from __future__ import annotations import base64 import hashlib import io import logging import re import zipfile from pathlib import Path logger = logging.getLogger(__name__) # Extract YYYYMMDD from filenames like 20260509_180857.jpg _DATE_PATTERN = re.compile(r'(\d{4})(\d{2})(\d{2})_\d{6}') _MIME = { '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg', '.png': 'image/png', '.gif': 'image/gif', '.bmp': 'image/bmp', '.tiff': 'image/tiff', '.tif': 'image/tiff', '.webp': 'image/webp', '.pdf': 'application/pdf', '.html': 'text/html', '.htm': 'text/html', '.txt': 'text/plain', '.zip': 'application/zip', } _IMAGE_EXTS = {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.tif', '.webp'} def parse_upload(filename: str, data: bytes) -> list[dict]: """ Parse one uploaded file into a list of receipt dicts. ZIP files are recursively unpacked; all other types return a single entry. Each dict: {filename, text, b64, mimetype} """ ext = Path(filename).suffix.lower() if ext == '.zip': return _extract_zip(filename, data) b64 = base64.b64encode(data).decode() mimetype = _MIME.get(ext, 'application/octet-stream') sha256 = hashlib.sha256(data).hexdigest() # Extract date from timestamp-style filenames (e.g. 20260509_180857.jpg) date_from_name = None m = _DATE_PATTERN.search(filename) if m: date_from_name = f'{m.group(1)}-{m.group(2)}-{m.group(3)}' if ext in _IMAGE_EXTS: text = _ocr_image(data, filename) elif ext == '.pdf': text = _extract_pdf(data, filename) elif ext in ('.html', '.htm'): text = _extract_html(data, filename) elif ext == '.txt': text = data.decode('utf-8', errors='replace') else: try: text = data.decode('utf-8', errors='replace') except Exception: text = f'[Binary file: {filename}]' return [{'filename': filename, 'text': text, 'b64': b64, 'mimetype': mimetype, 'sha256': sha256, 'date_from_name': date_from_name}] def _extract_zip(zip_filename: str, data: bytes) -> list[dict]: results = [] try: with zipfile.ZipFile(io.BytesIO(data)) as zf: for member in zf.namelist(): if member.endswith('/'): continue try: member_data = zf.read(member) results.extend(parse_upload(Path(member).name, member_data)) except Exception as exc: logger.warning('receipt_parser: zip member %s failed: %s', member, exc) except Exception as exc: logger.error('receipt_parser: zip %s failed: %s', zip_filename, exc) return results def _ocr_image(data: bytes, filename: str) -> str: """Extract text from a receipt image using Tesseract.""" return _ocr_image_tesseract(data, filename) def _ocr_image_tesseract(data: bytes, filename: str) -> str: """Tesseract-based OCR pipeline with phone-photo preprocessing.""" try: from PIL import Image, ImageFilter, ImageOps import pytesseract img = Image.open(io.BytesIO(data)) # ── Step 1: EXIF rotation correction ───────────────────────────────── # Phone photos are stored with EXIF orientation metadata but the pixel # data is not actually rotated. Without this fix Tesseract reads a # portrait receipt as a landscape image and produces garbage. try: img = ImageOps.exif_transpose(img) except Exception: pass # exif_transpose requires Pillow >= 6.0 # ── Step 1b: Content-based rotation correction ─────────────────────── # EXIF transpose (Step 1) only corrects for phone-tilt metadata. # If the receipt was physically laid sideways in the frame (e.g. a # landscape receipt photographed with the phone upright), the pixels # are genuinely rotated and EXIF can't help. Ask Tesseract's OSD # engine to detect the text orientation and rotate to correct it. try: osd = pytesseract.image_to_osd(img, config='--psm 0') _am = re.search(r'Rotate:\s*(\d+)', osd) if _am: _angle = int(_am.group(1)) if _angle: img = img.rotate(_angle, expand=True) logger.debug('OSD: rotated %s by %d°', filename, _angle) except Exception: pass # OSD unavailable or not enough text — proceed without correction # ── Step 2: Resize to working width (1800px) ────────────────────────── max_w = 1800 if img.width > max_w: scale = max_w / img.width img = img.resize((max_w, int(img.height * scale)), Image.LANCZOS) # Upscale very small images — Tesseract accuracy drops below ~600px elif img.width < 600: scale = 600 / img.width img = img.resize((600, int(img.height * scale)), Image.LANCZOS) # ── Step 3: Grayscale + contrast ───────────────────────────────────── img = ImageOps.grayscale(img) img = ImageOps.autocontrast(img) img_gray = img # save grayscale for fallback — before binarization # ── Step 4: Sharpen then binarize ───────────────────────────────────── # Sharpen first so edges are crisp before thresholding. # Threshold 160 (was 140) — gentler for faint thermal-print receipts # where light gray text would be wiped out by the stricter threshold. img = img.filter(ImageFilter.SHARPEN) img = img.point(lambda x: 0 if x < 160 else 255) # ── Step 5: OCR — try PSM modes best-suited for receipt layout ──────── # PSM 6 = single uniform text block (best for single-column receipts) # PSM 4 = single column, variable text sizes (wider fallback) # PSM 11 = sparse text — last resort for badly segmented images for psm in (6, 4, 11): try: text = pytesseract.image_to_string( img, config=f'--oem 3 --psm {psm}').strip() if len(text) >= 20: logger.debug('Tesseract OCR %s: psm=%d %d chars', filename, psm, len(text)) return text except Exception: pass # ── Step 5b: Grayscale fallback ─────────────────────────────────────── # Binarization at threshold 160 can destroy dot-matrix and certain # thermal-print fonts (e.g. parking kiosk receipts) where character # pixels are close to the threshold and get wiped to white. If every # binarized attempt failed, retry on the plain grayscale image — # Tesseract handles grey-level input reasonably well for these cases. for psm in (6, 4, 11): try: text = pytesseract.image_to_string( img_gray, config=f'--oem 3 --psm {psm}').strip() if len(text) >= 20: logger.debug('Tesseract grayscale fallback %s: psm=%d %d chars', filename, psm, len(text)) return text except Exception: pass logger.warning('Tesseract OCR %s: all PSM modes returned < 20 chars', filename) return '' except ImportError: logger.warning('pytesseract/Pillow not installed — OCR unavailable for %s', filename) return f'[Image: {filename} — install pytesseract+Pillow for OCR]' except Exception as exc: logger.warning('Tesseract OCR failed for %s: %s', filename, exc) return f'[Image: {filename} — OCR failed: {exc}]' def _extract_pdf(data: bytes, filename: str) -> str: try: import pdfplumber parts = [] with pdfplumber.open(io.BytesIO(data)) as pdf: for page in pdf.pages: t = page.extract_text() if t: parts.append(t) return '\n'.join(parts).strip() except ImportError: logger.warning('pdfplumber not installed — PDF extraction unavailable for %s', filename) return f'[PDF: {filename} — install pdfplumber for text extraction]' except Exception as exc: logger.warning('PDF extraction failed for %s: %s', filename, exc) return f'[PDF: {filename} — extraction failed: {exc}]' def _extract_html(data: bytes, filename: str) -> str: try: from html.parser import HTMLParser class _TextExtractor(HTMLParser): def __init__(self): super().__init__() self._parts: list[str] = [] self._skip = False def handle_starttag(self, tag, attrs): if tag in ('script', 'style'): self._skip = True def handle_endtag(self, tag): if tag in ('script', 'style'): self._skip = False def handle_data(self, data): if not self._skip: s = data.strip() if s: self._parts.append(s) def text(self): return ' '.join(self._parts) parser = _TextExtractor() parser.feed(data.decode('utf-8', errors='replace')) return parser.text() except Exception as exc: logger.warning('HTML extraction failed for %s: %s', filename, exc) return f'[HTML: {filename} — extraction failed: {exc}]'