Exception as e:
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logging.error(f"[{basename}] 실행 실패 {name}: {e}")
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# 모든 프로세스 대기 (timeout=300초)
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for name, proc, output_path in processes:
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try:
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stdout, stderr = proc.communicate(timeout=300)
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if proc.returncode != 0:
|
|
logging.error(f"[{basename}] {name} 실패 (rc={proc.returncode}): {stderr[:200]}")
|
|
else:
|
|
logging.info(f"[{basename}] {name} 완료: {stdout.strip()[:100]}")
|
|
except subprocess.TimeoutExpired:
|
|
proc.kill()
|
|
logging.error(f"[{basename}] {name} 타임아웃 (300초)")
|
|
|
|
logging.info(f"[{basename}] Phase 3 완료 ({time.time()-t3:.1f}s)")
|
|
|
|
# ── Phase 4: 결과 JSON 통합 + tagNo 기준 중복 제거 ──────────────
|
|
t4 = time.time()
|
|
logging.info(f"[{basename}] Phase 4: 결과 통합 + 중복 제거")
|
|
|
|
all_tags = []
|
|
seen_tagnos = set()
|
|
|
|
for name, _, output_path in processes:
|
|
if not os.path.exists(output_path):
|
|
logging.warning(f"[{basename}] 결과 없음: {name}")
|
|
continue
|
|
try:
|
|
with open(output_path, "r", encoding="utf-8") as f:
|
|
result = json.load(f)
|
|
tags = result.get("tags", [])
|
|
count_new = 0
|
|
for tag in tags:
|
|
tag_no = tag.get("tagNo", "")
|
|
if tag_no and tag_no not in seen_tagnos:
|
|
seen_tagnos.add(tag_no)
|
|
all_tags.append(tag)
|
|
count_new += 1
|
|
logging.info(f"[{basename}] {name}: {len(tags)}개 중 {count_new}개 신규")
|
|
except Exception as e:
|
|
logging.error(f"[{basename}] 결과 로드 실패 {name}: {e}")
|
|
|
|
logging.info(f"[{basename}] Phase 4 완료 ({time.time()-t4:.1f}s) - 총 {len(all_tags)}개 태그")
|
|
|
|
# ── Phase 5: 위상 그래프 빌드 + 저장 ────────────────────────────
|
|
t5 = time.time()
|
|
logging.info(f"[{basename}] Phase 5: 위상 그래프 빌드")
|
|
|
|
builder = PidTopologyBuilder(geo_data)
|
|
builder.build_graph()
|
|
|
|
# 추출된 태그를 그래프에 추가
|
|
from shapely.geometry import box as shapely_box
|
|
for tag in all_tags:
|
|
tag_no = tag.get("tagNo", "UNKNOWN")
|
|
eq_name = tag.get("equipmentName")
|
|
inst_type = tag.get("instrumentType")
|
|
confidence = tag.get("confidence", 0.5)
|
|
|
|
# 해당 태그의 bbox 찾기 (geo_data에서 clean_value 매칭)
|
|
matched_bbox = None
|
|
for entity in geo_data:
|
|
if entity.get("clean_value", "").upper() == tag_no.upper():
|
|
bbox = entity.get("bbox", {})
|
|
matched_bbox = (
|
|
bbox.get("min_x"), bbox.get("min_y"),
|
|
bbox.get("max_x"), bbox.get("max_y")
|
|
)
|
|
break
|
|
|
|
node_id = f"tag_{tag_no}"
|
|
if matched_bbox:
|
|
bbox_geom = shapely_box(matched_bbox[0], matched_bbox[1],
|
|
matched_bbox[2], matched_bbox[3])
|
|
builder.G.add_node(node_id,
|
|
type="TEXT",
|
|
bbox=bbox_geom,
|
|
value=tag_no,
|
|
equipment_name=eq_name,
|
|
instrument_type=inst_type,
|
|
confidence=confidence)
|
|
else:
|
|
builder.G.add_node(node_id,
|
|
type="TEXT",
|
|
bbox=shapely_box(0, 0, 1, 1),
|
|
value=tag_no,
|
|
equipment_name=eq_name,
|
|
instrument_type=inst_type,
|
|
confidence=confidence)
|
|
|
|
# 태그-설비 연결
|
|
equipments = [n for n, d in builder.G.nodes(data=True)
|
|
if d.get("type") not in ("TEXT", "LINE", "LWPOLYLINE")]
|
|
if equipments:
|
|
eq_grid = builder._build_spatial_grid(equipments)
|
|
tag_ids = [f"tag_{t.get('tagNo', '')}" for t in all_tags]
|
|
for tag_id in tag_ids:
|
|
if tag_id in builder.G:
|
|
best_match = builder._find_nearest_equipment(tag_id, eq_grid)
|
|
if best_match:
|
|
builder.G.add_edge(tag_id, best_match, relation="associated_with")
|
|
|
|
graph_id = basename.replace(".dxf", "_graph.json")
|
|
graph_path = os.path.join(STORAGE_DIR, graph_id)
|
|
builder.save_graph(graph_path)
|
|
|
|
# 임시 디렉토리 정리
|
|
shutil.rmtree(temp_dir, ignore_errors=True)
|
|
|
|
total_time = time.time() - t0
|
|
logging.info(f"[{basename}] 전체 완료 ({total_time:.1f}s) - graph_id={graph_id} "
|
|
f"nodes={builder.G.number_of_nodes()} "
|
|
f"edges={builder.G.number_of_edges()} "
|
|
f"tags={len(all_tags)}")
|
|
return json.dumps({
|
|
"success": True,
|
|
"graph_id": graph_id,
|
|
"graph_path": graph_path,
|
|
"nodes": builder.G.number_of_nodes(),
|
|
"edges": builder.G.number_of_edges(),
|
|
"tags_extracted": len(all_tags),
|
|
"processing_time_sec": round(total_time, 1)
|
|
}, ensure_ascii=False)
|
|
|
|
|
|
def _analyze_pid_impact(graph_id: str, start_node_id: str) -> str:
|
|
from pipeline.analyzer import PidAnalysisEngine
|
|
|
|
graph_path = os.path.join(STORAGE_DIR, graph_id)
|
|
mapping_path = graph_path.replace("_graph.json", "_mapping.json")
|
|
analyzer = PidAnalysisEngine(graph_path, mapping_path)
|
|
result = analyzer.analyze_impact(start_node_id)
|
|
return json.dumps(result, ensure_ascii=False, indent=2)
|
|
|
|
# ── 요청 디스패처 ─────────────────────────────────────────────────────────────
|
|
|
|
async def _dispatch(tool: str, params: dict) -> str:
|
|
try:
|
|
match tool:
|
|
# blocking 함수는 asyncio.to_thread로 스레드풀 오프로드
|
|
case "extract_pid_tags":
|
|
return await asyncio.to_thread(_extract_pid_tags, **params)
|
|
case "match_pid_tags":
|
|
return await asyncio.to_thread(_match_pid_tags, **params)
|
|
case "parse_pid_dxf":
|
|
return await asyncio.to_thread(_parse_pid_dxf, **params)
|
|
case "parse_pid_pdf":
|
|
return await asyncio.to_thread(_parse_pid_pdf, **params)
|
|
case "parse_pid_drawing":
|
|
return await asyncio.to_thread(_parse_pid_drawing, **params)
|
|
case "analyze_pid_impact":
|
|
return await asyncio.to_thread(_analyze_pid_impact, **params)
|
|
# 이미 async — 직접 await
|
|
case "build_pid_graph_parallel":
|
|
return await _build_pid_graph_parallel(**params)
|
|
case _:
|
|
return json.dumps({"success": False, "error": f"알 수 없는 도구: {tool}"},
|
|
ensure_ascii=False)
|
|
except Exception as e:
|
|
logging.error(f"dispatch error tool={tool}: {e}", exc_info=True)
|
|
return json.dumps({"success": False, "error": str(e)}, ensure_ascii=False)
|
|
|
|
# ── 종료 예약 ─────────────────────────────────────────────────────────────────
|
|
|
|
def _schedule_shutdown():
|
|
"""
|
|
응답 전송 완료 후 프로세스 종료 예약.
|
|
FastAPI의 BackgroundTasks를 사용하여 응답이 완전히 전송된 후 종료되도록 유도함.
|
|
"""
|
|
async def _do():
|
|
# 네트워크 전송 및 커넥션 정리를 위한 최소한의 대기 시간
|
|
await asyncio.sleep(1.0)
|
|
logging.info("One-shot worker shutting down...")
|
|
os.kill(os.getpid(), signal.SIGTERM)
|
|
asyncio.create_task(_do())
|
|
|
|
# ── HTTP 엔드포인트 ───────────────────────────────────────────────────────────
|
|
|
|
@app.get("/health")
|
|
async def health():
|
|
return {"status": "ok"}
|
|
|
|
|
|
@app.post("/execute")
|
|
async def execute(request: Request):
|
|
body = await request.json()
|
|
return await _dispatch(body["tool"], body["params"])
|
|
|
|
|
|
@app.post("/execute/one_shot")
|
|
async def execute_one_shot(request: Request, background_tasks: BackgroundTasks):
|
|
"""요청 처리 후 프로세스 자동 종료 (P&ID 워커 전용)."""
|
|
body = await request.json()
|
|
result = await _dispatch(body["tool"], body["params"])
|
|
|
|
# BackgroundTasks에 등록하여 응답 전송이 완료된 후 _schedule_shutdown이 실행되도록 함
|
|
background_tasks.add_task(_schedule_shutdown)
|
|
|
|
return result
|
|
|
|
# ── 진입점 ───────────────────────────────────────────────────────────────────
|
|
|
|
if __name__ == "__main__":
|
|
port = int(sys.argv[1]) if len(sys.argv) > 1 else 5005
|
|
os.makedirs(STORAGE_DIR, exist_ok=True)
|
|
uvicorn.run(app, host="0.0.0.0", port=port, log_level="warning")
|