- Dockerfile: add tesseract-ocr-osd for orientation detection data - receipt_parser: resize large phone photos to 1800px, convert to grayscale, sharpen before OCR; use psm 1 (auto + OSD) so rotated receipts are correctly oriented before text extraction - expenses_agent: tighten amount extraction prompt to pick the FINAL total, not subtotal or tax line, reducing misreads like 42.90->409.00 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
170 lines
6.0 KiB
Python
170 lines
6.0 KiB
Python
from __future__ import annotations
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import base64
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import hashlib
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import io
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import logging
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import re
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import zipfile
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from pathlib import Path
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logger = logging.getLogger(__name__)
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# Extract YYYYMMDD from filenames like 20260509_180857.jpg
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_DATE_PATTERN = re.compile(r'(\d{4})(\d{2})(\d{2})_\d{6}')
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_MIME = {
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'.jpg': 'image/jpeg', '.jpeg': 'image/jpeg',
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'.png': 'image/png', '.gif': 'image/gif',
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'.bmp': 'image/bmp', '.tiff': 'image/tiff', '.tif': 'image/tiff',
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'.webp': 'image/webp', '.pdf': 'application/pdf',
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'.html': 'text/html', '.htm': 'text/html',
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'.txt': 'text/plain', '.zip': 'application/zip',
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}
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_IMAGE_EXTS = {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.tif', '.webp'}
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def parse_upload(filename: str, data: bytes) -> list[dict]:
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"""
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Parse one uploaded file into a list of receipt dicts.
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ZIP files are recursively unpacked; all other types return a single entry.
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Each dict: {filename, text, b64, mimetype}
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"""
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ext = Path(filename).suffix.lower()
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if ext == '.zip':
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return _extract_zip(filename, data)
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b64 = base64.b64encode(data).decode()
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mimetype = _MIME.get(ext, 'application/octet-stream')
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sha256 = hashlib.sha256(data).hexdigest()
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# Extract date from timestamp-style filenames (e.g. 20260509_180857.jpg)
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date_from_name = None
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m = _DATE_PATTERN.search(filename)
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if m:
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date_from_name = f'{m.group(1)}-{m.group(2)}-{m.group(3)}'
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if ext in _IMAGE_EXTS:
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text = _ocr_image(data, filename)
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elif ext == '.pdf':
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text = _extract_pdf(data, filename)
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elif ext in ('.html', '.htm'):
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text = _extract_html(data, filename)
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elif ext == '.txt':
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text = data.decode('utf-8', errors='replace')
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else:
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try:
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text = data.decode('utf-8', errors='replace')
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except Exception:
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text = f'[Binary file: {filename}]'
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return [{'filename': filename, 'text': text, 'b64': b64, 'mimetype': mimetype,
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'sha256': sha256, 'date_from_name': date_from_name}]
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def _extract_zip(zip_filename: str, data: bytes) -> list[dict]:
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results = []
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try:
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with zipfile.ZipFile(io.BytesIO(data)) as zf:
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for member in zf.namelist():
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if member.endswith('/'):
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continue
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try:
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member_data = zf.read(member)
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results.extend(parse_upload(Path(member).name, member_data))
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except Exception as exc:
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logger.warning('receipt_parser: zip member %s failed: %s', member, exc)
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except Exception as exc:
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logger.error('receipt_parser: zip %s failed: %s', zip_filename, exc)
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return results
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def _ocr_image(data: bytes, filename: str) -> str:
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try:
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from PIL import Image, ImageFilter, ImageOps
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import pytesseract
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img = Image.open(io.BytesIO(data))
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# Resize very large images — tesseract is slower and less accurate at
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# phone-camera resolution; 1800px wide is plenty for receipt text.
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max_w = 1800
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if img.width > max_w:
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scale = max_w / img.width
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img = img.resize((max_w, int(img.height * scale)), Image.LANCZOS)
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# Convert to grayscale and sharpen — improves OCR on thermal receipts
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img = ImageOps.grayscale(img)
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img = img.filter(ImageFilter.SHARPEN)
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# Let Tesseract detect orientation (OSD) and use LSTM engine.
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# psm 1 = automatic + orientation detection so rotated/sideways receipts
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# are handled correctly. Fall back to psm 6 if OSD fails.
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config_osd = '--oem 3 --psm 1'
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config_block = '--oem 3 --psm 6'
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try:
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text = pytesseract.image_to_string(img, config=config_osd).strip()
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except Exception:
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text = pytesseract.image_to_string(img, config=config_block).strip()
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logger.debug('OCR %s: %d chars extracted', filename, len(text))
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return text
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except ImportError:
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logger.warning('pytesseract/Pillow not installed — OCR unavailable for %s', filename)
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return f'[Image: {filename} — install pytesseract+Pillow for OCR]'
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except Exception as exc:
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logger.warning('OCR failed for %s: %s', filename, exc)
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return f'[Image: {filename} — OCR failed: {exc}]'
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def _extract_pdf(data: bytes, filename: str) -> str:
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try:
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import pdfplumber
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parts = []
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with pdfplumber.open(io.BytesIO(data)) as pdf:
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for page in pdf.pages:
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t = page.extract_text()
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if t:
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parts.append(t)
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return '\n'.join(parts).strip()
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except ImportError:
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logger.warning('pdfplumber not installed — PDF extraction unavailable for %s', filename)
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return f'[PDF: {filename} — install pdfplumber for text extraction]'
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except Exception as exc:
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logger.warning('PDF extraction failed for %s: %s', filename, exc)
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return f'[PDF: {filename} — extraction failed: {exc}]'
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def _extract_html(data: bytes, filename: str) -> str:
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try:
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from html.parser import HTMLParser
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class _TextExtractor(HTMLParser):
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def __init__(self):
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super().__init__()
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self._parts: list[str] = []
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self._skip = False
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def handle_starttag(self, tag, attrs):
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if tag in ('script', 'style'):
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self._skip = True
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def handle_endtag(self, tag):
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if tag in ('script', 'style'):
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self._skip = False
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def handle_data(self, data):
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if not self._skip:
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s = data.strip()
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if s:
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self._parts.append(s)
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def text(self):
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return ' '.join(self._parts)
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parser = _TextExtractor()
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parser.feed(data.decode('utf-8', errors='replace'))
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return parser.text()
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except Exception as exc:
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logger.warning('HTML extraction failed for %s: %s', filename, exc)
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return f'[HTML: {filename} — extraction failed: {exc}]'
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