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0a367ef a6c05d2 0a367ef a6c05d2 a28aeac 0a367ef a6c05d2 0a367ef a6c05d2 0a367ef a6c05d2 0a367ef a6c05d2 0a367ef a6c05d2 0a367ef a6c05d2 0a367ef a6c05d2 0a367ef a6c05d2 0a367ef 189d6c8 0a367ef 189d6c8 0a367ef 189d6c8 0a367ef a6c05d2 0a367ef a6c05d2 0a367ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | from __future__ import annotations
import logging
from dataclasses import dataclass, field
logger = logging.getLogger(__name__)
# Each doc type maps keywords to their individual signal weight (0.0–1.0).
# Higher weight = stronger evidence for that doc type when matched.
_KEYWORD_MAP: dict[str, dict[str, float]] = {
"mtr": {
"material test": 1.0,
"mill test": 1.0,
" mtr ": 1.0,
"material remarks": 0.7,
"inspection certificate": 0.7,
"inspection document": 0.7,
"certificate no.": 0.7,
"certificate number": 0.7,
"test specimen": 0.6,
"test certificate": 0.6,
"specimen": 0.4,
"product test": 0.4,
"heat test": 0.4,
"hardness test": 0.4,
"heat treatment": 0.4,
"chemical composition": 0.4,
"chemical analysis": 0.4,
"flang test": 0.4,
"flattening test": 0.4,
"flaring test": 0.4,
"material": 0.2,
# "certificate": 0.2,
},
"po": {
"purchase order": 1.0,
"sales order": 1.0,
"total sales order amount": 1.0,
"p.o.": 0.6,
" po ": 0.6,
"po date": 0.5,
"po number": 0.5,
" po#": 0.5,
" po.": 0.5,
" so#": 0.5,
" so.": 0.5,
"total due": 0.5,
},
"invoice": {
"invoice": 0.6,
"customer statement": 1.0,
"invoice #": 1.0,
"invoice date": 0.6,
"paid to": 0.5,
"payment type": 0.5,
"bill payment": 0.5,
},
"quote": {
"quotation": 1.0,
"request for quote": 1.0,
"rfq": 0.7,
"quote": 0.8,
"bid": 0.5,
},
}
# quote is classified but intentionally not routed to Dropbox
_ROUTABLE: frozenset[str] = frozenset({"po", "invoice", "mtr"})
@dataclass
class KeywordMatch:
keyword: str
weight: float
filename_hits: int
ocr_hits: int
filename_contrib: float
ocr_contrib: float
@dataclass
class ClassifyResult:
doc_type: str
reason: str
scores: dict[str, float] = field(default_factory=dict)
# Only doc types with at least one keyword hit are present.
breakdown: dict[str, list[KeywordMatch]] = field(default_factory=dict)
@dataclass
class ScoringWeights:
filename: float
ocr: float
freq_multiplier: float
min_threshold: float
def classify(
filename: str,
file_bytes: bytes,
content_type: str,
weights: ScoringWeights,
) -> ClassifyResult:
if not content_type.startswith("application/pdf"):
return ClassifyResult(doc_type="skipped", reason="non_pdf", scores={})
filename_lower = filename.lower() if filename else ""
ocr_text = _ocr_pdf(file_bytes)
scores: dict[str, float] = {}
breakdown: dict[str, list[KeywordMatch]] = {}
for doc_type, keywords in _KEYWORD_MAP.items():
score = 0.0
matches: list[KeywordMatch] = []
for keyword, kw_weight in keywords.items():
fn_hits = filename_lower.count(keyword)
ocr_hits = ocr_text.count(keyword) if ocr_text else 0
fn_contrib = 0.0
ocr_contrib = 0.0
if fn_hits > 0:
fn_contrib = weights.filename * kw_weight * (1 + (fn_hits - 1) * weights.freq_multiplier)
score += fn_contrib
if ocr_hits > 0:
ocr_contrib = weights.ocr * kw_weight * (1 + (ocr_hits - 1) * weights.freq_multiplier)
score += ocr_contrib
if fn_hits > 0 or ocr_hits > 0:
matches.append(KeywordMatch(
keyword=keyword,
weight=kw_weight,
filename_hits=fn_hits,
ocr_hits=ocr_hits,
filename_contrib=round(fn_contrib, 4),
ocr_contrib=round(ocr_contrib, 4),
))
scores[doc_type] = round(score, 4)
if matches:
breakdown[doc_type] = matches
max_score = max(scores.values(), default=0.0)
if max_score < weights.min_threshold:
return ClassifyResult(doc_type="unknown", reason="below_threshold", scores=scores, breakdown=breakdown)
above_threshold = [t for t, s in scores.items() if s >= weights.min_threshold]
if len(above_threshold) > 1:
return ClassifyResult(doc_type="ambiguous", reason="ambiguous", scores=scores, breakdown=breakdown)
winner = above_threshold[0]
if winner not in _ROUTABLE:
return ClassifyResult(doc_type=winner, reason="not_routable", scores=scores, breakdown=breakdown)
return ClassifyResult(doc_type=winner, reason="routed", scores=scores, breakdown=breakdown)
def _ocr_pdf(file_bytes: bytes) -> str:
from pdf2image import convert_from_bytes
import pytesseract
images = convert_from_bytes(file_bytes, first_page=1, last_page=2)
return "\n".join(pytesseract.image_to_string(img).lower() for img in images)
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