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feat(data-entry): Add unified receipt form with OCR confidence tracking

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New unified receipt creation system with:
- UnifiedReceiptForm component with inline OCR preview and confidence indicators
- Compact upload zone with drag-drop and camera support
- TVA and Payment fields with dynamic add/remove
- Supplier dual-field with autocomplete and OCR hint
- Receipt form sections with collapsible auxiliary data

Backend OCR improvements:
- Add confidence_tva and confidence_payment to extraction results
- Update TVA extraction to return confidence scores
- Include TVA (15%) and payment (10%) in overall_confidence calculation

Also includes:
- CSS design system rules documentation
- Port check helper function for service scripts
- Expanded design tokens documentation in CLAUDE.md

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Claude Agent
2026-01-08 21:48:37 +00:00
parent cc98d6f21f
commit b4a226409c
21 changed files with 4876 additions and 55 deletions
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73
backend/modules/data_entry/services/ocr/profiles/base.py
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@@ -13,8 +13,9 @@ Usage:
CUI_LIST = ["22891860"]
NAME_PATTERNS = ["LIDL", "LDL"]
def extract_tva_entries(self, text: str) -> List[dict]:
def extract_tva_entries(self, text: str) -> Tuple[List[dict], float]:
# Custom Lidl TVA extraction logic
# Returns (entries_list, confidence_score)
...
"""
@@ -331,7 +332,7 @@ class BaseStoreProfile(ABC):
# Extraction methods - override in subclasses as needed
# -------------------------------------------------------------------------
def extract_tva_entries(self, text: str) -> List[dict]:
def extract_tva_entries(self, text: str) -> Tuple[List[dict], float]:
"""
Extract TVA entries from receipt text - GENERIC implementation.
@@ -346,37 +347,53 @@ class BaseStoreProfile(ABC):
text: Raw OCR text from receipt
Returns:
List of dicts with keys: code, percent, amount
Tuple of (List of dicts with keys: code, percent, amount, confidence float)
"""
entries = []
max_confidence = 0.0
text_upper = text.upper()
# Step 1: Check for known non-VAT payer (by class flag or text detection)
if self.IS_NON_VAT_PAYER or self._is_non_vat_payer(text_upper):
return [] # No TVA entries for non-VAT payers
return ([], 0.0) # No TVA entries for non-VAT payers
# Step 2: Normalize OCR spaces in numbers
normalized = re.sub(r'(\d+)[.,]\s+(\d{2})', r'\1.\2', text_upper)
lines = normalized.split('\n')
# Step 3: Try all formats, collect candidates
# Step 3: Try all formats, collect candidates with confidence
candidates = []
# Try inline multi-rate (Lidl-style)
candidates.extend(self._try_tva_inline(normalized))
inline_entries, inline_conf = self._try_tva_inline(normalized)
candidates.extend(inline_entries)
if inline_conf > max_confidence:
max_confidence = inline_conf
# Try reversed format (Stepout-style)
candidates.extend(self._try_tva_reversed(normalized, lines))
reversed_entries, reversed_conf = self._try_tva_reversed(normalized, lines)
candidates.extend(reversed_entries)
if reversed_conf > max_confidence:
max_confidence = reversed_conf
# Try multiline format (Brick/Electrobering)
candidates.extend(self._try_tva_multiline(normalized, lines))
multiline_entries, multiline_conf = self._try_tva_multiline(normalized, lines)
candidates.extend(multiline_entries)
if multiline_conf > max_confidence:
max_confidence = multiline_conf
# Try table format (OMV-style)
candidates.extend(self._try_tva_table(normalized))
table_entries, table_conf = self._try_tva_table(normalized)
candidates.extend(table_entries)
if table_conf > max_confidence:
max_confidence = table_conf
# Try standard/fallback patterns
if not candidates:
candidates.extend(self._try_tva_standard(normalized))
standard_entries, standard_conf = self._try_tva_standard(normalized)
candidates.extend(standard_entries)
if standard_conf > max_confidence:
max_confidence = standard_conf
# Step 4: Deduplicate and return
seen = set()
@@ -386,7 +403,7 @@ class BaseStoreProfile(ABC):
entries.append(entry)
seen.add(key)
return entries
return (entries, max_confidence if entries else 0.0)
def _is_non_vat_payer(self, text: str) -> bool:
"""Check if receipt is from non-VAT payer."""
@@ -395,9 +412,10 @@ class BaseStoreProfile(ABC):
return True
return False
def _try_tva_inline(self, text: str) -> List[dict]:
def _try_tva_inline(self, text: str) -> Tuple[List[dict], float]:
"""Try Lidl-style inline format: 'TVA A 21,00% 7.71'"""
entries = []
max_confidence = 0.0
# Pattern: "TVA A 21,00% 7.71" or "TVA B 11,00% 2.13"
for pattern, confidence, fmt in self.TVA_PATTERNS:
if fmt != 'inline':
@@ -415,13 +433,16 @@ class BaseStoreProfile(ABC):
'percent': percent,
'amount': amount
})
if confidence > max_confidence:
max_confidence = confidence
except (ValueError, InvalidOperation, IndexError):
continue
return entries
return (entries, max_confidence)
def _try_tva_reversed(self, text: str, lines: List[str]) -> List[dict]:
def _try_tva_reversed(self, text: str, lines: List[str]) -> Tuple[List[dict], float]:
"""Try Stepout-style reversed format: '5.00% TUA*B 2.00' (rate BEFORE TVA marker)"""
entries = []
confidence = 0.97 # Default confidence for reversed format
# Pattern: "5.00% TUA*B 2.00" - procent BEFORE TVA, amount same line or next
for i, line in enumerate(lines):
# Try pattern with amount on SAME line: "5.00% TUA*B 2.00"
@@ -462,11 +483,12 @@ class BaseStoreProfile(ABC):
})
except (ValueError, InvalidOperation, IndexError):
continue
return entries
return (entries, confidence if entries else 0.0)
def _try_tva_multiline(self, text: str, lines: List[str]) -> List[dict]:
def _try_tva_multiline(self, text: str, lines: List[str]) -> Tuple[List[dict], float]:
"""Try multiline format: 'TOTAL TVA A - 19%' + amount on next line"""
entries = []
confidence = 0.95 # Default confidence for multiline format
# Pattern: "TOTAL TVA A - 19%" or "TOTAL TVA A 19%" on one line, amount on next
multiline_patterns = [
r'TOTAL\s+TVA\s*([A-D])\s*[-\s]+(\d{1,2})\s*%',
@@ -491,14 +513,15 @@ class BaseStoreProfile(ABC):
'percent': percent,
'amount': amount
})
return entries
return (entries, confidence)
except (ValueError, InvalidOperation, IndexError):
continue
return entries
return (entries, 0.0)
def _try_tva_table(self, text: str) -> List[dict]:
def _try_tva_table(self, text: str) -> Tuple[List[dict], float]:
"""Try OMV-style table format: 'A-21,00% 285,66 49,58'"""
entries = []
confidence = 0.96 # Default confidence for table format
# Pattern: "A-21,00% 285,66 49,58" (code-percent base_amount tva_amount)
table_pattern = r'([A-D])\s*[-:]\s*(\d{1,2})[.,\s]*\d{0,2}\s*%\s+([\d.,\s]+)\s+([\d.,\s]+)'
for match in re.finditer(table_pattern, text, re.IGNORECASE):
@@ -530,13 +553,15 @@ class BaseStoreProfile(ABC):
'percent': 19, # Default rate
'amount': amount
})
confidence = 0.90 # Lower confidence for fallback
except (ValueError, InvalidOperation):
pass
return entries
return (entries, confidence if entries else 0.0)
def _try_tva_standard(self, text: str) -> List[dict]:
def _try_tva_standard(self, text: str) -> Tuple[List[dict], float]:
"""Try standard TVA patterns as fallback"""
entries = []
matched_confidence = 0.0
standard_fmts = ['standard', 'bon', 'percent', 'coded', 'fallback', 'books']
for pattern, confidence, fmt in self.TVA_PATTERNS:
if fmt not in standard_fmts:
@@ -563,7 +588,7 @@ class BaseStoreProfile(ABC):
'percent': percent,
'amount': amount
})
return entries
return (entries, confidence)
elif len(groups) == 1:
# Just amount
amount = self._parse_decimal(self._clean_ocr_number(groups[0]))
@@ -573,10 +598,10 @@ class BaseStoreProfile(ABC):
'percent': 19,
'amount': amount
})
return entries
return (entries, confidence)
except (ValueError, InvalidOperation, IndexError):
continue
return entries
return (entries, matched_confidence)
def _clean_ocr_number(self, value: str) -> str:
"""Remove OCR spaces from numbers (e.g., '55, 22' -> '55,22')."""