Initial portal commit: landing + 9 AI-powered apps
Apps: - dwg-rooms: extract room numbers from DWG/DXF - dwg-counting: count symbols in PDF drawings (OpenCV template matching) - contract-check: review PDF contracts against a checklist (Claude vision + Tesseract OCR fallback) - email-drafter: bullet notes → polished Czech/English business emails - invoice-extractor: PDF/image invoice → structured data → Excel - translator: Czech-first translator across 19 languages with tone control - vv-check: find inconsistent unit prices across VV sheets in one workbook - vv-compare: diff original vs new VV files (changes / added / removed) - feature-request: portal users submit ideas + sample files Infrastructure: - LiteLLM gateway with per-app virtual keys + budgets - Langfuse observability - Geist font, shared theme, cross-subdomain back link + theme sync via cookie/URL - Caddy reverse proxy on *.klas.chat
This commit is contained in:
Ondřej Glaser
440
dwg-counting/main.py
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440
dwg-counting/main.py
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"""FastAPI app: upload DWG/DXF/PDF → vision-detect legend → count selected symbols → Excel."""
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import asyncio
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import logging
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import os
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import uuid
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from pathlib import Path
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from fastapi import FastAPI, File, HTTPException, UploadFile
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import FileResponse
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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from counting import count_template, debug_template
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from excel_export import export_to_excel
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from pdf_export import render_annotated_pdf
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from renderer import crop_region, render
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from vision import detect_legend
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DEFAULT_FLOOR_INDEX = 0 # MVP: process the first detected floor
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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app = FastAPI(title="DWG Symbol Counter")
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
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WORK_DIR = Path(os.getenv("WORK_DIR", "/tmp/dwg-counting"))
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WORK_DIR.mkdir(parents=True, exist_ok=True)
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jobs: dict[str, dict] = {}
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# Persistent action log so the operator can replay what the user did.
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ACTION_LOG = WORK_DIR / "action.log"
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def _log_action(event: str, **fields):
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import datetime, json as _json
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line = _json.dumps({
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"ts": datetime.datetime.now().isoformat(timespec="seconds"),
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"event": event,
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**fields,
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}, ensure_ascii=False)
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try:
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with open(ACTION_LOG, "a") as f:
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f.write(line + "\n")
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except Exception:
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pass
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@app.get("/")
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async def root():
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return FileResponse("static/index.html")
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@app.post("/api/upload")
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async def upload(file: UploadFile = File(...), auto_detect: bool = False):
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suffix = Path(file.filename or "").suffix.lower()
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if suffix != ".pdf":
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raise HTTPException(400, "Podporovaný formát: .pdf")
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job_id = str(uuid.uuid4())
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job_dir = WORK_DIR / job_id
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job_dir.mkdir()
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input_path = job_dir / f"input{suffix}"
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input_path.write_bytes(await file.read())
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logger.info("Job %s: %s (%d bytes)", job_id, file.filename, input_path.stat().st_size)
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_log_action("upload", job_id=job_id, filename=file.filename,
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size=input_path.stat().st_size, auto_detect=auto_detect)
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try:
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rendered = await asyncio.to_thread(render, input_path, job_dir)
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floors = rendered["floors"]
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if not floors:
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raise RuntimeError("Z výkresu se nepodařilo nic vyrenderovat")
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floor = floors[DEFAULT_FLOOR_INDEX]
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png_path = job_dir / floor["png"]
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legend_norm = floor.get("legend_norm_bbox")
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legend_pixel_box = None
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symbols = []
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detect_path = png_path
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if legend_norm:
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from PIL import Image as _Img
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full_img = _Img.open(png_path)
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W, H = full_img.size
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nx0, ny0, nx1, ny1 = legend_norm
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# Expand box generously around the LEGENDA text
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cx = (nx0 + nx1) / 2
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cy = (ny0 + ny1) / 2
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# Legend rows extend DOWNWARD from the LEGENDA header text.
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# Crop a narrow column starting just above the header.
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half_w = 0.10
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top_pad = 0.02
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below = 0.30
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px0 = max(0, int((cx - half_w) * W))
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px1 = min(W, int((cx + half_w) * W))
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py0 = max(0, int((cy - top_pad) * H))
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py1 = min(H, int((cy + below) * H))
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legend_crop = job_dir / "legend_area.png"
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crop_img = full_img.crop((px0, py0, px1, py1))
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crop_img.save(legend_crop, "PNG")
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detect_path = legend_crop
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legend_pixel_box = (px0, py0, px1 - px0, py1 - py0)
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logger.info("Legend area %dx%d ready", px1 - px0, py1 - py0)
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if auto_detect:
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symbols = await detect_legend(detect_path)
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# Symbol bboxes returned by vision are normalized to the image vision saw
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# (detect_path), which may be the cropped legend region or the full page.
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for s in symbols:
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bbox = s.get("bbox") or {}
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if all(k in bbox for k in ("x", "y", "w", "h")):
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crop_path = job_dir / f"sym_{s['id']}.png"
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try:
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crop_region(detect_path, bbox, crop_path, pad=1.5, min_px=120)
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s["crop_file"] = crop_path.name
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except Exception as exc:
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logger.warning("Crop failed for %s: %s", s.get("id"), exc)
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jobs[job_id] = {
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"filename": file.filename,
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"png_path": str(png_path),
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"job_dir": str(job_dir),
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"floors": floors,
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"symbols": symbols,
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"results": [],
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"legend_pixel_box": legend_pixel_box,
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"next_user_sym_id": 1,
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}
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return {"job_id": job_id, "symbols": symbols, "floor_count": len(floors),
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"auto_detect": auto_detect}
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except Exception as exc:
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logger.exception("Job %s failed: %s", job_id, exc)
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raise HTTPException(500, str(exc))
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@app.get("/api/preview/{job_id}")
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async def preview(job_id: str):
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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return FileResponse(jobs[job_id]["png_path"], media_type="image/png")
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@app.get("/api/symbol/{job_id}/{sym_id}")
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async def symbol_crop(job_id: str, sym_id: str):
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job_dir = Path(jobs[job_id]["job_dir"])
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crop_path = job_dir / f"sym_{sym_id}.png"
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if not crop_path.exists():
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raise HTTPException(404, "Crop not available")
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return FileResponse(crop_path, media_type="image/png")
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@app.get("/api/legend/{job_id}")
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async def legend_image(job_id: str):
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"""Return the cropped legend area image (what vision saw)."""
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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legend_path = Path(jobs[job_id]["job_dir"]) / "legend_area.png"
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if not legend_path.exists():
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# Fall back to full page if no legend crop
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legend_path = Path(jobs[job_id]["png_path"])
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return FileResponse(legend_path, media_type="image/png")
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class RecropRequest(BaseModel):
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bbox: dict # {x, y, w, h} normalized 0-1 relative to the legend image
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class CreateSymbolRequest(BaseModel):
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bbox: dict # normalized 0-1 relative to the FULL drawing image
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description: str
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source: str = "drawing" # "drawing" or "legend"
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@app.post("/api/symbols/{job_id}")
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async def create_user_symbol(job_id: str, req: CreateSymbolRequest):
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"""User drew a rectangle on the drawing → create a symbol from that crop."""
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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job_dir = Path(job["job_dir"])
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# Pick source image
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if req.source == "legend":
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src = job_dir / "legend_area.png"
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if not src.exists():
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src = Path(job["png_path"])
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else:
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src = Path(job["png_path"])
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sym_id = f"user_{job['next_user_sym_id']}"
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job["next_user_sym_id"] += 1
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crop_path = job_dir / f"sym_{sym_id}.png"
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# User's rectangle is exact — no padding, no min size enforcement.
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crop_region(src, req.bbox, crop_path, pad=0.0, min_px=0)
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sym = {
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"id": sym_id,
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"description": req.description or sym_id,
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"bbox": req.bbox,
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"crop_file": crop_path.name,
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"user_defined": True,
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}
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job["symbols"].append(sym)
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_log_action("create_symbol", job_id=job_id, sym_id=sym_id,
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description=req.description, bbox=req.bbox, source=req.source)
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return sym
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@app.post("/api/symbols/{job_id}/upload")
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async def upload_symbol_image(
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job_id: str,
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file: UploadFile = File(...),
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description: str = "",
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):
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"""Accept a pre-cropped symbol image as a template, bypassing the
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rectangle-drawing UI. Useful when the user has a clean PNG from elsewhere.
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"""
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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job_dir = Path(job["job_dir"])
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sym_id = f"user_{job['next_user_sym_id']}"
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job["next_user_sym_id"] += 1
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crop_path = job_dir / f"sym_{sym_id}.png"
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raw = await file.read()
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crop_path.write_bytes(raw)
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# Normalize: ensure RGB on white background (drop alpha so processing
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# doesn't see transparency as "not white")
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from PIL import Image as _Img
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img = _Img.open(crop_path)
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if img.mode in ("RGBA", "LA"):
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bg = _Img.new("RGB", img.size, (255, 255, 255))
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bg.paste(img, mask=img.split()[-1])
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bg.save(crop_path, "PNG")
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sym = {
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"id": sym_id,
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"description": (description or file.filename or sym_id).strip(),
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"crop_file": crop_path.name,
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"user_defined": True,
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"uploaded": True,
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}
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job["symbols"].append(sym)
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_log_action("upload_symbol", job_id=job_id, sym_id=sym_id,
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description=sym["description"], filename=file.filename,
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size=len(raw))
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return sym
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@app.delete("/api/symbols/{job_id}/{sym_id}")
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async def delete_symbol(job_id: str, sym_id: str):
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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job["symbols"] = [s for s in job["symbols"] if s["id"] != sym_id]
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crop = Path(job["job_dir"]) / f"sym_{sym_id}.png"
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if crop.exists():
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crop.unlink()
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return {"ok": True}
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@app.post("/api/auto-detect/{job_id}")
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async def trigger_auto_detect(job_id: str):
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"""Run the vision legend detection on demand (optional shortcut)."""
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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job_dir = Path(job["job_dir"])
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legend_path = job_dir / "legend_area.png"
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detect_path = legend_path if legend_path.exists() else Path(job["png_path"])
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found = await detect_legend(detect_path)
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for s in found:
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bbox = s.get("bbox") or {}
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if all(k in bbox for k in ("x", "y", "w", "h")):
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crop_path = job_dir / f"sym_{s['id']}.png"
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try:
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crop_region(detect_path, bbox, crop_path, pad=1.5, min_px=120)
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s["crop_file"] = crop_path.name
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except Exception as exc:
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logger.warning("Crop failed for %s: %s", s.get("id"), exc)
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# Replace vision-detected ones (keep user-defined)
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job["symbols"] = [s for s in job["symbols"] if s.get("user_defined")] + found
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return {"symbols": job["symbols"]}
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@app.get("/api/drawing/{job_id}")
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async def drawing_image(job_id: str):
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"""Return the rendered full drawing image (for the user to crop on)."""
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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return FileResponse(jobs[job_id]["png_path"], media_type="image/png")
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@app.get("/api/debug/{job_id}/{sym_id}")
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async def debug_symbol(job_id: str, sym_id: str):
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"""Diagnostics about a symbol's template: size, ink, match scores."""
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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crop = Path(job["job_dir"]) / f"sym_{sym_id}.png"
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if not crop.exists():
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raise HTTPException(404, "Crop not available")
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drawing = Path(job["png_path"])
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info = await asyncio.to_thread(debug_template, crop, drawing)
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return info
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@app.get("/api/debug-template/{job_id}/{sym_id}")
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async def debug_template_image(job_id: str, sym_id: str):
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"""Return the *processed* template (what the matcher actually sees)."""
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import cv2
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from counting import _prep, _crop_to_content
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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crop = Path(job["job_dir"]) / f"sym_{sym_id}.png"
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if not crop.exists():
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raise HTTPException(404, "Crop not available")
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tmpl = _prep(crop)
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tmpl = _crop_to_content(tmpl)
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out = Path(job["job_dir"]) / f"sym_{sym_id}_processed.png"
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cv2.imwrite(str(out), tmpl)
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return FileResponse(str(out), media_type="image/png")
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@app.post("/api/symbol/{job_id}/{sym_id}/recrop")
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async def recrop_symbol(job_id: str, sym_id: str, req: RecropRequest):
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"""Replace a symbol's crop. bbox is normalized 0-1 relative to the FULL
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drawing image (which is what the frontend shows in the editor)."""
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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src = Path(job["png_path"])
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crop_path = Path(job["job_dir"]) / f"sym_{sym_id}.png"
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crop_region(src, req.bbox, crop_path, pad=0.0, min_px=0)
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return {"ok": True, "crop_file": crop_path.name}
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class CountRequest(BaseModel):
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symbol_ids: list[str]
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threshold: float | None = None # Override default (0.7); lower = more matches
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@app.post("/api/count/{job_id}")
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async def count(job_id: str, req: CountRequest):
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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png = Path(job["png_path"])
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job_dir = Path(job["job_dir"])
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selected = [s for s in job["symbols"] if s["id"] in req.symbol_ids]
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# Determine the legend mask box (avoid matching the legend itself).
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legend_box = job.get("legend_pixel_box") # set during upload if available
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thr = req.threshold if req.threshold is not None else None
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def _count_one(sym):
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crop = job_dir / f"sym_{sym['id']}.png"
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if not crop.exists():
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return {"id": sym["id"], "description": sym["description"],
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"count": 0, "matches": [], "notes": "no crop"}
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try:
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kwargs = {"exclude_box": legend_box}
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if thr is not None:
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kwargs["threshold"] = thr
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res = count_template(crop, png, **kwargs)
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except Exception as exc:
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logger.exception("Counting failed for %s", sym["id"])
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return {"id": sym["id"], "description": sym["description"],
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"count": 0, "matches": [], "notes": f"error: {exc}"}
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return {
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"id": sym["id"],
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"description": sym["description"],
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"count": res["count"],
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"matches": res["matches"],
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"notes": "" if res["count"] else "žádné shody nenalezeny",
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}
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# Serialize OpenCV calls — parallel matchTemplate on a 4000px drawing
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# blows past 4GB peak memory and OOM-kills the container.
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results = []
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for s in selected:
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r = await asyncio.to_thread(_count_one, s)
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results.append(r)
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_log_action("count_one", job_id=job_id, sym_id=s["id"],
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description=s.get("description"), count=r.get("count"))
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job["results"] = list(results)
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# Trim matches from API response (keep them server-side for PDF export)
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response_results = [{k: v for k, v in r.items() if k != "matches"} | {"count": r["count"]}
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for r in job["results"]]
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return {"results": response_results}
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@app.get("/api/export/{job_id}")
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async def export(job_id: str):
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if job_id not in jobs:
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raise HTTPException(404, "Not found")
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job = jobs[job_id]
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||||
if not job["results"]:
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raise HTTPException(400, "Nejprve spočítejte symboly")
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out_path = Path(job["job_dir"]) / "counts.xlsx"
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export_to_excel(job["results"], job["filename"] or "drawing", str(out_path))
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stem = Path(job["filename"]).stem if job["filename"] else "drawing"
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return FileResponse(
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str(out_path),
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||||
media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
|
||||
filename=f"symboly_{stem}.xlsx",
|
||||
)
|
||||
|
||||
|
||||
@app.get("/api/export-pdf/{job_id}")
|
||||
async def export_pdf(job_id: str):
|
||||
if job_id not in jobs:
|
||||
raise HTTPException(404, "Not found")
|
||||
job = jobs[job_id]
|
||||
if not job["results"]:
|
||||
raise HTTPException(400, "Nejprve spočítejte symboly")
|
||||
png_path = Path(job["png_path"])
|
||||
stem = Path(job["filename"]).stem if job["filename"] else "drawing"
|
||||
out_path = Path(job["job_dir"]) / "annotated.pdf"
|
||||
await asyncio.to_thread(
|
||||
render_annotated_pdf, png_path, job["results"], out_path, stem,
|
||||
)
|
||||
return FileResponse(
|
||||
str(out_path),
|
||||
media_type="application/pdf",
|
||||
filename=f"vyznaceno_{stem}.pdf",
|
||||
)
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health():
|
||||
return {"status": "ok"}
|
||||
|
||||
|
||||
app.mount("/static", StaticFiles(directory="static"), name="static")
|
||||
Reference in New Issue
Block a user