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/analysis_module/mock_templates.py b/analysis_module/mock_templates.py new file mode 100644 index 0000000..1d22d5f --- /dev/null +++ b/analysis_module/mock_templates.py @@ -0,0 +1,17148 @@ +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": { + "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, + }, + } + }, + }, + }, + ] +} + + +""" +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: Dict = { + "metadata": { + "format": "PDF 1.7", + "title": "", + "author": "ACAPS", + "subject": "", + "keywords": [], + "creator": "Microsoft Word for Office 365", + "producer": "Microsoft Word for Office 365", + "creationDate": "D:20191021144502+02'00'", + "modDate": "D:20191021145218+02'00'", + "trapped": "", + "encryption": "null", + "total_pages": 1, + "total_words_count": 1, + "format": "pdf" + }, + "blocks": [ + { + "type": "text", + "page": 0, + "x0": 0.0, + "y0": 0.0, + "x1": 543.7218933105469, + "y1": 109.99737167358398, + "rect": [ + 0.0, + 0.0, + 543.7218933105469, + 109.99737167358398 + ], + "text": "NIGERIA Floods in Borno, Delta, Kebbi, and Kogi states", + "textOrder": 0, + "textCrop": [ + 28.079999923706055, + 36.95722961425781, + 511.9797668457031, + 106.2417221069336 + ], + "relevant": False + }, + { + "type": "text", + "page": 0, + "x0": 0.0, + "y0": 109.99737167358398, + "x1": 543.7218933105469, + "y1": 261.5125198364258, + "rect": [ + 0.0, + 109.99737167358398, + 543.7218933105469, + 261.5125198364258 + ], + "text": "Seasonal rainfall and subsequent high-water levels in Niger and Benue rivers have been causing flooding across Nigeria since June 2019. 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": 1, + "textCrop": [ + 28.079999923706055, + 113.75302124023438, + 405.02587890625, + 192.47804260253906 + ], + "relevant": True, + "classification": { + "2": { + "204": { + "2402": { + "prediction": 0.5427906118287631, + "threshold": 0.489, + "is_selected": True + }, + "2401": { + "prediction": 0.2051073564883168, + "threshold": 0.461, + "is_selected": False + } + }, + "202": { + "2206": { + "prediction": 1.0259111101428668, + "threshold": 0.576, + "is_selected": True + }, + "2205": { + "prediction": 0.988062537674393, + "threshold": 0.448, + "is_selected": True + }, + "2203": { + "prediction": 0.040246154810112664, + "threshold": 0.492, + "is_selected": False + }, + "2201": { + "prediction": 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"is_selected": False + }, + "1702": { + "prediction": 0.014111534299620664, + "threshold": 0.415, + "is_selected": False + }, + "1703": { + "prediction": 0.02440607023575112, + "threshold": 0.479, + "is_selected": False + }, + "1704": { + "prediction": 0.019819518412955463, + "threshold": 0.458, + "is_selected": False + }, + "1705": { + "prediction": 0.006560413166880608, + "threshold": 0.425, + "is_selected": False + }, + "1706": { + "prediction": 0.0023820810357405658, + "threshold": 0.345, + "is_selected": False + }, + "1707": { + "prediction": 0.0052659290450063736, + "threshold": 0.574, + "is_selected": False + }, + "1708": { + "prediction": 0.013677510657939769, + "threshold": 0.538, + "is_selected": False + }, + "1709": { + "prediction": 0.005801920565069374, + "threshold": 0.457, + "is_selected": False + }, + "1710": { + "prediction": 0.039586182104645616, + "threshold": 0.386, + "is_selected": False + }, + "1711": { + "prediction": 0.01739774861867141, + "threshold": 0.507, + "is_selected": False + } + }, + "105": { + "1501": { + "prediction": 0.11859711134031917, + "threshold": 0.445, + "is_selected": False + } + }, + "108": { + "1801": { + "prediction": 0.01797955483198166, + "threshold": 0.515, + "is_selected": False + }, + "1802": { + "prediction": 0.06537258322608426, + "threshold": 0.542, + "is_selected": False + }, + "1805": { + "prediction": 0.030150983745799116, + "threshold": 0.046, + "is_selected": False + }, + "1804": { + "prediction": 0.00558186990104073, + "threshold": 0.604, + "is_selected": False + }, + "1803": { + "prediction": 0.12183886706125475, + "threshold": 0.593, + "is_selected": False + } + } + } + } + }, + { + "type": "text", + "page": 0, + "x0": 289.7720031738281, + "y0": 261.5125198364258, + "x1": 543.7218933105469, + "y1": 346.7069854736328, + "rect": [ + 289.7720031738281, + 261.5125198364258, + 543.7218933105469, + 346.7069854736328 + ], + "text": "Source: IFRC EPoA 07/10/2019", + "textOrder": 2, + "textCrop": [ + 416.2099914550781, + 330.5469970703125, + 529.7337646484375, + 341.5939636230469 + ], + "relevant": False + }, + { + "type": "text", + "page": 0, + "x0": 543.7218933105469, + "y0": 0.0, + "x1": 842.0399780273438, + "y1": 110.35965347290039, + "rect": [ + 543.7218933105469, + 0.0, + 842.0399780273438, + 110.35965347290039 + ], + "text": "Briefing note \u2013 17 October 2019", + "textOrder": 3, + "textCrop": [ + 649.9000244140625, + 86.60721588134766, + 815.0582275390625, + 101.80929565429688 + ], + "relevant": False + }, + { + "type": "text", + "page": 0, + "x0": 543.7218933105469, + "y0": 110.35965347290039, + "x1": 842.0399780273438, + "y1": 143.24000549316406, + "rect": [ + 543.7218933105469, + 110.35965347290039, + 842.0399780273438, + 143.24000549316406 + ], + "text": "Affected population (per 1000 people)", + "textOrder": 4, + "textCrop": [ + 707.3800048828125, + 118.9100112915039, + 791.8903198242188, + 143.18301391601562 + ], + "relevant": False + }, + { + "type": "text", + "page": 0, + "x0": 543.7218933105469, + "y0": 143.24000549316406, + "x1": 746.0530090332031, + "y1": 229.49049377441406, + "rect": [ + 543.7218933105469, + 143.24000549316406, + 746.0530090332031, + 229.49049377441406 + ], + "text": "Borno Kebbi", + "textOrder": 5, + "textCrop": [ + 707.3800048828125, + 147.78501892089844, + 731.446044921875, + 224.14100646972656 + ], + "relevant": False + }, + { + "type": "text", + "page": 0, + "x0": 746.0530090332031, + "y0": 143.24000549316406, + "x1": 842.0399780273438, + "y1": 229.49049377441406, + "rect": [ + 746.0530090332031, + 143.24000549316406, + 842.0399780273438, + 229.49049377441406 + ], + "text": "6,69 per 1000 people 8,01 per 1000 3,63 per 1000 people 1,47 per 1000", + "textOrder": 6, + "textCrop": [ + 760.6599731445312, + 143.2969970703125, + 810.1304321289062, + 229.01397705078125 + ], + "relevant": False + }, + { + "type": "text", + "page": 0, + "x0": 543.7218933105469, + "y0": 229.49049377441406, + "x1": 842.0399780273438, + "y1": 296.4169921875, + "rect": [ + 543.7218933105469, + 229.49049377441406, + 842.0399780273438, + 296.4169921875 + ], + "text": "Source: IFRC EPoA 07/10/2019", + "textOrder": 7, + "textCrop": [ + 702.0999755859375, + 229.96701049804688, + 815.603759765625, + 241.01397705078125 + ], + "relevant": False + }, + { + "type": "text", + "page": 0, + "x0": 543.7218933105469, + "y0": 559.116455078125, + "x1": 842.0399780273438, + "y1": 595.3200073242188, + "rect": [ + 543.7218933105469, + 559.116455078125, + 842.0399780273438, + 595.3200073242188 + ], + "text": "Any questions? Please contact us at info@acaps.org", + "textOrder": 8, + "textCrop": [ + 626.739990234375, + 570.572998046875, + 814.1712646484375, + 581.6199340820312 + ], + "relevant": False + }, + { + "type": "image", + "page": 0, + "x0": 701.97998046875, + "y0": 208.37002563476562, + "x1": 737.5, + "y1": 229.61001586914062, + "rect": [ + 701.97998046875, + 208.37002563476562, + 737.5, + 229.61001586914062 + ], + "imageLink": "pic-4f271128-4087-4d80-8d4b-cf486be2149c.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "Kogi" + }, + { + "type": "table", + "page": 0, + "x0": 701.97998046875, + "y0": 165.6199951171875, + "x1": 815.4039916992188, + "y1": 187.22000122070312, + "rect": [ + 701.97998046875, + 165.6199951171875, + 815.4039916992188, + 187.22000122070312 + ], + "tableImageLink": "table-9a24952b-b021-4537-81b8-03c073264fd8.png", + "tableCaption": "undefined", + "tableMeta": "undefined", + "tableText": "Delta\npeople" + }, + { + "type": "table", + "page": 0, + "x0": 28.079999923706055, + "y0": 344.3500061035156, + "x1": 814.9200439453125, + "y1": 559.89599609375, + "rect": [ + 28.079999923706055, + 344.3500061035156, + 814.9200439453125, + 559.89599609375 + ], + "tableImageLink": "table-f08ca138-bb5f-4568-9325-e057114dd11c.png", + "tableCaption": "undefined", + "tableMeta": "undefined", + "tableText": "Anticipated scope and scale Key priorities (country level) Humanitarian constraints\nThe flooding is estimated to have affected over 210,000 130,000 Humanitarian access to Borno state is severely\npeople, with about 130,000 of them being displaced limited due to security concerns and poor road\npeople displaced\n(IFRC EPoA 07/10/2019). Rainfall is predicted to continue in infrastructure. Monitoring the security situation in\ncentral Nigeria in the week of 17-23 October (NOAA). Kogi and Delta states is recommended due to the\nWASH\nFlooding has destroyed or flooded IDP camps across ongoing Middle Belt conflict. Destroyed bridges and\ndestruction of WASH facilities\nnorth-eastern states, especially in Borno. People in camps in flooded roads can further constrain access but data\nBorno face increased health risks and lack access to on infrastructure damage across states is lacking.\nWASH facilities.\nShelter\nflooding of camps and houses\nLimitations: Data is often not disaggregated by geographical area\nand information on sectoral needs is uneven, focusing largely on camp\nsites in north-eastern states and only sporadically on humanitarian\nHealth\nneeds in other states. This makes it difficult to ascertain the full impact\nrisk of disease outbreaks\nof the floods and the severity of humanitarian need in each state." + }, + { + "type": "image", + "page": 0, + "x0": 737.5, + "y0": 208.37002563476562, + "x1": 815.4039916992188, + "y1": 229.61001586914062, + "rect": [ + 737.5, + 208.37002563476562, + 815.4039916992188, + 229.61001586914062 + ], + "imageLink": "pic-b233080c-6209-43b8-9972-3ebd80aff1e8.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "people" + }, + { + "type": "image", + "page": 0, + "x0": 415.5, + "y0": 122.25, + "x1": 694.4599609375, + "y1": 328.75, + "rect": [ + 415.5, + 122.25, + 694.4599609375, + 328.75 + ], + "imageLink": "pic-fc015e36-d3a6-48a3-a108-0d01b2b50008.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 285.75, + "y0": 359.489990234375, + "x1": 370.75, + "y1": 444.489990234375, + "rect": [ + 285.75, + 359.489990234375, + 370.75, + 444.489990234375 + ], + "imageLink": "pic-e84639fe-7b7b-4cd4-a897-e405fdcfabf3.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 29.0, + "y0": 207.25001525878906, + "x1": 285.6099853515625, + "y1": 253.70001220703125, + "rect": [ + 29.0, + 207.25001525878906, + 285.6099853515625, + 253.70001220703125 + ], + "imageLink": "pic-4630f6d9-73f3-4a98-97e0-b8fe16041e92.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 630.2999877929688, + "y0": 57.5, + "x1": 701.3779907226562, + "y1": 79.25, + "rect": [ + 630.2999877929688, + 57.5, + 701.3779907226562, + 79.25 + ], + "imageLink": "pic-784fc929-bfe3-42d6-93a7-7d70663d3088.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 29.299999237060547, + "y0": 268.4499816894531, + "x1": 285.79998779296875, + "y1": 314.8999938964844, + "rect": [ + 29.299999237060547, + 268.4499816894531, + 285.79998779296875, + 314.8999938964844 + ], + "imageLink": "pic-910a9c03-5fc6-4ead-b85e-8d4f6a708f0a.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 530.5499877929688, + "y0": 359.2900085449219, + "x1": 615.5499877929688, + "y1": 444.2900085449219, + "rect": [ + 530.5499877929688, + 359.2900085449219, + 615.5499877929688, + 444.2900085449219 + ], + "imageLink": "pic-0ffe3b91-a4b5-490d-946b-79b4c738d533.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 285.75, + "y0": 404.489990234375, + "x1": 370.75, + "y1": 489.489990234375, + "rect": [ + 285.75, + 404.489990234375, + 370.75, + 489.489990234375 + ], + "imageLink": "pic-bc2715af-5c1a-44fc-be26-ecc36e9b80ef.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 285.75, + "y0": 450.2440185546875, + "x1": 370.75, + "y1": 535.2440185546875, + "rect": [ + 285.75, + 450.2440185546875, + 370.75, + 535.2440185546875 + ], + "imageLink": "pic-1d67cc7e-a6e2-42bb-bc96-846075a6e8fe.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 286.04998779296875, + "y0": 488.9940185546875, + "x1": 371.04998779296875, + "y1": 573.9940185546875, + "rect": [ + 286.04998779296875, + 488.9940185546875, + 371.04998779296875, + 573.9940185546875 + ], + "imageLink": "pic-35e9c2cd-94ab-45eb-93a7-add960c73dc4.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "image", + "page": 0, + "x0": 710.9000244140625, + "y0": 50.900001525878906, + "x1": 820.7000122070312, + "y1": 85.5, + "rect": [ + 710.9000244140625, + 50.900001525878906, + 820.7000122070312, + 85.5 + ], + "imageLink": "pic-f8f4c812-a5bc-4c49-9516-8fc0ba387ac9.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + }, + { + "type": "text", + "page": 1, + "x0": 0.0, + "y0": 0.0, + "x1": 421.0233154296875, + "y1": 66.79900741577148, + "rect": [ + 0.0, + 0.0, + 421.0233154296875, + 66.79900741577148 + ], + "text": "Crisis impact", + "textOrder": 0, + "textCrop": [ + 28.079999923706055, + 43.47001647949219, + 122.34001159667969, + 64.1250228881836 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 0.0, + "y0": 66.79900741577148, + "x1": 421.0233154296875, + "y1": 160.49553680419922, + "rect": [ + 0.0, + 66.79900741577148, + 421.0233154296875, + 160.49553680419922 + ], + "text": "As a result of heavy rainfalls and high water levels in the Niger and Benue rivers, the two main rivers of Nigeria, a series of flooding has submerged areas across the country in water since June 2019. Following a peak in water levels of rivers Niger and Benue in late September, severe flooding aggravated the situation (Floodlist 07/10/2019). The National Emergency Management Agency (NEMA) has not declared a state of emergency but the Nigeria Hydrological Services Agency (NIHSA) had issued a flood red alert earlier in September (Sahara Reporters 17/09/2019).", + "textOrder": 1, + "textCrop": [ + 28.079999923706055, + 69.47299194335938, + 410.9566345214844, + 158.99806213378906 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 0.0, + "y0": 160.49553680419922, + "x1": 421.0233154296875, + "y1": 303.68553161621094, + "rect": [ + 0.0, + 160.49553680419922, + 421.0233154296875, + 303.68553161621094 + ], + "text": "According to the latest statistics issued by the International Federation of Red Cross (IFRC) on 7 October, severe floods occurred in 32 of the 36 states and Federal Capital Territory and it is estimated that in total over 210,000 people are affected with about 130,000 of them being displaced and at least 12 reported deaths in Cross River and Niger states (IFRC EPoA 07/10/2019). No updated estimations were available but since rainfall has been ongoing and is predicted to continue in central Nigeria (NOAA), the number of people affected by the floods is likely to have increased. Moreover, the total number of fatalities is likely to be higher. As of 30 August, at least 15 people had also been killed in Adamawa state (OCHA SitRep 2 30/08/2019) and additional deaths in flood-related accidents in other states keep being reported, for instance in Lagos and Delta state (Guardian Nigeria 12/10/2019 and 23/09/2019).", + "textOrder": 2, + "textCrop": [ + 28.079999923706055, + 161.99301147460938, + 410.9566345214844, + 302.18804931640625 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 0.0, + "y0": 303.68553161621094, + "x1": 421.0233154296875, + "y1": 388.93043518066406, + "rect": [ + 0.0, + 303.68553161621094, + 421.0233154296875, + 388.93043518066406 + ], + "text": "According to assessments of the Nigerian Red Cross Society (NRCS), the numbers of affected people in Borno and Delta states are higher than in Kebbi and Kogi. For Delta, estimations range between 40,000 and 60,000 affected people, for Borno between 20,000 and 40,000 people (IFRC EPoA 07/10/2019). Other severely affected states in terms of the total number of affected people include Anambra, Bauchi, Jigawa, and Lagos.", + "textOrder": 3, + "textCrop": [ + 28.079999923706055, + 305.1830139160156, + 410.9151306152344, + 382.1280517578125 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 0.0, + "y0": 388.93043518066406, + "x1": 421.0233154296875, + "y1": 443.29042053222656, + "rect": [ + 0.0, + 388.93043518066406, + 421.0233154296875, + 443.29042053222656 + ], + "text": "AFFECTED POPULATION RATES (PER 1000 PEOPLE) BORNO", + "textOrder": 4, + "textCrop": [ + 130.94000244140625, + 395.7328186035156, + 289.2440185546875, + 440.3280334472656 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 0.0, + "y0": 490.5384216308594, + "x1": 421.0233154296875, + "y1": 595.3200073242188, + "rect": [ + 0.0, + 490.5384216308594, + 421.0233154296875, + 595.3200073242188 + ], + "text": "Source: IFRC EPoA 07/10/2019", + "textOrder": 5, + "textCrop": [ + 128.4199981689453, + 493.5588073730469, + 274.53009033203125, + 528.3399658203125 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 421.0233154296875, + "y0": 0.0, + "x1": 842.0399780273438, + "y1": 38.868499755859375, + "rect": [ + 421.0233154296875, + 0.0, + 842.0399780273438, + 38.868499755859375 + ], + "text": "ACAPS Briefing Note: Floods in Nigeria", + "textOrder": 6, + "textCrop": [ + 674.02001953125, + 22.557010650634766, + 814.0595703125, + 33.603973388671875 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 421.0233154296875, + "y0": 38.868499755859375, + "x1": 842.0399780273438, + "y1": 118.3155288696289, + "rect": [ + 421.0233154296875, + 38.868499755859375, + 842.0399780273438, + 118.3155288696289 + ], + "text": "As the floods affect states across the different geo-political zones in the country as well as different population groups, especially IDPs and host communities, sectoral needs and gaps vary. Information on specific needs across states is uneven and focuses particularly on flood-related needs in context of the humanitarian crisis in north-east Borno, Adamawa, and Yobe (BAY) states (ex. CCCM MST report 3 04/10/2019).", + "textOrder": 7, + "textCrop": [ + 431.0899963378906, + 44.133026123046875, + 814.0199584960938, + 108.47804260253906 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 421.0233154296875, + "y0": 118.3155288696289, + "x1": 842.0399780273438, + "y1": 206.48552703857422, + "rect": [ + 421.0233154296875, + 118.3155288696289, + 842.0399780273438, + 206.48552703857422 + ], + "text": "Shelter/NFI: Displacement and shelter damage are widespread across flood-affected states. Heavy rains and flooding in northeast Nigeria destroyed emergency and makeshift camps, rendering IDP residents homeless or forced to stay with family members and friends in overcrowded shelters (NRC 23/08/2019). 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In many camps, people have no choice than to practice open defecation increasing the risk of the outbreak of diseases (NRC 23/08/2019). Specific WASH related needs in other states remain unclear.", + "textOrder": 10, + "textCrop": [ + 431.0899963378906, + 237.38299560546875, + 813.9166259765625, + 314.18804931640625 + ], + "relevant": False + }, + { + "type": "text", + "page": 1, + "x0": 421.0233154296875, + "y0": 315.68553161621094, + "x1": 842.0399780273438, + "y1": 383.0255126953125, + "rect": [ + 421.0233154296875, + 315.68553161621094, + 842.0399780273438, + 383.0255126953125 + ], + "text": "Food: In September, households in Delta, Kebbi, and Kogi states faced only minimal food insecurity (IPC Phase 1) and there are no reports of current food shortages in these states, though it is likely that flood-displaced people will require food assistance (FEWS NET 09/2019). 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Still, roads across Nigeria lack effective drainage, making them prone to get submerged (African Arguments 2018).", + "textOrder": 5, + "textCrop": [ + 28.079999923706055, + 316.343017578125, + 410.72430419921875, + 355.3680419921875 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 0.0, + "y0": 356.8655242919922, + "x1": 421.01690673828125, + "y1": 406.91302490234375, + "rect": [ + 0.0, + 356.8655242919922, + 421.01690673828125, + 406.91302490234375 + ], + "text": "Many flood-affected states and regions have reported infrastructure damages including roads submerged in water such as in Lagos, indicating severe temporary infrastructure impediments across the country (PM News 12/10/2019).", + "textOrder": 6, + "textCrop": [ + 28.079999923706055, + 358.3630065917969, + 410.76177978515625, + 397.248046875 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 0.0, + "y0": 406.91302490234375, + "x1": 421.01690673828125, + "y1": 435.8540344238281, + "rect": [ + 0.0, + 406.91302490234375, + 421.01690673828125, + 435.8540344238281 + ], + "text": "Vulnerable groups affected", + "textOrder": 7, + "textCrop": [ + 28.079999923706055, + 416.5780029296875, + 183.3896026611328, + 434.3850402832031 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 0.0, + "y0": 435.8540344238281, + "x1": 421.01690673828125, + "y1": 595.3200073242188, + "rect": [ + 0.0, + 435.8540344238281, + 421.01690673828125, + 595.3200073242188 + ], + "text": "IDPs: Nigeria has a large population of IDPs, mostly displaced due to the Boko Haram conflict in the northeastern states. 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This makes it difficult to assess particular vulnerabilities there.", + "textOrder": 11, + "textCrop": [ + 431.0899963378906, + 86.15301513671875, + 814.0199584960938, + 137.75804138183594 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 421.01690673828125, + "y0": 147.42302703857422, + "x1": 842.0399780273438, + "y1": 176.3640365600586, + "rect": [ + 421.01690673828125, + 147.42302703857422, + 842.0399780273438, + 176.3640365600586 + ], + "text": "Humanitarian and operational constraints", + "textOrder": 12, + "textCrop": [ + 431.0899963378906, + 157.0880126953125, + 668.072998046875, + 174.8950653076172 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 421.01690673828125, + "y0": 176.3640365600586, + "x1": 842.0399780273438, + "y1": 243.5655288696289, + "rect": [ + 421.01690673828125, + 176.3640365600586, + 842.0399780273438, + 243.5655288696289 + ], + "text": "Humanitarian access to Borno state is severely limited. 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As the flooding and ensuing damage to crops and livestock can increase the land conflicts underlying the tensions and a recent government livestock plan has been rejected (SaharaReporters 27/09/2019), the security situation in Kogi should still be monitored.", + "textOrder": 14, + "textCrop": [ + 431.0899963378906, + 245.06301879882812, + 813.8353271484375, + 309.3880310058594 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 421.01690673828125, + "y0": 310.88551330566406, + "x1": 842.0399780273438, + "y1": 345.4340362548828, + "rect": [ + 421.01690673828125, + 310.88551330566406, + 842.0399780273438, + 345.4340362548828 + ], + "text": "Destroyed bridges and flooded roads can further constrain access but data on the infrastructural damages across the different states is lacking.", + "textOrder": 15, + "textCrop": [ + 431.0899963378906, + 312.38299560546875, + 813.5042724609375, + 338.68804931640625 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 421.01690673828125, + "y0": 345.4340362548828, + "x1": 842.0399780273438, + "y1": 397.0940246582031, + "rect": [ + 421.01690673828125, + 345.4340362548828, + 842.0399780273438, + 397.0940246582031 + ], + "text": "Aggravating factors Recurring flooding", + "textOrder": 16, + "textCrop": [ + 431.0899963378906, + 352.1800231933594, + 572.7100830078125, + 395.6250305175781 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 421.01690673828125, + "y0": 397.0940246582031, + "x1": 842.0399780273438, + "y1": 439.06553649902344, + "rect": [ + 421.01690673828125, + 397.0940246582031, + 842.0399780273438, + 439.06553649902344 + ], + "text": "Nigeria experiences flooding annually during the rainy season with Niger and Benue rivers frequently overflowing not only due to local rainfall but also because the country is located downstream of several neighbouring countries (IFRC 09/2019).", + "textOrder": 17, + "textCrop": [ + 431.0899963378906, + 398.5630187988281, + 813.7708740234375, + 437.56805419921875 + ], + "relevant": False + }, + { + "type": "text", + "page": 2, + "x0": 421.01690673828125, + "y0": 439.06553649902344, + "x1": 842.0399780273438, + "y1": 569.6635437011719, + "rect": [ + 421.01690673828125, + 439.06553649902344, + 842.0399780273438, + 569.6635437011719 + ], + "text": "In recent years, severe flooding has affected the country in 2012 and in 2018. In the past year, large parts of the country were flooded and the government declared a state of emergency in Kogi, Niger, Anambra and Delta (IFRC 09/2019). The 2018 flooding is estimated to have killed up to 200 people and displaced over half a million people (WHO 09/2019). The 2018 floods also caused widespread crop destruction of at least 150,000 hectares of agricultural land (OCHA 2018). Areas that are now affected by severe flooding for a second consecutive year face increased risks of reduced crop production that can impact the availability of produce in the region (Aljazeera 23/09/2019). In addition, there is a protracted risk of negative impacts on livelihoods of farmers and local populations\u2019 capacity to rebuild and repair their homes.", + "textOrder": 18, + "textCrop": [ + 431.0899963378906, + 440.5630187988281, + 814.0977783203125, + 568.0340576171875 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 0.0, + "y0": 0.0, + "x1": 421.0743103027344, + "y1": 93.62402725219727, + "rect": [ + 0.0, + 0.0, + 421.0743103027344, + 93.62402725219727 + ], + "text": "Rainy season", + "textOrder": 0, + "textCrop": [ + 28.079999923706055, + 74.28800201416016, + 106.69536590576172, + 92.09503936767578 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 0.0, + "y0": 93.62402725219727, + "x1": 421.0743103027344, + "y1": 183.55802154541016, + "rect": [ + 0.0, + 93.62402725219727, + 421.0743103027344, + 183.55802154541016 + ], + "text": "The main rain season in the north of the country usually lasts from June until October while it can last until November in the south (FEWS NET). For 2019, the Nigeria Hydrological Services Agency (NIHSA) predicted flooding particularly from June throughout September (NNN 26/06/2019). Nevertheless, rainfall is likely to continue in the next days. For central Nigeria, heavy rainfall is expected in the upcoming week making further flooding likely (FEWS NET 11/10/2019).", + "textOrder": 1, + "textCrop": [ + 28.079999923706055, + 95.15301513671875, + 411.0115051269531, + 171.9580535888672 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 0.0, + "y0": 183.55802154541016, + "x1": 421.0743103027344, + "y1": 214.43402862548828, + "rect": [ + 0.0, + 183.55802154541016, + 421.0743103027344, + 214.43402862548828 + ], + "text": "Conflicts", + "textOrder": 2, + "textCrop": [ + 28.079999923706055, + 195.15798950195312, + 77.79168701171875, + 212.9650421142578 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 0.0, + "y0": 214.43402862548828, + "x1": 421.0743103027344, + "y1": 332.2555236816406, + "rect": [ + 0.0, + 214.43402862548828, + 421.0743103027344, + 332.2555236816406 + ], + "text": "Northeast: The three northeastern states Borno, Yobe and Adamawa continue to face a volatile security situation due to the Boko Haram conflict that has led to an ongoing humanitarian emergency. As of August 2019, over 7 million people are considered in need of humanitarian assistance in the northeast (OCHA 08/2019). The Boko Haram conflict is a key driver for displacement in Nigeria. Due to ongoing violence against civilians, around 42,000 new cases of displacement were reported in northeast states in the first half of 2019, with actual numbers likely to be even higher (IDMC 12/09/2019). The heavy rains and flooding during this rainy season have increased needs of IDPs and local farmers even further (OCHA 08/2019).", + "textOrder": 3, + "textCrop": [ + 28.079999923706055, + 215.90301513671875, + 411.0586242675781, + 330.748046875 + ], + "relevant": True, + "classification": { + "2": { + "204": { + "2402": { + "prediction": 1.1087949778160922, + "threshold": 0.489, + "is_selected": True + }, + "2401": { + "prediction": 0.30029136808730517, + "threshold": 0.461, + "is_selected": False + } + }, + "202": { + "2206": { + "prediction": 0.48724344621102017, + "threshold": 0.576, + "is_selected": False + }, + "2205": { + "prediction": 0.9285765700042248, + "threshold": 0.448, + "is_selected": True + }, + "2203": { + "prediction": 0.03594491721653357, + "threshold": 0.492, + "is_selected": False + }, + "2201": { + "prediction": 1.713403692931421, + "threshold": 0.431, + "is_selected": True + }, + "2207": { + "prediction": 0.4254854266247694, + "threshold": 0.518, + 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In central Nigerian states, the violence between herders and farmers in central Nigerian states known as the Middle Belt conflict has displaced 23,000 people (IDMC 12/09/2019). The Middle Belt conflict mainly affects Benue, Nasarawa, Plateau, and Taraba states (CrisisGroup 26/07/2018) but there have been violent incidents in Kogi in the past (Idakwoji et al. 2018, HRW). The current extent of the humanitarian needs of conflict-affected people in the Middle Belt is under-reported and displacement numbers are likely to be higher (IDMC Special Report 05/2019). Insecurity and displacement have made farming and herding more difficult for communities (CrisisGroup 26/07/2018). This situation is likely to have increased in flooded areas where livestock and crops have been destroyed and can increase the risk of food insecurity. 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According to the government, the decision was a step to counter smuggling (DW 17/09/2019) and is also part of a wider policy to strengthen local agricultural production. The shutdown has begun to impact food prices, which have been rising in September (Bloomberg 15/10/2019). Increased inflation can further limit access to food for poor people and people whose livelihoods have been reduced or destroyed by the floods and now rely on purchasing additional food. It also might aggravate the general availability of food items which has been strained by crop damage due to the flooding.", + "textOrder": 7, + "textCrop": [ + 431.0899963378906, + 44.133026123046875, + 813.9660034179688, + 133.7980499267578 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 421.0743103027344, + "y0": 143.3040313720703, + "x1": 842.0399780273438, + "y1": 176.06900787353516, + "rect": [ + 421.0743103027344, + 143.3040313720703, + 842.0399780273438, + 176.06900787353516 + ], + "text": "Key characteristics", + "textOrder": 8, + "textCrop": [ + 431.0899963378906, + 152.8100128173828, + 568.7350463867188, + 173.4650115966797 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 421.0743103027344, + "y0": 176.06900787353516, + "x1": 842.0399780273438, + "y1": 269.87042236328125, + "rect": [ + 421.0743103027344, + 176.06900787353516, + 842.0399780273438, + 269.87042236328125 + ], + "text": "Demographic profile (population/surface area): Country level: 193 million people / 909,890 square metres Borno: 5.7 million people / 72,000 square metres Delta: 3.9 million people / 17,108 square metres Kebbi: 4.4 million people / 36,985 square metres Kogi: 4.5 million people / 27,747 square metres (GoN National Bureau of Statistics 2011; population projection 2016)", + "textOrder": 9, + "textCrop": [ + 431.0899963378906, + 178.67300415039062, + 734.4566650390625, + 268.3480224609375 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 421.0743103027344, + "y0": 269.87042236328125, + "x1": 842.0399780273438, + "y1": 349.81040954589844, + "rect": [ + 421.0743103027344, + 269.87042236328125, + 842.0399780273438, + 349.81040954589844 + ], + "text": "Poverty statistics: Poverty gap at national poverty lines (%): 17 (2009) Rural poverty gap at national poverty lines (%): 20.1 (2009) Urban poverty gap at national poverty lines (%): 11.6 (2009) Poverty headcount ratio at USD 3.20 a day (2011 PPP) (% of population): 77.6 (2009) Poverty headcount ratio at USD 1.90 a day (2011 PPP) (% of population): 53.5 (2009)", + "textOrder": 10, + "textCrop": [ + 431.0899963378906, + 271.392822265625, + 810.5216064453125, + 348.28802490234375 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 421.0743103027344, + "y0": 349.81040954589844, + "x1": 842.0399780273438, + "y1": 404.4104309082031, + "rect": [ + 421.0743103027344, + 349.81040954589844, + 842.0399780273438, + 404.4104309082031 + ], + "text": "Food security figures: Most households outside northeast Nigeria face Minimal (IPC Phase 1) acute food insecurity. In Borno, Adamawa and Yobe states, Crisis (IPC Phase 3) and Emergency (IPC Phase 4) outcomes continue (FEWS NET 09/2019).", + "textOrder": 11, + "textCrop": [ + 431.0899963378906, + 351.3327941894531, + 810.3082885742188, + 402.8880310058594 + ], + "relevant": False + }, + { + "type": "text", + "page": 3, + "x0": 421.0743103027344, + "y0": 404.4104309082031, + "x1": 842.0399780273438, + "y1": 492.5535125732422, + "rect": [ + 421.0743103027344, + 404.4104309082031, + 842.0399780273438, + 492.5535125732422 + ], + "text": "Nutrition levels: Prevalence of severe acute malnutrition, weight for height (% of children under 5): 2.9 Prevalence of acute malnutrition, weight for height (% of children under 5): 10.6 (2017) Prevalence of chronic malnutrition, height for age (% of children under 5): 43.6 (2017) Prevalence of undernourishment (% of population): 7.9 (2015)", + "textOrder": 12, + "textCrop": [ + 431.0899963378906, + 405.9328308105469, + 813.2286987304688, + 482.718017578125 + ], + 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(2015) Access to electricity (% of population): 59.3 (2016)", + "textOrder": 1, + "textCrop": [ + 28.079999923706055, + 98.80281829833984, + 398.8687744140625, + 137.75804138183594 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 0.0, + "y0": 139.38751983642578, + "x1": 421.04444885253906, + "y1": 160.41698455810547, + "rect": [ + 0.0, + 139.38751983642578, + 421.04444885253906, + 160.41698455810547 + ], + "text": "Source: World Bank 2015", + "textOrder": 2, + "textCrop": [ + 28.079999923706055, + 141.01699829101562, + 119.28911590576172, + 152.06396484375 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 0.0, + "y0": 160.41698455810547, + "x1": 421.04444885253906, + "y1": 213.7740249633789, + "rect": [ + 0.0, + 160.41698455810547, + 421.04444885253906, + 213.7740249633789 + ], + "text": "Response capacity Local and national response capacity", + "textOrder": 3, + "textCrop": [ + 28.079999923706055, + 168.77000427246094, + 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For 2019, the Nigeria Hydrological Services Agency (NIHSA) predicted flooding particularly from June throughout September (NNN 26/06/2019).", + "textOrder": 4, + "textCrop": [ + 28.079999923706055, + 215.30300903320312, + 410.9477233886719, + 279.5080261230469 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 0.0, + "y0": 281.0055236816406, + "x1": 421.04444885253906, + "y1": 335.6255187988281, + "rect": [ + 0.0, + 281.0055236816406, + 421.04444885253906, + 335.6255187988281 + ], + "text": "On the individual state level, the response and capacities of state governments vary. Kebbi government for instance announced in September to earmark 1.5 billion Nigerian naira for flood-related emergencies but it remains unclear what the operational response has been in each state (Guardian Nigeria 21/09/2019).", + "textOrder": 5, + "textCrop": [ + 28.079999923706055, + 282.5030212402344, + 410.6949462890625, + 334.1280212402344 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 0.0, + "y0": 335.6255187988281, + "x1": 421.04444885253906, + "y1": 389.03076171875, + "rect": [ + 0.0, + 335.6255187988281, + 421.04444885253906, + 389.03076171875 + ], + "text": "In the past, the national disaster management framework has been criticized for not clearly defining the role of local, state and government emergency management agencies and emergency response has been said to have been sub-optimal (Olanrewaju, Chitakira, Olanrewaju & Louw, 2019). This raises concerns about the", + "textOrder": 6, + "textCrop": [ + 28.079999923706055, + 337.1230163574219, + 410.9607238769531, + 388.72802734375 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 0.0, + "y0": 389.03076171875, + "x1": 421.04444885253906, + "y1": 402.391845703125, + "rect": [ + 0.0, + 389.03076171875, + 421.04444885253906, + 402.391845703125 + ], + "text": "government\u2019s response capacities regarding the current flooding.", + "textOrder": 7, + "textCrop": [ + 28.079999923706055, + 389.33349609375, + 315.0956115722656, + 400.4606628417969 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 0.0, + "y0": 402.391845703125, + "x1": 421.04444885253906, + "y1": 595.3200073242188, + "rect": [ + 0.0, + 402.391845703125, + 421.04444885253906, + 595.3200073242188 + ], + "text": "The IFRC has launched an Emergency Plan of Action in October that is supposed to last until 4 February 2020 (IFRC EPoA 07/10/2019). National partner organisations involved in the implementation are the Nigerian Red Cross Society (NRCS), the National Emergency Management Agency (NEMA), and the State Emergency Management Agencies (SEMA). On 22 September, a State Emergency Operation Centre (EOC) was activated to coordinate this response. The IFRC disaster response aims to deliver WASH services in Cross River, Kogi, Niger and Taraba states and to support 6,000 persons through a cash transfer programme. Apart from the NRCS, there is no information on local and national NGOs responding to the flood-affected population.", + "textOrder": 8, + "textCrop": [ + 28.079999923706055, + 404.3230285644531, + 410.9989013671875, + 519.1940307617188 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 421.04444885253906, + "y0": 0.0, + "x1": 842.0399780273438, + "y1": 38.69600296020508, + "rect": [ + 421.04444885253906, + 0.0, + 842.0399780273438, + 38.69600296020508 + ], + "text": "ACAPS Briefing Note: Floods in Nigeria", + "textOrder": 9, + "textCrop": [ + 674.02001953125, + 22.557010650634766, + 814.0595703125, + 33.603973388671875 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 421.04444885253906, + "y0": 38.69600296020508, + "x1": 842.0399780273438, + "y1": 63.97927474975586, + "rect": [ + 421.04444885253906, + 38.69600296020508, + 842.0399780273438, + 63.97927474975586 + ], + "text": "International response capacity", + "textOrder": 10, + "textCrop": [ + 431.0899963378906, + 43.78803253173828, + 610.9779663085938, + 61.595069885253906 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 421.04444885253906, + "y0": 63.97927474975586, + "x1": 842.0399780273438, + "y1": 92.45552825927734, + "rect": [ + 421.04444885253906, + 63.97927474975586, + 842.0399780273438, + 92.45552825927734 + ], + "text": "International partners supporting the IFRC\u2019s Emergency Plan of Action are the ICRC and the British Red Cross (IFRC EPoA 07/10/2019).", + "textOrder": 11, + "textCrop": [ + 431.0899963378906, + 66.36347961425781, + 813.3958129882812, + 90.95805358886719 + ], + "relevant": False + }, + { + "type": "text", + "page": 4, + "x0": 421.04444885253906, + "y0": 92.45552825927734, + "x1": 842.0399780273438, + "y1": 172.37552642822266, + "rect": [ + 421.04444885253906, + 92.45552825927734, + 842.0399780273438, + 172.37552642822266 + ], + "text": "Despite the concentration of operational presence of humanitarian actors in Maiduguri, the state capital of Borno, gaps remain across sectors in the city. Already in June, MSF reported that some the capacities of its feeding centre in Maiduguri were reached (MSF 13/08/2019). 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geographical breakdown of the displaced populations, fatalities, injuries, and houses damaged or focuses exclusively on humanitarian needs in the conflict-affected areas in northeast Nigeria. A clear breakdown of humanitarian needs by sector and state level is lacking. Moreover, there is a lack of information regarding the effect of the flooding on non-IDPs and on host communities. On the one hand, there is a detailed picture of sectoral needs of displaced people living in camps in north-east Nigeria through agencies involved with the Humanitarian Response Plan for north-east Nigeria, namely through the multi-sectoral site tracker of the CCCM (CCCM MST report 3 04/10/2019). 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+ "imageLink": "pic-7401e7de-52a1-4f8b-b0fd-a409d2da1668.png", + "imageCaption": "undefined", + "imageMeta": "undefined", + "imageText": "" + } + ] +} + diff --git a/analysis_module/mockserver.py b/analysis_module/mockserver.py index 2aea71b..39532fa 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_GEOLOCATION 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) @@ -547,6 +304,51 @@ def process_extraction_mock(body) -> Any: 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 [] + text_extraction_id = body.get("text_extraction_id", "") + callback_url = body.get("callback_url") + if not documents or not callback_url: + return + + for document in documents: + client_id = document["client_id"] + random_entry_extraction_classification = MOCK_ENTRY_CLASSIFICATION + filepath = save_data_local_and_get_url( + "entry_extraction", client_id, random_extracted_text + ) + + """ + 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( + callback_url, + json=callback_data, + timeout=30, + ) + except Exception: + logger.error("Could not send data to callback url", exc_info=True) + + TYPE_ACTIONS_MOCK = { "topicmodel": topicmodeling_mock_model, "summarization": summarization_mock_model, @@ -554,6 +356,7 @@ 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 } diff --git a/analysis_module/serializers.py b/analysis_module/serializers.py index 971bb50..89b5257 100644 --- a/analysis_module/serializers.py +++ b/analysis_module/serializers.py @@ -90,3 +90,57 @@ 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.SYSTEM, + ) + mock = serializers.BooleanField(default=False) \ No newline at end of file diff --git a/analysis_module/utils.py b/analysis_module/utils.py index 28e378c..ab702ef 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, + ENTRY_EXTRACTION_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.TEXT_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") - return None + elif request_type == NLPRequest.FeaturesType.ENTRY_EXTRACTION: + return urljoin(ENTRY_EXTRACTION_ECS_ENDPOINT, "/extract_entries") + return None \ No newline at end of file diff --git a/analysis_module/views/entry_extraction.py b/analysis_module/views/entry_extraction.py new file mode 100644 index 0000000..c1afa36 --- /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/models.py b/core/models.py index f130010..d3d6fa8 100644 --- a/core/models.py +++ b/core/models.py @@ -451,6 +451,7 @@ 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) diff --git a/core_server/settings.py b/core_server/settings.py index 4786069..464fafc 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") +ENTRY_EXTRACTION_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..de8bdd3 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:-}