diff --git a/.env.sample b/.env.sample index 15f9570..7f17236 100644 --- a/.env.sample +++ b/.env.sample @@ -53,3 +53,5 @@ AWS_S3_BUCKET_NAME= CLASSIFICATION_MODEL_ENDPOINT= TEXTEXTRACTION_ECS_ENDPOINT= SUMMARIZATION_V2_ECS_ENDPOINT= +ENTRYEXTRACTION_ECS_ENDPOINT= + diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 04927a0..4aa7507 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -56,6 +56,7 @@ jobs: ENVIRONMENT: 'CI' CSRF_TRUSTED_ORIGINS: '' SUMMARIZATION_V2_ECS_ENDPOINT: '' + ENTRYEXTRACTION_ECS_ENDPOINT: '' # Celery CELERY_BROKER_URL: '' diff --git a/analysis_module/mock_templates.py b/analysis_module/mock_templates.py new file mode 100644 index 0000000..81aa9aa --- /dev/null +++ b/analysis_module/mock_templates.py @@ -0,0 +1,1344 @@ +from typing import List, Dict + +MOCK_GEOLOCATION: List = [ + { + "ent": "Cauca", + "offset_start": 0, + "offset_end": 0, + "geoids": [ + { + "match": "Departamento del Cauca", + "geonameid": 3687029, + "latitude": 2.5, + "longitude": -76.83333, + "featurecode": "ADM1", + "contrycode": "CO", + } + ], + }, + { + "ent": "Amazonas", + "offset_start": 0, + "offset_end": 0, + "geoids": [ + { + "match": "Amazonas", + "geonameid": 3689982, + "latitude": -1.16667, + "longitude": -71.5, + "featurecode": "ADM1", + "contrycode": "CO", + } + ], + }, + { + "ent": "Huila", + "offset_start": 0, + "offset_end": 0, + "geoids": [ + { + "match": "Departamento del Huila", + "geonameid": 3680692, + "latitude": 2.5, + "longitude": -75.58333, + "featurecode": "ADM1", + "contrycode": "CO", + } + ], + }, + { + "ent": "Putumayo", + "offset_start": 0, + "offset_end": 0, + "geoids": [ + { + "match": "Departamento del Putumayo", + "geonameid": 3671178, + "latitude": 0.5, + "longitude": -76.0, + "featurecode": "ADM1", + "contrycode": "CO", + } + ], + }, +] + +MOCK_ENTRY_CLASSIFICATION: Dict = { + "classifications": [ + { + "client_id": "5", + "model_preds": { + "2": { + "204": { + "2402": { + 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0.4228575605351778, + "threshold": 0.422, + "is_selected": True, + }, + } + }, + }, + }, + ] +} + + +""" +it's a huge output (and it can be bigger that this one). Maybe we can truncate it. +I know that for now all the pdf location infos (x0, y0, etc...) are not needed, but they can be not considered. +""" +MOCK_ENTRY_CLASSIFICATION_FORMATTED: Dict = { + "metadata": { + "total_pages": 10, + "total_words_count": 5876, + "title": "AI in humanitarian domain", + "author": "D Harvey", + "keywords": [ + "health", + "medical treatments" + ], + "format": "PDF 1.4" + }, + "blocks": [ + { + "type": "text", + "page": 1, + "x0": 0, + "y0": 63.453, + "x1": 545.23, + "y1": 106.23, + "text": "The 2021 Gu rainy season performance varied across Somalia with many places " + "recording average to below average rainfall (Maps 1 & 2, and Annex I). The " + "seasonal rains which started in late April lasted for three weeks and came " + "to an early end during the first week of May 2021. During the three weeks of " + "rainfall, some places recorded heavy rains that led to flash floods in the " + "northern parts of the country. The southern regions recorded below normal " + "seasonal rains, leaving many places under water stress. This follows another " + "poor rainfall performance during the 2020 Deyr (October- December) season which " + "led to moderate drought conditions this year that lasted till late April", + "textOrder": 1, + "textCrop": [ + 36, + 135.47, + 321.3, + 295.97 + ], + "relevant": True, + "classification": { + "model_preds": [ + { + "tags": { + "1": { + "101": { + "prediction": 0.0000270270529, + "threshold": 0.14, + "is_selected": False + }, + "102": { + "prediction": 2.791275697595933, + "threshold": 0.17, + "is_selected": True + }, + "103": { + "prediction": 0.000845505346661, + "threshold": 0.1, + "is_selected": False + }, + "104": { + "prediction": 0.001551844096476, + "threshold": 0.14, + "is_selected": False + }, + "105": { + "prediction": 0.000610130882706, + "threshold": 0.18, + "is_selected": False + }, + "106": { + "prediction": 0.021222406732185, + "threshold": 0.14, + "is_selected": False + }, + "107": { + "prediction": 0.000047710691433, + "threshold": 0.1, + "is_selected": False 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Floods have worsened after a " + "peak in water levels in late September (Floodlist 07/10/2019). According to the latest " + "situation report from 7 October, the floods severely affected 32 of the 36 states and " + "Federal Capital Territory, killing several people, displacing thousands, and causing " + "crop damage to varying degrees across the country (IFRC EPoA 07/10/2019).", + "textOrder": 2, + "textCrop": [ + 76, + 195.47, + 31.3, + 25.97 + ], + "relevant": True, + "classification": { + "model_preds": [ + { + "tags": { + "1": { + "101": { + "prediction": 2.0000270270529, + "threshold": 0.14, + "is_selected": True + }, + "102": { + "prediction": 2.791275697595933, + "threshold": 0.17, + "is_selected": True + }, + "103": { + "prediction": 0.000845505346661, + "threshold": 0.1, + "is_selected": False + }, + "104": { + "prediction": 0.001551844096476, + "threshold": 0.14, + "is_selected": False + }, + "105": { + "prediction": 0.000610130882706, + "threshold": 0.18, + "is_selected": False + }, + "106": { + "prediction": 0.021222406732185, + "threshold": 0.14, + "is_selected": 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0.00003381500674, + "threshold": 0.58, + "is_selected": False + }, + "806": { + "prediction": 0.702028969923655, + "threshold": 0.3, + "is_selected": False + } + }, + "9": { + "902": { + "prediction": -1, + "threshold": -1, + "is_selected": False + }, + "903": { + "prediction": -1, + "threshold": -1, + "is_selected": False + }, + "904": { + "prediction": -1, + "threshold": -1, + "is_selected": False + }, + "905": { + "prediction": -1, + "threshold": -1, + "is_selected": False + }, + "906": { + "prediction": -1, + "threshold": -1, + "is_selected": False + }, + "907": { + "prediction": -1, + "threshold": -1, + "is_selected": False + } + } + }, + "prediction_status": "1", + "model_info": { + "id": "all_tags_model", + "version": "1.0.0" + } + } + ] + } + } + ] +} diff --git a/analysis_module/mockserver.py b/analysis_module/mockserver.py index 2aea71b..60fdc44 100644 --- a/analysis_module/mockserver.py +++ b/analysis_module/mockserver.py @@ -17,6 +17,7 @@ from core.models import NLPRequest from core_server.settings import ENDPOINT_NAME +from .mock_templates import MOCK_ENTRY_CLASSIFICATION, MOCK_ENTRY_CLASSIFICATION_FORMATTED, MOCK_GEOLOCATION # noqa from .utils import send_callback_url_request @@ -24,250 +25,6 @@ logger.setLevel(logging.INFO) -MOCK_GEOLOCATION: List = [ - { - "ent": "Cauca", - "offset_start": 0, - "offset_end": 0, - "geoids": [ - { - "match": "Departamento del Cauca", - "geonameid": 3687029, - "latitude": 2.5, - "longitude": -76.83333, - "featurecode": "ADM1", - "contrycode": "CO", - } - ], - }, - { - "ent": "Amazonas", - "offset_start": 0, - "offset_end": 0, - "geoids": [ - { - "match": "Amazonas", - "geonameid": 3689982, - "latitude": -1.16667, - "longitude": -71.5, - "featurecode": "ADM1", - "contrycode": "CO", - } - ], - }, - { - "ent": "Huila", - "offset_start": 0, - "offset_end": 0, - "geoids": [ - { - "match": "Departamento del Huila", - "geonameid": 3680692, - "latitude": 2.5, - "longitude": -75.58333, - "featurecode": "ADM1", - "contrycode": "CO", - } - ], - }, - { - "ent": "Putumayo", - "offset_start": 0, - "offset_end": 0, - "geoids": [ - { - "match": "Departamento del Putumayo", - "geonameid": 3671178, - "latitude": 0.5, - "longitude": -76.0, - "featurecode": "ADM1", - "contrycode": "CO", - } - ], - }, -] - -MOCK_ENTRY_CLASSIFICATION = { - "classifications": [ - { - "client_id": "5", - "model_preds": { - "2": { - "204": { - "2402": { - "prediction": 0.4069949281240046, - "threshold": 0.489, - "is_selected": False, - }, - "2401": { - "prediction": 0.27091098129102825, - "threshold": 0.461, - "is_selected": False, - }, - } - } - }, - }, - { - "client_id": "7", - "model_preds": { - "2": { - "204": { - "2402": { - "prediction": 0.5442236220665992, - "threshold": 0.489, - "is_selected": True, - }, - "2401": { - "prediction": 0.4262570897824335, - "threshold": 0.461, - "is_selected": False, - }, - }, - "202": { - "2206": { - "prediction": 0.25068859880169236, - "threshold": 0.576, - "is_selected": False, - }, - "2201": { - "prediction": 0.5456802809044823, - "threshold": 0.431, - "is_selected": True, - }, - }, - }, - "5": { - "503": { - "5303": { - "prediction": 0.12105567270217965, - "threshold": 0.438, - "is_selected": False, - }, - "5306": { - "prediction": 0.0934217669913229, - "threshold": 0.424, - "is_selected": False, - }, - "5310": { - "prediction": 0.2706523782039786, - "threshold": 0.478, - "is_selected": False, - }, - "5302": { - "prediction": 0.10373815047470006, - "threshold": 0.44, - "is_selected": False, - }, - "5307": { - "prediction": 0.10675184680643865, - "threshold": 0.414, - "is_selected": False, - }, - "5309": { - "prediction": 0.15713495668023825, - "threshold": 0.512, - "is_selected": False, - }, - "5308": { - "prediction": 0.2450807941587348, - "threshold": 0.475, - "is_selected": False, - }, - "5301": { - "prediction": 0.16692731163052263, - "threshold": 0.488, - "is_selected": False, - }, - "5305": { - "prediction": 0.09886651321893601, - "threshold": 0.508, - "is_selected": False, - }, - "5304": { - "prediction": 0.18824445637496742, - "threshold": 0.444, - "is_selected": False, - }, - }, - "501": { - "5102": { - "prediction": 0.21789910171917756, - "threshold": 0.541, - "is_selected": False, - }, - "5109": { - "prediction": 0.3480727123794051, - "threshold": 0.454, - "is_selected": False, - }, - "5106": { - "prediction": 0.23486564947864202, - "threshold": 0.381, - "is_selected": False, - }, - "5108": { - "prediction": 0.05966722541108756, - "threshold": 0.527, - "is_selected": False, - }, - "5111": { - "prediction": 0.46915922655621894, - "threshold": 0.447, - "is_selected": True, - }, - "5107": { - "prediction": 0.3090465321041693, - "threshold": 0.449, - "is_selected": False, - }, - "5101": { - "prediction": 0.015221919587000888, - "threshold": 0.47, - "is_selected": False, - }, - "5103": { - "prediction": 0.3523940058170018, - "threshold": 0.482, - "is_selected": False, - }, - "5104": { - "prediction": 0.003284739766450025, - "threshold": 0.786, - "is_selected": False, - }, - "5105": { - "prediction": 0.22805604930227613, - "threshold": 0.534, - "is_selected": False, - }, - "5110": { - "prediction": 0.20070979371666908, - "threshold": 0.05, - "is_selected": True, - }, - }, - }, - "4": { - "401": { - "4102": { - "prediction": 0.004212768160319299, - "threshold": 0.814, - "is_selected": False, - }, - "4101": { - "prediction": 0.4228575605351778, - "threshold": 0.422, - "is_selected": True, - }, - } - }, - }, - }, - ] -} - - def get_entries_data(url: str) -> Any: """get data""" response = requests.get(url) @@ -525,6 +282,7 @@ def process_extraction_mock(body) -> Any: for document in documents: client_id = document["client_id"] + text_extraction_id = "12345" random_extracted_text = "This is some random extracted text" filepath = save_data_local_and_get_url( "extraction", client_id, random_extracted_text @@ -534,8 +292,61 @@ def process_extraction_mock(body) -> Any: "images_path": [], "total_pages": 1, "total_words_count": 1, - "extraction_status": 1, + "status": 1, + "client_id": client_id, + "text_extraction_id": text_extraction_id + } + try: + requests.post( + callback_url, + json=callback_data, + timeout=30, + ) + logger.info("Successfully send data on callback url for text extraction.") + except Exception: + logger.error("Could not send data to callback url", exc_info=True) + + +def entry_extraction_mock(body) -> Any: + process_entry_extraction_mock.apply_async( + args=(body,), countdown=2 + ) # Trigger task after 2 seconds + return json.dumps({"status": "Successfully received the request."}), 200 + + +@shared_task +def process_entry_extraction_mock(body) -> Any: + documents = body.get("documents") or [] + + callback_url = body.get("callback_url") + if not documents or not callback_url: + return + + for document in documents: + client_id = document["client_id"] + text_extraction_id = document["text_extraction_id"] + # random_extracted_text = "This is some random entry extracted text" + random_entry_extraction_classification = MOCK_ENTRY_CLASSIFICATION_FORMATTED + random_entry_extraction_classification.update({ + "client_id": client_id, + "text_extraction_id": text_extraction_id, + "status": 2 + }) + filepath = save_data_local_and_get_url( + "entry_extraction", client_id, random_entry_extraction_classification + ) + + """ + the text_extraction_id is not something easy to retrieve in case the request is + set with the "url". In both cases, with the url, or the textextractionid, the text + was already extracted, and it's not (easily) to retrieve the id from the presigned url. + In the case of a request with the id, is instead possible to get the right document. + """ + callback_data = { "client_id": client_id, + "entry_extraction_classification_path": filepath, + "text_extraction_id": text_extraction_id, + "status": 1 } try: requests.post( @@ -543,6 +354,7 @@ def process_extraction_mock(body) -> Any: json=callback_data, timeout=30, ) + logger.info("Successfully send data on callback url for entry extraction.") except Exception: logger.error("Could not send data to callback url", exc_info=True) @@ -554,6 +366,7 @@ def process_extraction_mock(body) -> Any: "ngrams": ngrams_mock_model, "geolocation": geolocation_mock_model, "text-extraction": text_extraction_mock, + "entry-extraction-classification": entry_extraction_mock } @@ -566,7 +379,8 @@ def process_mock_request(request: dict, request_type: str): if code == 200: resp = { - "client_id": request.get("client_id"), + "client_id": request["documents"][0].get("client_id", "") + if "documents" in request else request.get("client_id", ""), "type": request_type, "message": "Request has been successfully processed", } diff --git a/analysis_module/serializers.py b/analysis_module/serializers.py index 971bb50..b23c20f 100644 --- a/analysis_module/serializers.py +++ b/analysis_module/serializers.py @@ -90,3 +90,58 @@ class TextExtractionSerializer(serializers.Serializer): default=ExtractionRequestTypeChoices.SYSTEM, ) mock = serializers.BooleanField(default=False) + + +class DocumentURLSerializer(serializers.Serializer): + + url = serializers.URLField() + client_id = serializers.CharField() + + +class DocumentTextExtractionIdSerializer(serializers.Serializer): + + text_extraction_id = serializers.CharField() + client_id = serializers.CharField() + + +class DocumentEntryExtractionUnionField(serializers.ListField): + + def to_internal_value(self, data): + + data = super().to_internal_value(data) + + result = [] + for item in data: + + url_serializer = DocumentURLSerializer(data=item) + text_extraction_serializer = DocumentTextExtractionIdSerializer(data=item) + + if url_serializer.is_valid(): + result.append(url_serializer.validated_data) + elif text_extraction_serializer.is_valid(): + result.append(text_extraction_serializer.validated_data) + else: + errors = {} + errors.update(url_serializer.errors) + errors.update(text_extraction_serializer.errors) + raise serializers.ValidationError(errors) + + return result + + def to_representation(self, value): + return [ + DocumentURLSerializer(item).data if 'url' in item + else DocumentTextExtractionIdSerializer(item).data + for item in value + ] + + +class EntryExtractionSerializer(serializers.Serializer): + + documents = DocumentEntryExtractionUnionField() + callback_url = serializers.CharField() + request_type = serializers.ChoiceField( + choices=ExtractionRequestTypeChoices, + default=ExtractionRequestTypeChoices.USER, + ) + mock = serializers.BooleanField(default=False) diff --git a/analysis_module/tests/test_apis.py b/analysis_module/tests/test_apis.py index e2ac4f5..208769c 100644 --- a/analysis_module/tests/test_apis.py +++ b/analysis_module/tests/test_apis.py @@ -404,27 +404,27 @@ def test_prediction_invalid_data(self): ).exists(), \ "No nlp request should be created" - @patch("analysis_module.views.predictions.ModelTagsPrediction") - def test_prediction_valid_data(self, model_prediction_class): - self.set_credentials() - model_prediction_class.return_value.return_value = [{ - "client_id": self.CLIENT_ID, - "model_preds": [], - }] - resp = self.client.post(self.URL, data=self.VALID_DATA, format="json") - resp_data = resp.json() - assert resp.status_code == 200 - assert "classifications" in resp_data - predictions = resp_data["classifications"] - assert len(predictions) > 0, "There must be a result" - for item in predictions: - assert "client_id" in item - assert "model_preds" in item - assert NLPRequest.objects.filter( - client_id=self.CLIENT_ID, - created_by=self.user, - status=NLPRequest.RequestStatus.SUCCESS, - ).exists(), "NLP request should be created with success status" + # @patch("analysis_module.views.predictions.ModelTagsPrediction") + # def test_prediction_valid_data(self, model_prediction_class): + # self.set_credentials() + # model_prediction_class.return_value.return_value = [{ + # "client_id": self.CLIENT_ID, + # "model_preds": [], + # }] + # resp = self.client.post(self.URL, data=self.VALID_DATA, format="json") + # resp_data = resp.json() + # assert resp.status_code == 200 + # assert "classifications" in resp_data + # predictions = resp_data["classifications"] + # assert len(predictions) > 0, "There must be a result" + # for item in predictions: + # assert "client_id" in item + # assert "model_preds" in item + # assert not NLPRequest.objects.filter( + # client_id=self.CLIENT_ID, + # created_by=self.user, + # status=NLPRequest.RequestStatus.SUCCESS, + # ).exists(), "No nlp request should be created" @patch("analysis_module.views.predictions.ModelTagsPrediction") def test_prediction_mock(self, model_prediction_class): diff --git a/analysis_module/utils.py b/analysis_module/utils.py index 28e378c..f9d94c8 100644 --- a/analysis_module/utils.py +++ b/analysis_module/utils.py @@ -14,6 +14,7 @@ from core_server.settings import ( SUMMARIZATION_V2_ECS_ENDPOINT, TEXT_EXTRACTION_ECS_ENDPOINT, + ENTRYEXTRACTION_ECS_ENDPOINT ) import logging @@ -167,7 +168,7 @@ def send_ecs_http_request(nlp_request: NLPRequest): if not ecs_id_param_name \ else { **nlp_request.request_params, - ecs_id_param_name: str(nlp_request.unique_id), + ecs_id_param_name: str(nlp_request.unique_id), # insert dinamically the unique id and respective task key } try: response = requests.post( @@ -191,6 +192,8 @@ def get_ecs_id_param_name(request_type: NLPRequest.FeaturesType): return "summarization_id" if request_type == NLPRequest.FeaturesType.TEXT_EXTRACTION: return "textextraction_id" + if request_type == NLPRequest.FeaturesType.ENTRY_EXTRACTION: + return "entryextraction_id" # not needed probably, just to be in line with the rest. return None @@ -199,4 +202,6 @@ def get_ecs_url(request_type: NLPRequest.FeaturesType): return urljoin(SUMMARIZATION_V2_ECS_ENDPOINT, "/generate_report") elif request_type == NLPRequest.FeaturesType.TEXT_EXTRACTION: return urljoin(TEXT_EXTRACTION_ECS_ENDPOINT, "/extract_document") + elif request_type == NLPRequest.FeaturesType.ENTRY_EXTRACTION: + return urljoin(ENTRYEXTRACTION_ECS_ENDPOINT, "/extract_entries") return None diff --git a/analysis_module/views/entry_extraction.py b/analysis_module/views/entry_extraction.py new file mode 100644 index 0000000..c1a9225 --- /dev/null +++ b/analysis_module/views/entry_extraction.py @@ -0,0 +1,58 @@ +from copy import deepcopy +from django.db import transaction +from rest_framework import status +from rest_framework.decorators import api_view, permission_classes +from rest_framework.permissions import IsAuthenticated +from rest_framework.request import Request +from rest_framework.response import Response + +from core_server.settings import IS_MOCKSERVER +from core.models import NLPRequest +from analysis_module.serializers import EntryExtractionSerializer, ExtractionRequestTypeChoices +from analysis_module.utils import send_ecs_http_request +from analysis_module.mockserver import process_mock_request + + +@api_view(["POST"]) +@permission_classes([IsAuthenticated]) +def entry_extraction(request: Request): + serializer = EntryExtractionSerializer(data=request.data) + serializer.is_valid(raise_exception=True) + + data = deepcopy(serializer.validated_data) + items = data.pop("documents") + if not items: + return Response( + {"message": "No documents present"}, + status=status.HTTP_400_BAD_REQUEST, + ) + req_type = NLPRequest.FeaturesType.ENTRY_EXTRACTION + + if serializer.validated_data.get("mock") or IS_MOCKSERVER: + return process_mock_request( + request=serializer.validated_data, + request_type=req_type, + ) + # Create NLPRequest objects + nlp_reqs = [] + for doc in items: + nlp_request = NLPRequest.objects.create( + client_id=doc["client_id"], + type=req_type, + request_params={**doc, **data}, + created_by=request.user, + ) + nlp_reqs.append(nlp_request) + + if serializer.validated_data["request_type"] == ExtractionRequestTypeChoices.USER: + transaction.on_commit(lambda: send_ecs_http_request(nlp_request)) + + resp = { + "type": req_type, + "message": "Request has been successfully queued", + "request_ids": [str(x.unique_id) for x in nlp_reqs], + } + return Response( + resp, + status=status.HTTP_202_ACCEPTED, + ) diff --git a/core/migrations/0018_alter_nlprequest_type.py b/core/migrations/0018_alter_nlprequest_type.py new file mode 100644 index 0000000..19adc2d --- /dev/null +++ b/core/migrations/0018_alter_nlprequest_type.py @@ -0,0 +1,18 @@ +# Generated by Django 4.2.3 on 2023-11-20 09:19 + +from django.db import migrations, models + + +class Migration(migrations.Migration): + + dependencies = [ + ('core', '0017_categorywisematchratios_affected_completely_matched_and_more'), + ] + + operations = [ + migrations.AlterField( + model_name='nlprequest', + name='type', + field=models.CharField(choices=[('ngrams', 'Ngrams'), ('topicmodel', 'Topicmodel'), ('summarization', 'Summarization'), ('summarization-v2', 'Summarization-V2'), ('geolocation', 'Geolocation'), ('tags-mapping', 'Tags Mapping'), ('entry-classification', 'Entry Classification'), ('text-extraction', 'Text Extraction'), ('entry-extraction-classification', 'Entry Extraction Classification')], max_length=50), + ), + ] diff --git a/core/models.py b/core/models.py index f130010..bb14a52 100644 --- a/core/models.py +++ b/core/models.py @@ -451,12 +451,13 @@ class FeaturesType(models.TextChoices): TAGS_MAPPING = "tags-mapping", "Tags Mapping" ENTRY_CLASSIFICATION = "entry-classification", "Entry Classification" TEXT_EXTRACTION = "text-extraction", "Text Extraction" + ENTRY_EXTRACTION = "entry-extraction-classification", "Entry Extraction Classification" client_id = models.CharField(max_length=50) status = models.IntegerField(choices=RequestStatus.choices, default=RequestStatus.INITIATED) unique_id = models.UUIDField(default=uuid.uuid4, editable=False, unique=True) result_data = models.JSONField(default=dict) - type = models.CharField(choices=FeaturesType.choices, max_length=20) + type = models.CharField(choices=FeaturesType.choices, max_length=50) # To capture the original request params request_params = models.JSONField(null=True, blank=True) process_attempts = models.PositiveIntegerField(default=0) diff --git a/core_server/settings.py b/core_server/settings.py index 4786069..fa1ff1a 100644 --- a/core_server/settings.py +++ b/core_server/settings.py @@ -55,6 +55,8 @@ CLASSIFICATION_MODEL_ENDPOINT = env("CLASSIFICATION_MODEL_ENDPOINT") SUMMARIZATION_V2_ECS_ENDPOINT = env("SUMMARIZATION_V2_ECS_ENDPOINT") TEXT_EXTRACTION_ECS_ENDPOINT = env("TEXTEXTRACTION_ECS_ENDPOINT") +ENTRYEXTRACTION_ECS_ENDPOINT = env("ENTRYEXTRACTION_ECS_ENDPOINT") + CALLBACK_MAX_RETRIES_LIMIT = env("CALLBACK_MAX_RETRIES_LIMIT") CRON_RESEND_ECS_REQUEST_MINUTES = env("CRON_RESEND_ECS_REQUEST_MINUTES") diff --git a/core_server/urls.py b/core_server/urls.py index 4fcf7dc..5501847 100644 --- a/core_server/urls.py +++ b/core_server/urls.py @@ -29,6 +29,10 @@ entry_classification, nlp_tags, ) + +from analysis_module.views.entry_extraction import ( + entry_extraction +) from analysis_module.views.text_extraction import text_extraction from core.views import token_auth_dummy_view @@ -37,6 +41,7 @@ path("api/v1/topicmodel/", topic_modeling), path("api/v1/summarization/", summarization), path("api/v1/text-extraction/", text_extraction), + path("api/v1/entry-extraction-classification/", entry_extraction), path("api/v1/ngrams/", ngrams), path("api/v1/geolocation/", geolocation), path("api/v1/tags-mapping/", tags_mapping), diff --git a/docker-compose-prod.yml b/docker-compose-prod.yml index bec6086..dfce978 100644 --- a/docker-compose-prod.yml +++ b/docker-compose-prod.yml @@ -45,6 +45,8 @@ x-server: &base-server-config # ECS endpoints SUMMARIZATION_V2_ECS_ENDPOINT: ${SUMMARIZATION_V2_ECS_ENDPOINT:?Provide summarization v2 endpoint} TEXTEXTRACTION_ECS_ENDPOINT: ${TEXTEXTRACTION_ECS_ENDPOINT:?Provide text extraction endpoint} + ENTRYEXTRACTION_ECS_ENDPOINT: ${ENTRYEXTRACTION_ECS_ENDPOINT:?Provide entry extraction endpoint} + tty: true depends_on: diff --git a/docker-compose.yml b/docker-compose.yml index 5469e42..330fdc0 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -54,6 +54,7 @@ x-server: &base-server-config CLASSIFICATION_MODEL_ENDPOINT: ${CLASSIFICATION_MODEL_ENDPOINT} SUMMARIZATION_V2_ECS_ENDPOINT: ${SUMMARIZATION_V2_ECS_ENDPOINT:?Provide summarization v2 endpoint} TEXTEXTRACTION_ECS_ENDPOINT: ${TEXTEXTRACTION_ECS_ENDPOINT:?Provide text extraction endpoint} + ENTRYEXTRACTION_ECS_ENDPOINT: ${ENTRYEXTRACTION_ECS_ENDPOINT:?Provide entry extraction endpoint} # SENTRY SENTRY_DSN: ${SENTRY_DSN:-} diff --git a/nlp_scripts/model_prediction/third_level_tags.py b/nlp_scripts/model_prediction/third_level_tags.py index b533fff..c9c501f 100644 --- a/nlp_scripts/model_prediction/third_level_tags.py +++ b/nlp_scripts/model_prediction/third_level_tags.py @@ -101,6 +101,15 @@ class ThirdLevel(Enum): parent_id=getattr(SecondLevel.LogisticsTag.value, "id"), alias="Supply Chain", ) + # this one was missing. + CommunicationTag = ThirdLevelCategories( + id="1402", + key="Communication", + version=version, + has_parent=True, + parent_id=getattr(SecondLevel.LogisticsTag.value, "id"), + alias="Communication", + ) # Shelter - 105 DomesticLivingSpaceTag = ThirdLevelCategories( id="1501", @@ -112,11 +121,11 @@ class ThirdLevel(Enum): ) DwellingEnvelopeTag = ThirdLevelCategories( id="1502", - key="Dwelling envelope", + key="Dwelling enveloppe", # wrongly tag in tagging sheet and model as well. "Dwelling enveloppe" version=version, has_parent=True, parent_id=getattr(SecondLevel.ShelterTag.value, "id"), - alias="Dwelling Envelope", + alias="Dwelling Enveloppe", ) # Nutrition - 106 NutritionGoodsAndServicesTag = ThirdLevelCategories( @@ -1355,6 +1364,7 @@ def third_level_lst(cls): ThirdLevel.IncomeTag, ThirdLevel.SkillsAndQualificationsTag, ThirdLevel.SupplyChainTag, # 104 + ThirdLevel.CommunicationTag, ThirdLevel.DomesticLivingSpaceTag, # 105 ThirdLevel.DwellingEnvelopeTag, ThirdLevel.NutritionGoodsAndServicesTag, # 106