mirror of
https://github.com/paperless-ngx/paperless-ngx.git
synced 2025-04-02 13:45:10 -05:00
Merge branch 'dev' into feature-ocrmypdf
This commit is contained in:
jonaswinkler
commit
f956073f4a
1
Pipfile
1
Pipfile
@ -34,6 +34,7 @@ scikit-learn="~=0.23.2"
|
||||
whitenoise = "~=5.2.0"
|
||||
watchdog = "*"
|
||||
whoosh="~=2.7.4"
|
||||
inotify-simple = "*"
|
||||
ocrmypdf = "*"
|
||||
|
||||
[dev-packages]
|
||||
|
||||
282
Pipfile.lock
generated
282
Pipfile.lock
generated
@ -1,7 +1,7 @@
|
||||
{
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"_meta": {
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"hash": {
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"sha256": "cf1c008df0080c01273c032aef59bd841e4f27b66beaf3fa459665a7a7a4fcc4"
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"sha256": "e9792119f687757dd388e73827ddd4216910327d5b65a8b950d4b202679c36eb"
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"pipfile-spec": 6,
|
||||
"requires": {},
|
||||
@ -42,94 +42,6 @@
|
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|
||||
"version": "==3.2.1"
|
||||
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|
||||
"dateparser": {
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||||
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"sha256:7552c994f893b5cb8fcf103b4cd2ff7f57aab9bfd2619fdf0cf571c0740fd90b",
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||||
@ -209,14 +121,6 @@
|
||||
"index": "pypi",
|
||||
"version": "==20.0.4"
|
||||
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|
||||
"humanfriendly": {
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||||
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||||
"version": "==8.2"
|
||||
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||||
"imap-tools": {
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||||
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@ -225,12 +129,13 @@
|
||||
"index": "pypi",
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||||
"version": "==0.32.0"
|
||||
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|
||||
"img2pdf": {
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||||
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||||
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||||
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|
||||
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||||
"joblib": {
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||||
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||||
@ -249,51 +154,6 @@
|
||||
"index": "pypi",
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||||
"version": "==1.0.8"
|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
@ -335,14 +195,6 @@
|
||||
"markers": "python_version >= '3.6'",
|
||||
"version": "==1.19.4"
|
||||
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|
||||
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|
||||
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||||
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@ -358,14 +210,6 @@
|
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|
||||
"sha256:98aeb8b07a4127e1a30223bd933ef080bbd29aa88f801717ca6c5618380b8aa6"
|
||||
@ -373,33 +217,6 @@
|
||||
"index": "pypi",
|
||||
"version": "==2.1.5"
|
||||
},
|
||||
"pikepdf": {
|
||||
"hashes": [
|
||||
"sha256:0dd42f791f29e7e2ab120103605b9ddd65937c773a72d21341a56873a89e76c9",
|
||||
"sha256:12a1d243143cf972ce11def50f0bd1f6e630f5e660cdeddb2c7c49db5adad40a",
|
||||
"sha256:2e1713af11b71e95c2d218c10d68b6f8e813be19c8596c560f3c84617f6d5437",
|
||||
"sha256:2f90acad26d9939193946eb6ca8363fd3cf44b46b5c1409468906618bccb8113",
|
||||
"sha256:3c482fe30fd58ff385795605a9233f37f97fb83427c3e829b1a568a2a3b59f60",
|
||||
"sha256:3ddabfc33a8a7cecba76c1685ce5125fdf239a38d0854d7c2a703490b5783773",
|
||||
"sha256:61dd3f13b7416111d19bf493ce4e7281f63a1dd22c532200cbbcd65813ea43e4",
|
||||
"sha256:6ce42b7780835fb52452ccaff3a3ac1b28ae1f9d80faab59c559045d9fcb211d",
|
||||
"sha256:6dba75782f108ebbf3947fcb29ea0ba7da0482868e53f6602643adc36245201d",
|
||||
"sha256:716427a5c0372f3cc7dc282c4b49d49d8d5182a3e937739a4c3632151e74d6a4",
|
||||
"sha256:730ef4013099da7ea722a9b5659260097af6f47ddfa3c2abab4d4493de2591f3",
|
||||
"sha256:73e14bba4135adfb89ae2f2163369bd788ecf23839acc8d062d832118f07e288",
|
||||
"sha256:84df07acc8968051da33891af55a3ab1aa55453d83df4ce9b84d821eedc34583",
|
||||
"sha256:8f739e9c660d71cd479f11f9aa110857cf0d0d9c2472f40bbcbaf02f980355a1",
|
||||
"sha256:a20ca7adbb9d3da416cf5f6de0ebca53855f9a3b99acdd6ec864c61482894d71",
|
||||
"sha256:bc58d9486c0959619a2584e558a54d36468c6d1165cd9fe0bfb1ecc3e6b33c6a",
|
||||
"sha256:c0627930a17b3a5e1a7c9109099535259afc50fe006a05af9c3634de05abd318",
|
||||
"sha256:de5f445eaaadd7dae56e1043ab8ca5eef49ece302a4e37e1fc6d21b7dcfcfb1b",
|
||||
"sha256:de6aae7782db33f2cc71c9ba63b7e2ec0e0529843c065eac4e71fcbe043426e2",
|
||||
"sha256:e2efd844c09f8ce3103a93bfbd54983542a0a63c88bdc0f0cdbb2997f99a147d",
|
||||
"sha256:fdb481ad1219e8d667625afd2f01b26f98df079e4f66e7e49816ec20c8d8c401"
|
||||
],
|
||||
"markers": "python_version < '3.9'",
|
||||
"version": "==2.1.2"
|
||||
},
|
||||
"pillow": {
|
||||
"hashes": [
|
||||
"sha256:006de60d7580d81f4a1a7e9f0173dc90a932e3905cc4d47ea909bc946302311a",
|
||||
@ -435,14 +252,6 @@
|
||||
"index": "pypi",
|
||||
"version": "==8.0.1"
|
||||
},
|
||||
"pluggy": {
|
||||
"hashes": [
|
||||
"sha256:15b2acde666561e1298d71b523007ed7364de07029219b604cf808bfa1c765b0",
|
||||
"sha256:966c145cd83c96502c3c3868f50408687b38434af77734af1e9ca461a4081d2d"
|
||||
],
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
||||
"version": "==0.13.1"
|
||||
},
|
||||
"psycopg2-binary": {
|
||||
"hashes": [
|
||||
"sha256:0deac2af1a587ae12836aa07970f5cb91964f05a7c6cdb69d8425ff4c15d4e2c",
|
||||
@ -486,13 +295,13 @@
|
||||
"index": "pypi",
|
||||
"version": "==2.8.6"
|
||||
},
|
||||
"pycparser": {
|
||||
"pyocr": {
|
||||
"hashes": [
|
||||
"sha256:2d475327684562c3a96cc71adf7dc8c4f0565175cf86b6d7a404ff4c771f15f0",
|
||||
"sha256:7582ad22678f0fcd81102833f60ef8d0e57288b6b5fb00323d101be910e35705"
|
||||
"sha256:fa15adc7e1cf0d345a2990495fe125a947c6e09a60ddba0256a1c14b2e603179",
|
||||
"sha256:fd602af17b6e21985669aadc058a95f343ff921e962ed4aa6520ded32e4d1301"
|
||||
],
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
||||
"version": "==2.20"
|
||||
"index": "pypi",
|
||||
"version": "==0.7.2"
|
||||
},
|
||||
"python-dateutil": {
|
||||
"hashes": [
|
||||
@ -600,53 +409,6 @@
|
||||
],
|
||||
"version": "==2020.11.13"
|
||||
},
|
||||
"reportlab": {
|
||||
"hashes": [
|
||||
"sha256:06be7f04a631f02cd0202f7dee0d3e61dc265223f4ff861525ed7784b5552540",
|
||||
"sha256:0a788a537c48915eda083485b59ac40ac012fa7c43070069bde6eb5ea588313c",
|
||||
"sha256:1a7a38810e79653d0ea8e61db4f0517ac2a0e76edd2497cf6d4969dd3be30030",
|
||||
"sha256:22301773db730545b44d4c77d8f29baf5683ccabec9883d978e8b8eda6d2175f",
|
||||
"sha256:2906321b3d2779faafe47e2c13f9c69e1fb4ddb907f5a49cab3f9b0ea95df1f5",
|
||||
"sha256:2d65f9cc5c0d3f63b5d024e6cf92234f1ab1f267cc9e5a847ab5d3efe1c3cf3e",
|
||||
"sha256:2e012f7b845ef9f1f5bd63461d5201fa624b019a65ff5a93d0002b4f915bbc89",
|
||||
"sha256:31ccfdbf5bb5ec85f0397661085ce4c9e52537ca0d2bf4220259666a4dcc55c2",
|
||||
"sha256:3e10bd20c8ada9f7e1113157aa73b8e0048f2624e74794b73799c3deb13d7a3f",
|
||||
"sha256:440d5f86c2b822abdb7981d691a78bdcf56f4710174830283034235ab2af2969",
|
||||
"sha256:4f307accda32c9f17015ed77c7424f904514e349dff063f78d2462d715963e53",
|
||||
"sha256:59659ee8897950fd1acd41a9cc61f4afdfda52dc2bb69a1924ce68089491849d",
|
||||
"sha256:6216b11313467989ac9d9578ea3756d0af46e97184ee4e11a6b7ef652458f70d",
|
||||
"sha256:6268a9a3d75e714b22beeb7687270956b06b232ccfdf37b1c6462961eab04457",
|
||||
"sha256:6b226830f80df066d5986a3fdb3eb4d1b6320048f3d9ade539a6c03a5bc8b3ec",
|
||||
"sha256:6e10eba6a0e330096f4200b18824b3194c399329b7830e34baee1c04ea07f99f",
|
||||
"sha256:6e224c16c3d6fafdb2fb67b33c4b84d984ec34869834b3a137809f2fe5b84778",
|
||||
"sha256:7da162fa677b90bd14f19b20ff80fec18c24a31ac44e5342ba49e198b13c4f92",
|
||||
"sha256:8406e960a974a65b765c9ff74b269aa64718b4af1e8c511ebdbd9a5b44b0c7e6",
|
||||
"sha256:8999bb075102d1b8ca4aada6ca14653d52bf02e37fd064e477eb180741f75077",
|
||||
"sha256:8ae21aa94e405bf5171718f11ebc702a0edf18c91d88b14c5c5724cabd664673",
|
||||
"sha256:8f6163729612e815b89649aed2e237505362a78014199f819fd92f9e5c96769b",
|
||||
"sha256:9699fa8f0911ad56b46cc60bbaebe1557fd1c9e8da98185a7a1c0c40193eba48",
|
||||
"sha256:9a53d76eec33abda11617aad1c9f5f4a2d906dd2f92a03a3f1ea370efbb52c95",
|
||||
"sha256:9ed4d761b726ff411565eddb10cb37a6bca0ec873d9a18a83cf078f4502a2d94",
|
||||
"sha256:a020d308e7c2de284d5407e3c6c13e3977a62b314f7bfe19bcc69677931da589",
|
||||
"sha256:a2e6c15aecbe631245aab639751a58671312cced7e17de1ed9c45fb37036f6c9",
|
||||
"sha256:b10cb48606d97b70edb094576e3d493d40467395e4fc267655135a2c92defbe8",
|
||||
"sha256:b8d6e9df5181ed07b7ae145258eb69e686133afc97930af51a3c0c9d784d834d",
|
||||
"sha256:bbb297754f5cf25eb8fcb817752984252a7feb0ca83e383718e4eec2fb67ea32",
|
||||
"sha256:be90599e5e78c1ddfcfee8c752108def58b4c672ebcc4d3d9aa7fe65e7d3f16b",
|
||||
"sha256:bfdfad9b8ae00bd0752b77f954c7405327fd99b2cc6d5e4273e65be61429d56a",
|
||||
"sha256:c1e5ef5089e16b249388f65d8c8f8b74989e72eb8332060dc580a2ecb967cfc2",
|
||||
"sha256:c5ed342e29a5fd7eeb0f2ccf7e5b946b5f750f05633b2d6a94b1c02094a77967",
|
||||
"sha256:c7087a26b26aa82a3ba27e13e66f507cc697f9ceb4c046c0f758876b55f040a5",
|
||||
"sha256:cf589e980d92b0bf343fa512b9d3ae9ed0469cbffd99cb270b6c83da143cb437",
|
||||
"sha256:e6fb762e524a4fb118be9f44dbd9456cf80e42253ee8f1bdb0ea5c1f882d4ba8",
|
||||
"sha256:e961d3a84c65ca030963ca934a4faad2ac9fee75af36ba2f98733da7d3f7efab",
|
||||
"sha256:f2fde5abb6f21c1eff5430f380cdbbee7fdeda6af935a83730ddce9f0c4e504e",
|
||||
"sha256:f585b3bf7062c228306acd7f40b2ad915b32603228c19bb225952cc98fd2015a",
|
||||
"sha256:f955a6366cf8e6729776c96e281bede468acd74f6eb49a5bbb048646adaa43d8",
|
||||
"sha256:fe882fd348d8429debbdac4518d6a42888a7f4ad613dc596ce94788169caeb08"
|
||||
],
|
||||
"version": "==3.5.55"
|
||||
},
|
||||
"scikit-learn": {
|
||||
"hashes": [
|
||||
"sha256:090bbf144fd5823c1f2efa3e1a9bf180295b24294ca8f478e75b40ed54f8036e",
|
||||
@ -710,13 +472,6 @@
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
||||
"version": "==1.15.0"
|
||||
},
|
||||
"sortedcontainers": {
|
||||
"hashes": [
|
||||
"sha256:37257a32add0a3ee490bb170b599e93095eed89a55da91fa9f48753ea12fd73f",
|
||||
"sha256:59cc937650cf60d677c16775597c89a960658a09cf7c1a668f86e1e4464b10a1"
|
||||
],
|
||||
"version": "==2.3.0"
|
||||
},
|
||||
"sqlparse": {
|
||||
"hashes": [
|
||||
"sha256:017cde379adbd6a1f15a61873f43e8274179378e95ef3fede90b5aa64d304ed0",
|
||||
@ -733,14 +488,6 @@
|
||||
"markers": "python_version >= '3.5'",
|
||||
"version": "==2.1.0"
|
||||
},
|
||||
"tqdm": {
|
||||
"hashes": [
|
||||
"sha256:3d3f1470d26642e88bd3f73353cb6ff4c51ef7d5d7efef763238f4bc1f7e4e81",
|
||||
"sha256:5ff3f5232b19fa4c5531641e480b7fad4598819f708a32eb815e6ea41c5fa313"
|
||||
],
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
||||
"version": "==4.53.0"
|
||||
},
|
||||
"tzlocal": {
|
||||
"hashes": [
|
||||
"sha256:643c97c5294aedc737780a49d9df30889321cbe1204eac2c2ec6134035a92e44",
|
||||
@ -750,11 +497,11 @@
|
||||
},
|
||||
"watchdog": {
|
||||
"hashes": [
|
||||
"sha256:3caefdcc8f06a57fdc5ef2d22aa7c0bfda4f55e71a0bee74cbf3176d97536ef3",
|
||||
"sha256:e38bffc89b15bafe2a131f0e1c74924cf07dcec020c2e0a26cccd208831fcd43"
|
||||
"sha256:034c85530b647486e8c8477410fe79476511282658f2ce496f97106d9e5acfb8",
|
||||
"sha256:4214e1379d128b0588021880ccaf40317ee156d4603ac388b9adcf29165e0c04"
|
||||
],
|
||||
"index": "pypi",
|
||||
"version": "==0.10.4"
|
||||
"version": "==0.10.3"
|
||||
},
|
||||
"wcwidth": {
|
||||
"hashes": [
|
||||
@ -832,7 +579,6 @@
|
||||
"sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae",
|
||||
"sha256:fc323ffcaeaed0e0a02bf4d117757b98aed530d9ed4531e3e15460124c106691"
|
||||
],
|
||||
"markers": "python_version >= '3.1'",
|
||||
"version": "==3.0.4"
|
||||
},
|
||||
"coverage": {
|
||||
|
||||
@ -6,7 +6,8 @@ import re
|
||||
|
||||
from sklearn.feature_extraction.text import CountVectorizer
|
||||
from sklearn.neural_network import MLPClassifier
|
||||
from sklearn.preprocessing import MultiLabelBinarizer
|
||||
from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
|
||||
from sklearn.utils.multiclass import type_of_target
|
||||
|
||||
from documents.models import Document, MatchingModel
|
||||
from paperless import settings
|
||||
@ -27,7 +28,7 @@ def preprocess_content(content):
|
||||
|
||||
class DocumentClassifier(object):
|
||||
|
||||
FORMAT_VERSION = 5
|
||||
FORMAT_VERSION = 6
|
||||
|
||||
def __init__(self):
|
||||
# mtime of the model file on disk. used to prevent reloading when
|
||||
@ -54,6 +55,8 @@ class DocumentClassifier(object):
|
||||
"Cannor load classifier, incompatible versions.")
|
||||
else:
|
||||
if self.classifier_version > 0:
|
||||
# Don't be confused by this check. It's simply here
|
||||
# so that we wont log anything on initial reload.
|
||||
logger.info("Classifier updated on disk, "
|
||||
"reloading classifier models")
|
||||
self.data_hash = pickle.load(f)
|
||||
@ -122,9 +125,14 @@ class DocumentClassifier(object):
|
||||
labels_tags_unique = set([tag for tags in labels_tags for tag in tags])
|
||||
|
||||
num_tags = len(labels_tags_unique)
|
||||
|
||||
# substract 1 since -1 (null) is also part of the classes.
|
||||
num_correspondents = len(set(labels_correspondent)) - 1
|
||||
num_document_types = len(set(labels_document_type)) - 1
|
||||
|
||||
# union with {-1} accounts for cases where all documents have
|
||||
# correspondents and types assigned, so -1 isnt part of labels_x, which
|
||||
# it usually is.
|
||||
num_correspondents = len(set(labels_correspondent) | {-1}) - 1
|
||||
num_document_types = len(set(labels_document_type) | {-1}) - 1
|
||||
|
||||
logging.getLogger(__name__).debug(
|
||||
"{} documents, {} tag(s), {} correspondent(s), "
|
||||
@ -145,12 +153,23 @@ class DocumentClassifier(object):
|
||||
)
|
||||
data_vectorized = self.data_vectorizer.fit_transform(data)
|
||||
|
||||
self.tags_binarizer = MultiLabelBinarizer()
|
||||
labels_tags_vectorized = self.tags_binarizer.fit_transform(labels_tags)
|
||||
|
||||
# Step 3: train the classifiers
|
||||
if num_tags > 0:
|
||||
logging.getLogger(__name__).debug("Training tags classifier...")
|
||||
|
||||
if num_tags == 1:
|
||||
# Special case where only one tag has auto:
|
||||
# Fallback to binary classification.
|
||||
labels_tags = [label[0] if len(label) == 1 else -1
|
||||
for label in labels_tags]
|
||||
self.tags_binarizer = LabelBinarizer()
|
||||
labels_tags_vectorized = self.tags_binarizer.fit_transform(
|
||||
labels_tags).ravel()
|
||||
else:
|
||||
self.tags_binarizer = MultiLabelBinarizer()
|
||||
labels_tags_vectorized = self.tags_binarizer.fit_transform(
|
||||
labels_tags)
|
||||
|
||||
self.tags_classifier = MLPClassifier(tol=0.01)
|
||||
self.tags_classifier.fit(data_vectorized, labels_tags_vectorized)
|
||||
else:
|
||||
@ -222,6 +241,16 @@ class DocumentClassifier(object):
|
||||
X = self.data_vectorizer.transform([preprocess_content(content)])
|
||||
y = self.tags_classifier.predict(X)
|
||||
tags_ids = self.tags_binarizer.inverse_transform(y)[0]
|
||||
return tags_ids
|
||||
if type_of_target(y).startswith('multilabel'):
|
||||
# the usual case when there are multiple tags.
|
||||
return list(tags_ids)
|
||||
elif type_of_target(y) == 'binary' and tags_ids != -1:
|
||||
# This is for when we have binary classification with only one
|
||||
# tag and the result is to assign this tag.
|
||||
return [tags_ids]
|
||||
else:
|
||||
# Usually binary as well with -1 as the result, but we're
|
||||
# going to catch everything else here as well.
|
||||
return []
|
||||
else:
|
||||
return []
|
||||
|
||||
@ -1,11 +1,11 @@
|
||||
import logging
|
||||
import os
|
||||
from time import sleep
|
||||
|
||||
from django.conf import settings
|
||||
from django.core.management.base import BaseCommand
|
||||
from django_q.tasks import async_task
|
||||
from watchdog.events import FileSystemEventHandler
|
||||
from watchdog.observers import Observer
|
||||
from watchdog.observers.polling import PollingObserver
|
||||
|
||||
try:
|
||||
@ -13,25 +13,54 @@ try:
|
||||
except ImportError:
|
||||
INotify = flags = None
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _consume(file):
|
||||
try:
|
||||
if os.path.isfile(file):
|
||||
async_task("documents.tasks.consume_file",
|
||||
file,
|
||||
task_name=os.path.basename(file)[:100])
|
||||
else:
|
||||
logger.debug(
|
||||
f"Not consuming file {file}: File has moved.")
|
||||
|
||||
except Exception as e:
|
||||
# Catch all so that the consumer won't crash.
|
||||
# This is also what the test case is listening for to check for
|
||||
# errors.
|
||||
logger.error(
|
||||
"Error while consuming document: {}".format(e))
|
||||
|
||||
|
||||
def _consume_wait_unmodified(file, num_tries=20, wait_time=1):
|
||||
mtime = -1
|
||||
current_try = 0
|
||||
while current_try < num_tries:
|
||||
try:
|
||||
new_mtime = os.stat(file).st_mtime
|
||||
except FileNotFoundError:
|
||||
logger.debug(f"File {file} moved while waiting for it to remain "
|
||||
f"unmodified.")
|
||||
return
|
||||
if new_mtime == mtime:
|
||||
_consume(file)
|
||||
return
|
||||
mtime = new_mtime
|
||||
sleep(wait_time)
|
||||
current_try += 1
|
||||
|
||||
logger.error(f"Timeout while waiting on file {file} to remain unmodified.")
|
||||
|
||||
|
||||
class Handler(FileSystemEventHandler):
|
||||
|
||||
def _consume(self, file):
|
||||
if os.path.isfile(file):
|
||||
try:
|
||||
async_task("documents.tasks.consume_file",
|
||||
file,
|
||||
task_name=os.path.basename(file)[:100])
|
||||
except Exception as e:
|
||||
# Catch all so that the consumer won't crash.
|
||||
logging.getLogger(__name__).error(
|
||||
"Error while consuming document: {}".format(e))
|
||||
|
||||
def on_created(self, event):
|
||||
self._consume(event.src_path)
|
||||
_consume_wait_unmodified(event.src_path)
|
||||
|
||||
def on_moved(self, event):
|
||||
self._consume(event.src_path)
|
||||
_consume_wait_unmodified(event.dest_path)
|
||||
|
||||
|
||||
class Command(BaseCommand):
|
||||
@ -40,12 +69,15 @@ class Command(BaseCommand):
|
||||
consumption directory.
|
||||
"""
|
||||
|
||||
# This is here primarily for the tests and is irrelevant in production.
|
||||
stop_flag = False
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
|
||||
self.verbosity = 0
|
||||
self.logger = logging.getLogger(__name__)
|
||||
|
||||
BaseCommand.__init__(self, *args, **kwargs)
|
||||
self.observer = None
|
||||
|
||||
def add_arguments(self, parser):
|
||||
parser.add_argument(
|
||||
@ -54,38 +86,60 @@ class Command(BaseCommand):
|
||||
nargs="?",
|
||||
help="The consumption directory."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--oneshot",
|
||||
action="store_true",
|
||||
help="Run only once."
|
||||
)
|
||||
|
||||
def handle(self, *args, **options):
|
||||
|
||||
self.verbosity = options["verbosity"]
|
||||
directory = options["directory"]
|
||||
|
||||
logging.getLogger(__name__).info(
|
||||
"Starting document consumer at {}".format(
|
||||
directory
|
||||
)
|
||||
)
|
||||
f"Starting document consumer at {directory}")
|
||||
|
||||
# Consume all files as this is not done initially by the watchdog
|
||||
for entry in os.scandir(directory):
|
||||
if entry.is_file():
|
||||
async_task("documents.tasks.consume_file",
|
||||
entry.path,
|
||||
task_name=os.path.basename(entry.path)[:100])
|
||||
|
||||
# Start the watchdog. Woof!
|
||||
if settings.CONSUMER_POLLING > 0:
|
||||
logging.getLogger(__name__).info(
|
||||
"Using polling instead of file system notifications.")
|
||||
observer = PollingObserver(timeout=settings.CONSUMER_POLLING)
|
||||
if options["oneshot"]:
|
||||
return
|
||||
|
||||
if settings.CONSUMER_POLLING == 0 and INotify:
|
||||
self.handle_inotify(directory)
|
||||
else:
|
||||
observer = Observer()
|
||||
event_handler = Handler()
|
||||
observer.schedule(event_handler, directory, recursive=True)
|
||||
observer.start()
|
||||
self.handle_polling(directory)
|
||||
|
||||
logger.debug("Consumer exiting.")
|
||||
|
||||
def handle_polling(self, directory):
|
||||
logging.getLogger(__name__).info(
|
||||
f"Polling directory for changes: {directory}")
|
||||
self.observer = PollingObserver(timeout=settings.CONSUMER_POLLING)
|
||||
self.observer.schedule(Handler(), directory, recursive=False)
|
||||
self.observer.start()
|
||||
try:
|
||||
while observer.is_alive():
|
||||
observer.join(1)
|
||||
while self.observer.is_alive():
|
||||
self.observer.join(1)
|
||||
if self.stop_flag:
|
||||
self.observer.stop()
|
||||
except KeyboardInterrupt:
|
||||
observer.stop()
|
||||
observer.join()
|
||||
self.observer.stop()
|
||||
self.observer.join()
|
||||
|
||||
def handle_inotify(self, directory):
|
||||
logging.getLogger(__name__).info(
|
||||
f"Using inotify to watch directory for changes: {directory}")
|
||||
|
||||
inotify = INotify()
|
||||
inotify.add_watch(directory, flags.CLOSE_WRITE | flags.MOVED_TO)
|
||||
try:
|
||||
while not self.stop_flag:
|
||||
for event in inotify.read(timeout=1000, read_delay=1000):
|
||||
file = os.path.join(directory, event.name)
|
||||
if os.path.isfile(file):
|
||||
_consume(file)
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
|
||||
BIN
src/documents/tests/samples/simple.pdf
Normal file
BIN
src/documents/tests/samples/simple.pdf
Normal file
Binary file not shown.
BIN
src/documents/tests/samples/simple.zip
Normal file
BIN
src/documents/tests/samples/simple.zip
Normal file
Binary file not shown.
@ -5,6 +5,7 @@ from unittest import mock
|
||||
|
||||
from django.contrib.auth.models import User
|
||||
from django.test import override_settings
|
||||
from pathvalidate import ValidationError
|
||||
from rest_framework.test import APITestCase
|
||||
|
||||
from documents.models import Document, Correspondent, DocumentType, Tag
|
||||
@ -215,3 +216,41 @@ class DocumentApiTest(APITestCase):
|
||||
self.assertEqual(response.status_code, 200)
|
||||
self.assertEqual(response.data['documents_total'], 3)
|
||||
self.assertEqual(response.data['documents_inbox'], 1)
|
||||
|
||||
@mock.patch("documents.forms.async_task")
|
||||
def test_upload(self, m):
|
||||
|
||||
with open(os.path.join(os.path.dirname(__file__), "samples", "simple.pdf"), "rb") as f:
|
||||
response = self.client.post("/api/documents/post_document/", {"document": f})
|
||||
|
||||
self.assertEqual(response.status_code, 200)
|
||||
|
||||
m.assert_called_once()
|
||||
|
||||
self.assertEqual(m.call_args.kwargs['override_filename'], "simple.pdf")
|
||||
|
||||
@mock.patch("documents.forms.async_task")
|
||||
def test_upload_invalid_form(self, m):
|
||||
|
||||
with open(os.path.join(os.path.dirname(__file__), "samples", "simple.pdf"), "rb") as f:
|
||||
response = self.client.post("/api/documents/post_document/", {"documenst": f})
|
||||
self.assertEqual(response.status_code, 400)
|
||||
m.assert_not_called()
|
||||
|
||||
@mock.patch("documents.forms.async_task")
|
||||
def test_upload_invalid_file(self, m):
|
||||
|
||||
with open(os.path.join(os.path.dirname(__file__), "samples", "simple.zip"), "rb") as f:
|
||||
response = self.client.post("/api/documents/post_document/", {"document": f})
|
||||
self.assertEqual(response.status_code, 400)
|
||||
m.assert_not_called()
|
||||
|
||||
@mock.patch("documents.forms.async_task")
|
||||
@mock.patch("documents.forms.validate_filename")
|
||||
def test_upload_invalid_filename(self, validate_filename, async_task):
|
||||
validate_filename.side_effect = ValidationError()
|
||||
with open(os.path.join(os.path.dirname(__file__), "samples", "simple.pdf"), "rb") as f:
|
||||
response = self.client.post("/api/documents/post_document/", {"document": f})
|
||||
self.assertEqual(response.status_code, 400)
|
||||
|
||||
async_task.assert_not_called()
|
||||
|
||||
@ -1,8 +1,10 @@
|
||||
import tempfile
|
||||
from time import sleep
|
||||
from unittest import mock
|
||||
|
||||
from django.test import TestCase, override_settings
|
||||
|
||||
from documents.classifier import DocumentClassifier
|
||||
from documents.classifier import DocumentClassifier, IncompatibleClassifierVersionError
|
||||
from documents.models import Correspondent, Document, Tag, DocumentType
|
||||
|
||||
|
||||
@ -15,10 +17,12 @@ class TestClassifier(TestCase):
|
||||
def generate_test_data(self):
|
||||
self.c1 = Correspondent.objects.create(name="c1", matching_algorithm=Correspondent.MATCH_AUTO)
|
||||
self.c2 = Correspondent.objects.create(name="c2")
|
||||
self.c3 = Correspondent.objects.create(name="c3", matching_algorithm=Correspondent.MATCH_AUTO)
|
||||
self.t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
||||
self.t2 = Tag.objects.create(name="t2", matching_algorithm=Tag.MATCH_ANY, pk=34, is_inbox_tag=True)
|
||||
self.t3 = Tag.objects.create(name="t3", matching_algorithm=Tag.MATCH_AUTO, pk=45)
|
||||
self.dt = DocumentType.objects.create(name="dt", matching_algorithm=DocumentType.MATCH_AUTO)
|
||||
self.dt2 = DocumentType.objects.create(name="dt2", matching_algorithm=DocumentType.MATCH_AUTO)
|
||||
|
||||
self.doc1 = Document.objects.create(title="doc1", content="this is a document from c1", correspondent=self.c1, checksum="A", document_type=self.dt)
|
||||
self.doc2 = Document.objects.create(title="doc1", content="this is another document, but from c2", correspondent=self.c2, checksum="B")
|
||||
@ -59,8 +63,8 @@ class TestClassifier(TestCase):
|
||||
self.classifier.train()
|
||||
self.assertEqual(self.classifier.predict_correspondent(self.doc1.content), self.c1.pk)
|
||||
self.assertEqual(self.classifier.predict_correspondent(self.doc2.content), None)
|
||||
self.assertTupleEqual(self.classifier.predict_tags(self.doc1.content), (self.t1.pk,))
|
||||
self.assertTupleEqual(self.classifier.predict_tags(self.doc2.content), (self.t1.pk, self.t3.pk))
|
||||
self.assertListEqual(self.classifier.predict_tags(self.doc1.content), [self.t1.pk])
|
||||
self.assertListEqual(self.classifier.predict_tags(self.doc2.content), [self.t1.pk, self.t3.pk])
|
||||
self.assertEqual(self.classifier.predict_document_type(self.doc1.content), self.dt.pk)
|
||||
self.assertEqual(self.classifier.predict_document_type(self.doc2.content), None)
|
||||
|
||||
@ -71,6 +75,42 @@ class TestClassifier(TestCase):
|
||||
self.assertTrue(self.classifier.train())
|
||||
self.assertFalse(self.classifier.train())
|
||||
|
||||
def testVersionIncreased(self):
|
||||
|
||||
self.generate_test_data()
|
||||
self.assertTrue(self.classifier.train())
|
||||
self.assertFalse(self.classifier.train())
|
||||
|
||||
classifier2 = DocumentClassifier()
|
||||
|
||||
current_ver = DocumentClassifier.FORMAT_VERSION
|
||||
with mock.patch("documents.classifier.DocumentClassifier.FORMAT_VERSION", current_ver+1):
|
||||
# assure that we won't load old classifiers.
|
||||
self.assertRaises(IncompatibleClassifierVersionError, self.classifier.reload)
|
||||
|
||||
self.classifier.save_classifier()
|
||||
|
||||
# assure that we can load the classifier after saving it.
|
||||
classifier2.reload()
|
||||
|
||||
def testReload(self):
|
||||
|
||||
self.generate_test_data()
|
||||
self.assertTrue(self.classifier.train())
|
||||
self.classifier.save_classifier()
|
||||
|
||||
classifier2 = DocumentClassifier()
|
||||
classifier2.reload()
|
||||
v1 = classifier2.classifier_version
|
||||
|
||||
# change the classifier after some time.
|
||||
sleep(1)
|
||||
self.classifier.save_classifier()
|
||||
|
||||
classifier2.reload()
|
||||
v2 = classifier2.classifier_version
|
||||
self.assertNotEqual(v1, v2)
|
||||
|
||||
@override_settings(DATA_DIR=tempfile.mkdtemp())
|
||||
def testSaveClassifier(self):
|
||||
|
||||
@ -83,3 +123,112 @@ class TestClassifier(TestCase):
|
||||
new_classifier = DocumentClassifier()
|
||||
new_classifier.reload()
|
||||
self.assertFalse(new_classifier.train())
|
||||
|
||||
def test_one_correspondent_predict(self):
|
||||
c1 = Correspondent.objects.create(name="c1", matching_algorithm=Correspondent.MATCH_AUTO)
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1", correspondent=c1, checksum="A")
|
||||
|
||||
self.classifier.train()
|
||||
self.assertEqual(self.classifier.predict_correspondent(doc1.content), c1.pk)
|
||||
|
||||
def test_one_correspondent_predict_manydocs(self):
|
||||
c1 = Correspondent.objects.create(name="c1", matching_algorithm=Correspondent.MATCH_AUTO)
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1", correspondent=c1, checksum="A")
|
||||
doc2 = Document.objects.create(title="doc2", content="this is a document from noone", checksum="B")
|
||||
|
||||
self.classifier.train()
|
||||
self.assertEqual(self.classifier.predict_correspondent(doc1.content), c1.pk)
|
||||
self.assertIsNone(self.classifier.predict_correspondent(doc2.content))
|
||||
|
||||
def test_one_type_predict(self):
|
||||
dt = DocumentType.objects.create(name="dt", matching_algorithm=DocumentType.MATCH_AUTO)
|
||||
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1",
|
||||
checksum="A", document_type=dt)
|
||||
|
||||
self.classifier.train()
|
||||
self.assertEqual(self.classifier.predict_document_type(doc1.content), dt.pk)
|
||||
|
||||
def test_one_type_predict_manydocs(self):
|
||||
dt = DocumentType.objects.create(name="dt", matching_algorithm=DocumentType.MATCH_AUTO)
|
||||
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1",
|
||||
checksum="A", document_type=dt)
|
||||
|
||||
doc2 = Document.objects.create(title="doc1", content="this is a document from c2",
|
||||
checksum="B")
|
||||
|
||||
self.classifier.train()
|
||||
self.assertEqual(self.classifier.predict_document_type(doc1.content), dt.pk)
|
||||
self.assertIsNone(self.classifier.predict_document_type(doc2.content))
|
||||
|
||||
def test_one_tag_predict(self):
|
||||
t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
||||
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
|
||||
|
||||
doc1.tags.add(t1)
|
||||
self.classifier.train()
|
||||
self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
|
||||
|
||||
def test_one_tag_predict_unassigned(self):
|
||||
t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
||||
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
|
||||
|
||||
self.classifier.train()
|
||||
self.assertListEqual(self.classifier.predict_tags(doc1.content), [])
|
||||
|
||||
def test_two_tags_predict_singledoc(self):
|
||||
t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
||||
t2 = Tag.objects.create(name="t2", matching_algorithm=Tag.MATCH_AUTO, pk=121)
|
||||
|
||||
doc4 = Document.objects.create(title="doc1", content="this is a document from c4", checksum="D")
|
||||
|
||||
doc4.tags.add(t1)
|
||||
doc4.tags.add(t2)
|
||||
self.classifier.train()
|
||||
self.assertListEqual(self.classifier.predict_tags(doc4.content), [t1.pk, t2.pk])
|
||||
|
||||
def test_two_tags_predict(self):
|
||||
t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
||||
t2 = Tag.objects.create(name="t2", matching_algorithm=Tag.MATCH_AUTO, pk=121)
|
||||
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
|
||||
doc2 = Document.objects.create(title="doc1", content="this is a document from c2", checksum="B")
|
||||
doc3 = Document.objects.create(title="doc1", content="this is a document from c3", checksum="C")
|
||||
doc4 = Document.objects.create(title="doc1", content="this is a document from c4", checksum="D")
|
||||
|
||||
doc1.tags.add(t1)
|
||||
doc2.tags.add(t2)
|
||||
|
||||
doc4.tags.add(t1)
|
||||
doc4.tags.add(t2)
|
||||
self.classifier.train()
|
||||
self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
|
||||
self.assertListEqual(self.classifier.predict_tags(doc2.content), [t2.pk])
|
||||
self.assertListEqual(self.classifier.predict_tags(doc3.content), [])
|
||||
self.assertListEqual(self.classifier.predict_tags(doc4.content), [t1.pk, t2.pk])
|
||||
|
||||
def test_one_tag_predict_multi(self):
|
||||
t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
||||
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
|
||||
doc2 = Document.objects.create(title="doc2", content="this is a document from c2", checksum="B")
|
||||
|
||||
doc1.tags.add(t1)
|
||||
doc2.tags.add(t1)
|
||||
self.classifier.train()
|
||||
self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
|
||||
self.assertListEqual(self.classifier.predict_tags(doc2.content), [t1.pk])
|
||||
|
||||
def test_one_tag_predict_multi_2(self):
|
||||
t1 = Tag.objects.create(name="t1", matching_algorithm=Tag.MATCH_AUTO, pk=12)
|
||||
|
||||
doc1 = Document.objects.create(title="doc1", content="this is a document from c1", checksum="A")
|
||||
doc2 = Document.objects.create(title="doc2", content="this is a document from c2", checksum="B")
|
||||
|
||||
doc1.tags.add(t1)
|
||||
self.classifier.train()
|
||||
self.assertListEqual(self.classifier.predict_tags(doc1.content), [t1.pk])
|
||||
self.assertListEqual(self.classifier.predict_tags(doc2.content), [])
|
||||
|
||||
188
src/documents/tests/test_management_consumer.py
Normal file
188
src/documents/tests/test_management_consumer.py
Normal file
@ -0,0 +1,188 @@
|
||||
import filecmp
|
||||
import os
|
||||
import shutil
|
||||
import tempfile
|
||||
from threading import Thread
|
||||
from time import sleep
|
||||
from unittest import mock
|
||||
|
||||
from django.conf import settings
|
||||
from django.test import TestCase, override_settings
|
||||
|
||||
from documents.consumer import ConsumerError
|
||||
from documents.management.commands import document_consumer
|
||||
|
||||
|
||||
class ConsumerThread(Thread):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.cmd = document_consumer.Command()
|
||||
|
||||
def run(self) -> None:
|
||||
self.cmd.handle(directory=settings.CONSUMPTION_DIR, oneshot=False)
|
||||
|
||||
def stop(self):
|
||||
# Consumer checks this every second.
|
||||
self.cmd.stop_flag = True
|
||||
|
||||
|
||||
def chunked(size, source):
|
||||
for i in range(0, len(source), size):
|
||||
yield source[i:i+size]
|
||||
|
||||
|
||||
class TestConsumer(TestCase):
|
||||
|
||||
sample_file = os.path.join(os.path.dirname(__file__), "samples", "simple.pdf")
|
||||
|
||||
def setUp(self) -> None:
|
||||
patcher = mock.patch("documents.management.commands.document_consumer.async_task")
|
||||
self.task_mock = patcher.start()
|
||||
self.addCleanup(patcher.stop)
|
||||
|
||||
self.consume_dir = tempfile.mkdtemp()
|
||||
|
||||
override_settings(CONSUMPTION_DIR=self.consume_dir).enable()
|
||||
|
||||
def t_start(self):
|
||||
self.t = ConsumerThread()
|
||||
self.t.start()
|
||||
# give the consumer some time to do initial work
|
||||
sleep(1)
|
||||
|
||||
def tearDown(self) -> None:
|
||||
if self.t:
|
||||
self.t.stop()
|
||||
|
||||
def wait_for_task_mock_call(self):
|
||||
n = 0
|
||||
while n < 100:
|
||||
if self.task_mock.call_count > 0:
|
||||
# give task_mock some time to finish and raise errors
|
||||
sleep(1)
|
||||
return
|
||||
n += 1
|
||||
sleep(0.1)
|
||||
self.fail("async_task was never called")
|
||||
|
||||
# A bogus async_task that will simply check the file for
|
||||
# completeness and raise an exception otherwise.
|
||||
def bogus_task(self, func, filename, **kwargs):
|
||||
eq = filecmp.cmp(filename, self.sample_file, shallow=False)
|
||||
if not eq:
|
||||
print("Consumed an INVALID file.")
|
||||
raise ConsumerError("Incomplete File READ FAILED")
|
||||
else:
|
||||
print("Consumed a perfectly valid file.")
|
||||
|
||||
def slow_write_file(self, target, incomplete=False):
|
||||
with open(self.sample_file, 'rb') as f:
|
||||
pdf_bytes = f.read()
|
||||
|
||||
if incomplete:
|
||||
pdf_bytes = pdf_bytes[:len(pdf_bytes) - 100]
|
||||
|
||||
with open(target, 'wb') as f:
|
||||
# this will take 2 seconds, since the file is about 20k.
|
||||
print("Start writing file.")
|
||||
for b in chunked(1000, pdf_bytes):
|
||||
f.write(b)
|
||||
sleep(0.1)
|
||||
print("file completed.")
|
||||
|
||||
def test_consume_file(self):
|
||||
self.t_start()
|
||||
|
||||
f = os.path.join(self.consume_dir, "my_file.pdf")
|
||||
shutil.copy(self.sample_file, f)
|
||||
|
||||
self.wait_for_task_mock_call()
|
||||
|
||||
self.task_mock.assert_called_once()
|
||||
self.assertEqual(self.task_mock.call_args.args[1], f)
|
||||
|
||||
@override_settings(CONSUMER_POLLING=1)
|
||||
def test_consume_file_polling(self):
|
||||
self.test_consume_file()
|
||||
|
||||
def test_consume_existing_file(self):
|
||||
f = os.path.join(self.consume_dir, "my_file.pdf")
|
||||
shutil.copy(self.sample_file, f)
|
||||
|
||||
self.t_start()
|
||||
self.task_mock.assert_called_once()
|
||||
self.assertEqual(self.task_mock.call_args.args[1], f)
|
||||
|
||||
@override_settings(CONSUMER_POLLING=1)
|
||||
def test_consume_existing_file_polling(self):
|
||||
self.test_consume_existing_file()
|
||||
|
||||
@mock.patch("documents.management.commands.document_consumer.logger.error")
|
||||
def test_slow_write_pdf(self, error_logger):
|
||||
|
||||
self.task_mock.side_effect = self.bogus_task
|
||||
|
||||
self.t_start()
|
||||
|
||||
fname = os.path.join(self.consume_dir, "my_file.pdf")
|
||||
|
||||
self.slow_write_file(fname)
|
||||
|
||||
self.wait_for_task_mock_call()
|
||||
|
||||
error_logger.assert_not_called()
|
||||
|
||||
self.task_mock.assert_called_once()
|
||||
|
||||
self.assertEqual(self.task_mock.call_args.args[1], fname)
|
||||
|
||||
@override_settings(CONSUMER_POLLING=1)
|
||||
def test_slow_write_pdf_polling(self):
|
||||
self.test_slow_write_pdf()
|
||||
|
||||
@mock.patch("documents.management.commands.document_consumer.logger.error")
|
||||
def test_slow_write_and_move(self, error_logger):
|
||||
|
||||
self.task_mock.side_effect = self.bogus_task
|
||||
|
||||
self.t_start()
|
||||
|
||||
fname = os.path.join(self.consume_dir, "my_file.~df")
|
||||
fname2 = os.path.join(self.consume_dir, "my_file.pdf")
|
||||
|
||||
self.slow_write_file(fname)
|
||||
shutil.move(fname, fname2)
|
||||
|
||||
self.wait_for_task_mock_call()
|
||||
|
||||
self.task_mock.assert_called_once()
|
||||
self.assertEqual(self.task_mock.call_args.args[1], fname2)
|
||||
|
||||
error_logger.assert_not_called()
|
||||
|
||||
@override_settings(CONSUMER_POLLING=1)
|
||||
def test_slow_write_and_move_polling(self):
|
||||
self.test_slow_write_and_move()
|
||||
|
||||
@mock.patch("documents.management.commands.document_consumer.logger.error")
|
||||
def test_slow_write_incomplete(self, error_logger):
|
||||
|
||||
self.task_mock.side_effect = self.bogus_task
|
||||
|
||||
self.t_start()
|
||||
|
||||
fname = os.path.join(self.consume_dir, "my_file.pdf")
|
||||
self.slow_write_file(fname, incomplete=True)
|
||||
|
||||
self.wait_for_task_mock_call()
|
||||
|
||||
self.task_mock.assert_called_once()
|
||||
self.assertEqual(self.task_mock.call_args.args[1], fname)
|
||||
|
||||
# assert that we have an error logged with this invalid file.
|
||||
error_logger.assert_called_once()
|
||||
|
||||
@override_settings(CONSUMER_POLLING=1)
|
||||
def test_slow_write_incomplete_polling(self):
|
||||
self.test_slow_write_incomplete()
|
||||
Loading…
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Reference in New Issue
Block a user