From ff42c78194086a57a7eb63f886b9d14433524443 Mon Sep 17 00:00:00 2001 From: Mark Neumann Date: Thu, 14 Jan 2021 11:09:44 -0800 Subject: [PATCH] clean history --- .gitignore | 160 + .skiff/cloudbuild-deploy.yaml | 140 + .skiff/webapp.jsonnet | 352 + LICENSE | 201 + README.md | 168 + api/.dockerignore | 8 + api/.flake8 | 12 + api/Dockerfile | 22 + api/app/__init__.py | 0 api/app/annotations.py | 38 + api/app/metadata.py | 32 + api/app/pre_serve.py | 38 + api/app/utils.py | 20 + .../allowed_users_local_development.txt | 1 + api/config/configuration.json | 87 + api/main.py | 321 + api/pytest.ini | 4 + api/requirements.txt | 21 + api/test/__init__.py | 0 api/test/fixtures/config/configuration.json | 24 + ...ebb2bed8865945e7fddc99efd791887bb7e14f.pdf | Bin 0 -> 633701 bytes .../pdf_structure.json | 1 + .../fixtures/status/example@gmail.com.json | 12 + api/test/fixtures/users/allowed.txt | 2 + api/test/test_app.py | 174 + bin/kubeconf | 70 + bin/sh | 29 + cli/__init__.py | 0 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13 + ui/public/robots.txt | 12 + ui/src/App.tsx | 59 + ui/src/api/index.ts | 111 + ui/src/components/AnnotationSummary.tsx | 73 + ui/src/components/CenterOnPage.tsx | 8 + ui/src/components/PDF.tsx | 274 + ui/src/components/RelationModal.tsx | 69 + ui/src/components/Selection.tsx | 232 + ui/src/components/index.ts | 5 + ui/src/components/sidebar/Annotations.tsx | 113 + .../components/sidebar/AssignedPaperList.tsx | 118 + ui/src/components/sidebar/Comment.tsx | 37 + ui/src/components/sidebar/Header.tsx | 24 + ui/src/components/sidebar/Labels.tsx | 138 + ui/src/components/sidebar/Relations.tsx | 31 + ui/src/components/sidebar/common.tsx | 60 + ui/src/components/sidebar/index.ts | 7 + ui/src/components/sidebar/pawlsLogo.png | Bin 0 -> 26226 bytes ui/src/context/AnnotationStore.ts | 200 + ui/src/context/PDFStore.ts | 223 + ui/src/context/index.ts | 2 + ui/src/index.tsx | 23 + ui/src/listeners/index.tsx | 164 + ui/src/pages/PDFPage.tsx | 313 + ui/src/pages/index.ts | 1 + ui/src/react-app-env.d.ts | 1 + ui/src/utils.ts | 29 + ui/tsconfig.json | 30 + ui/yarn.lock | 11996 ++++++++++++++++ 114 files changed, 19704 insertions(+) create mode 100644 .gitignore create mode 100644 .skiff/cloudbuild-deploy.yaml create mode 100644 .skiff/webapp.jsonnet create mode 100644 LICENSE create mode 100644 README.md create mode 100644 api/.dockerignore create mode 100644 api/.flake8 create mode 100644 api/Dockerfile create mode 100644 api/app/__init__.py create mode 100644 api/app/annotations.py create mode 100644 api/app/metadata.py create mode 100644 api/app/pre_serve.py create mode 100644 api/app/utils.py create mode 100644 api/config/allowed_users_local_development.txt create mode 100644 api/config/configuration.json create mode 100644 api/main.py create mode 100644 api/pytest.ini create mode 100644 api/requirements.txt create mode 100644 api/test/__init__.py create mode 100644 api/test/fixtures/config/configuration.json create mode 100644 api/test/fixtures/data/3febb2bed8865945e7fddc99efd791887bb7e14f/3febb2bed8865945e7fddc99efd791887bb7e14f.pdf create mode 100644 api/test/fixtures/data/3febb2bed8865945e7fddc99efd791887bb7e14f/pdf_structure.json create mode 100644 api/test/fixtures/status/example@gmail.com.json create mode 100644 api/test/fixtures/users/allowed.txt create mode 100644 api/test/test_app.py create mode 100755 bin/kubeconf create mode 100755 bin/sh create mode 100644 cli/__init__.py create mode 100644 cli/pawls/__init__.py create mode 100644 cli/pawls/__main__.py create mode 100644 cli/pawls/commands/__init__.py create mode 100644 cli/pawls/commands/assign.py create mode 100644 cli/pawls/commands/export.py create mode 100644 cli/pawls/commands/metric.py create mode 100644 cli/pawls/commands/preannotate.py create mode 100644 cli/pawls/commands/preprocess.py create mode 100644 cli/pawls/commands/status.py create mode 100644 cli/pawls/commands/utils.py create mode 100644 cli/pawls/preprocessors/__init__.py create mode 100644 cli/pawls/preprocessors/grobid.py create mode 100644 cli/pawls/preprocessors/model.py create mode 100644 cli/pawls/preprocessors/pdfplumber.py create mode 100644 cli/readme.md create mode 100644 cli/requirements.txt create mode 100644 cli/setup.py create mode 100644 cli/test/__init__.py create mode 100644 cli/test/assign_test.py create mode 100644 cli/test/export_test.py create mode 100644 cli/test/fixtures/anno.json create mode 100644 cli/test/fixtures/configuration.json create mode 100644 cli/test/fixtures/pawls/34f25a8704614163c4095b3ee2fc969b60de4698/34f25a8704614163c4095b3ee2fc969b60de4698.pdf create mode 100644 cli/test/fixtures/pawls/34f25a8704614163c4095b3ee2fc969b60de4698/markn@example.com_annotations.json create mode 100644 cli/test/fixtures/pawls/34f25a8704614163c4095b3ee2fc969b60de4698/pdf_structure.json create mode 100644 cli/test/fixtures/pawls/3febb2bed8865945e7fddc99efd791887bb7e14f/3febb2bed8865945e7fddc99efd791887bb7e14f.pdf create mode 100644 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proxy/dist/.gitignore create mode 100644 proxy/local.conf create mode 100644 proxy/nginx.conf create mode 100644 proxy/prod.conf create mode 100644 scripts/ai2-internal/README.md create mode 100644 scripts/ai2-internal/fetch_pdfs.py create mode 100644 scripts/generate_pdf_layouts.py create mode 100644 skiff.json create mode 100644 skiff_files/.git_keep create mode 100644 sonar/Dockerfile create mode 100644 sonar/ping.py create mode 100644 sonar/requirements.txt create mode 100644 ui/.dockerignore create mode 100644 ui/.env create mode 100644 ui/Dockerfile create mode 100644 ui/package.json create mode 100644 ui/public/favicon.ico create mode 100644 ui/public/index.html create mode 100644 ui/public/robots.txt create mode 100644 ui/src/App.tsx create mode 100644 ui/src/api/index.ts create mode 100644 ui/src/components/AnnotationSummary.tsx create mode 100644 ui/src/components/CenterOnPage.tsx create mode 100644 ui/src/components/PDF.tsx create mode 100644 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ui/src/react-app-env.d.ts create mode 100644 ui/src/utils.ts create mode 100644 ui/tsconfig.json create mode 100644 ui/yarn.lock diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..79cf0a00 --- /dev/null +++ b/.gitignore @@ -0,0 +1,160 @@ +# Python cache files +__pycache__ +**/*.pyc + +# UI dependencies +ui/node_modules/ + +# UI build output +ui/build/ + +# UI package manager debug output +ui/*.log +.skiff/webapp.yaml + +# Docker compose env files +.env + +# Ignore any files in ./skiff_files +skiff_files/* + +### Python Build Related ### + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ \ No newline at end of file diff --git a/.skiff/cloudbuild-deploy.yaml b/.skiff/cloudbuild-deploy.yaml new file mode 100644 index 00000000..8c76f7e7 --- /dev/null +++ b/.skiff/cloudbuild-deploy.yaml @@ -0,0 +1,140 @@ +# This file tells Google Cloud Build how to deploy the application. +# It can be attached to a variety of triggers, the default being whenever +# someone merges changes to the `master` branch. +steps: +# Pull down the latest versions of each Docker image, so the build is faster. +- id: 'api.pull' + name: 'gcr.io/cloud-builders/docker' + entrypoint: '/bin/bash' + args: [ + '-c', + 'docker pull gcr.io/$PROJECT_ID/$REPO_NAME-api:latest || exit 0' + ] + waitFor: [ '-' ] +- id: 'ui.pull' + name: 'gcr.io/cloud-builders/docker' + entrypoint: '/bin/bash' + args: [ + '-c', + 'docker pull gcr.io/$PROJECT_ID/$REPO_NAME-ui:latest || exit 0' + ] + waitFor: [ '-' ] +- id: 'proxy.pull' + name: 'gcr.io/cloud-builders/docker' + entrypoint: '/bin/bash' + args: [ + '-c', + 'docker pull gcr.io/$PROJECT_ID/$REPO_NAME-proxy:latest || exit 0' + ] + waitFor: [ '-' ] +# Build docker images for each piece of the puzzle +- id: 'api.build' + name: 'gcr.io/cloud-builders/docker' + entrypoint: '/bin/bash' + args: [ + '-c', + 'docker build -t gcr.io/$PROJECT_ID/$REPO_NAME-api:latest -t gcr.io/$PROJECT_ID/$REPO_NAME-api:$COMMIT_SHA --cache-from gcr.io/$PROJECT_ID/$REPO_NAME-api:latest .' + ] + waitFor: [ 'api.pull' ] + dir: 'api' +- id: 'ui.build' + name: 'gcr.io/cloud-builders/docker' + args: [ + 'build', + '-t', 'gcr.io/$PROJECT_ID/$REPO_NAME-ui:latest', + '-t', 'gcr.io/$PROJECT_ID/$REPO_NAME-ui:$COMMIT_SHA', + '--cache-from', 'gcr.io/$PROJECT_ID/$REPO_NAME-ui:latest', + '.', + '--build-arg', 'NODE_ENV=production', + '--build-arg', 'BABEL_ENV=production' + ] + waitFor: [ 'ui.pull' ] + dir: 'ui' +- id: 'ui.dist.create' + name: 'gcr.io/cloud-builders/docker' + args: [ + 'create', + '--name', '$REPO_NAME-ui', + 'gcr.io/$PROJECT_ID/$REPO_NAME-ui:$COMMIT_SHA', + ] + waitFor: [ 'ui.build' ] + dir: 'ui' +- id: 'ui.dist.cp' + name: 'gcr.io/cloud-builders/docker' + args: [ + 'cp', + '$REPO_NAME-ui:/usr/local/src/skiff/app/ui/build', + '/workspace/proxy/dist' + ] + waitFor: ['ui.dist.create'] +- id: 'proxy.build' + name: 'gcr.io/cloud-builders/docker' + args: [ + 'build', + '-t', 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:latest', + '-t', 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:$COMMIT_SHA', + '--cache-from', 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:latest', + '.', + '--build-arg', 'CONF_FILE=prod.conf' + ] + waitFor: [ 'ui.dist.cp', 'proxy.pull' ] + dir: 'proxy' +- id: 'api.push' + name: 'gcr.io/cloud-builders/docker' + args: [ + 'push', + 'gcr.io/$PROJECT_ID/$REPO_NAME-api:$COMMIT_SHA', + ] + waitFor: [ 'api.build' ] +- id: 'proxy.push' + name: 'gcr.io/cloud-builders/docker' + args: [ + 'push', + 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:$COMMIT_SHA' + ] + waitFor: [ 'proxy.build' ] +# Generate our Kubernetes configuration +- id: 'config' + name: 'gcr.io/ai2-reviz/jsonnet' + args: [ + 'eval', + '-y', + '--output-file', './webapp.yaml', + '--tla-str', 'env=$_ENV', + '--tla-str', 'apiImage=gcr.io/$PROJECT_ID/$REPO_NAME-api:$COMMIT_SHA', + '--tla-str', 'proxyImage=gcr.io/$PROJECT_ID/$REPO_NAME-proxy:$COMMIT_SHA', + '--tla-str', 'sha=$COMMIT_SHA', + '--tla-str', 'cause=Automated Skiff Deploy SHA:$COMMIT_SHA BUILD:$BUILD_ID', + '--tla-str', 'branch=$BRANCH_NAME', + '--tla-str', 'repo=$REPO_NAME', + '--tla-str', 'buildId=$BUILD_ID', + './webapp.jsonnet' + ] + dir: '.skiff' + waitFor: [ '-' ] +# Deploy the image to Kubernetes +- id: 'deploy' + name: 'gcr.io/ai2-reviz/rudder' + args: [ + 'deploy', + '-f', + 'webapp.yaml' + ] + dir: '.skiff' + waitFor: [ 'api.push', 'proxy.push', 'config' ] +substitutions: + _ENV: prod +images: [ + 'gcr.io/$PROJECT_ID/$REPO_NAME-api:$COMMIT_SHA', + 'gcr.io/$PROJECT_ID/$REPO_NAME-api:latest', + 'gcr.io/$PROJECT_ID/$REPO_NAME-ui:$COMMIT_SHA', + 'gcr.io/$PROJECT_ID/$REPO_NAME-ui:latest', + 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:$COMMIT_SHA', + 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:latest' +] +artifacts: + objects: + location: 'gs://skiff-archive/$REPO_NAME/$_ENV/$BUILD_ID/$COMMIT_SHA' + paths: ['.skiff/webapp.yaml'] + +timeout: 900s diff --git a/.skiff/webapp.jsonnet b/.skiff/webapp.jsonnet new file mode 100644 index 00000000..39b56205 --- /dev/null +++ b/.skiff/webapp.jsonnet @@ -0,0 +1,352 @@ +/** + * This is a template that's compiled down to a definition of the + * infrastructural resources required for running your application. + * + * For more information on the JSONNET language, see: + * https://jsonnet.org/learning/getting_started.html + */ + +// This file is generated once at template creation time and unlikely to change +// from that point forward. +local config = import '../skiff.json'; + +function( + apiImage, proxyImage, cause, sha, env='prod', branch='', repo='', + buildId='' +) + // We only allow registration of hostnames attached to '*.apps.allenai.org' + // at this point. If you need a custom domain, contact us: reviz@allenai.org. + local topLevelDomain = '.apps.allenai.org'; + local hosts = [ + if env == 'prod' then + config.appName + topLevelDomain + else + config.appName + '.' + env + topLevelDomain + ]; + + // In production we run two versions of your application, as to ensure that + // if one instance goes down or is busy, end users can still use the application. + // In all other environments we run a single instance to save money. + local replicas = ( + if env == 'prod' then + 2 + else + 1 + ); + + // Each app gets it's own namespace. + local namespaceName = config.appName; + + // Since we deploy resources for different environments in the same namespace, + // we need to give things a fully qualified name that includes the environment + // as to avoid unintentional collission / redefinition. + local fullyQualifiedName = config.appName + '-' + env; + + // Every resource is tagged with the same set of labels. These labels serve the + // following purposes: + // - They make it easier to query the resources, i.e. + // kubectl get pod -l app=my-app,env=staging + // - The service definition uses them to find the pods it directs traffic to. + local namespaceLabels = { + app: config.appName, + contact: config.contact, + team: config.team + }; + + local labels = namespaceLabels + { + env: env + }; + + // By default multiple instances of your application could get scheduled + // to the same node. This means if that node goes down your application + // does too. We use the label below to avoid that. + local antiAffinityLabels = { + onlyOneOfPerNode: config.appName + '-' + env + }; + local podLabels = labels + antiAffinityLabels; + + // Annotations carry additional information about your deployment that + // we use for auditing, debugging and administrative purposes + local annotations = { + "apps.allenai.org/sha": sha, + "apps.allenai.org/branch": branch, + "apps.allenai.org/repo": repo, + "apps.allenai.org/build": buildId + }; + + // The port the NGINX proxy is bound to. + local proxyPort = 80; + + // The port the API (Python Flask application) is bound to. + local apiPort = 8000; + + // This is used to verify that the proxy (and thereby the UI portion of the + // application) is healthy. If this fails the application won't receive traffic, + // and may be restarted. + local proxyHealthCheck = { + port: proxyPort, + scheme: 'HTTP' + }; + + // This is used to verify that the API is funtional. We simply check for + // whether the socket is open and available. + local apiHealthCheck = { + port: apiPort, + scheme: 'HTTP' + }; + + local namespace = { + apiVersion: 'v1', + kind: 'Namespace', + metadata: { + name: namespaceName, + labels: namespaceLabels + } + }; + + local ingress = { + apiVersion: 'extensions/v1beta1', + kind: 'Ingress', + metadata: { + name: fullyQualifiedName, + namespace: namespaceName, + labels: labels, + annotations: annotations + { + 'cert-manager.io/cluster-issuer': 'letsencrypt-prod', + 'kubernetes.io/ingress.class': 'nginx', + 'nginx.ingress.kubernetes.io/ssl-redirect': 'true', + 'nginx.ingress.kubernetes.io/auth-url': 'https://google.login.apps.allenai.org/oauth2/auth', + 'nginx.ingress.kubernetes.io/auth-signin': 'https://google.login.apps.allenai.org/oauth2/start?rd=https://$host$request_uri', + 'nginx.ingress.kubernetes.io/auth-response-headers': 'X-Auth-Request-User, X-Auth-Request-Email' + } + }, + spec: { + tls: [ + { + secretName: fullyQualifiedName + '-tls', + hosts: hosts + } + ], + rules: [ + { + host: host, + http: { + paths: [ + { + backend: { + serviceName: fullyQualifiedName, + servicePort: proxyPort + } + } + ] + } + } for host in hosts + ] + } + }; + + local deployment = { + apiVersion: 'apps/v1', + kind: 'Deployment', + metadata: { + labels: labels, + name: fullyQualifiedName, + namespace: namespaceName, + annotations: annotations + { + 'kubernetes.io/change-cause': cause + } + }, + spec: { + revisionHistoryLimit: 3, + replicas: replicas, + selector: { + matchLabels: labels + }, + template: { + metadata: { + name: fullyQualifiedName, + namespace: namespaceName, + labels: podLabels, + annotations: annotations + }, + spec: { + # This block tells the cluster that we'd like to make sure + # each instance of your application is on a different node. This + # way if a node goes down, your application doesn't: + # See: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#node-isolation-restriction + affinity: { + podAntiAffinity: { + requiredDuringSchedulingIgnoredDuringExecution: [ + { + labelSelector: { + matchExpressions: [ + { + key: labelName, + operator: "In", + values: [ antiAffinityLabels[labelName], ], + } for labelName in std.objectFields(antiAffinityLabels) + ], + }, + topologyKey: "kubernetes.io/hostname" + }, + ] + }, + }, + volumes: [ + { + name: 'skiff-files', + persistentVolumeClaim: { + claimName: 'skiff-files-server-pawls' + } + }, + { + name: "users", + secret: { + secretName: "users" + } + } + ], + containers: [ + { + name: fullyQualifiedName + '-api', + image: apiImage, + env: [ { name: "IN_PRODUCTION", value: "prod" }], + volumeMounts: [ + { + mountPath: '/skiff_files/apps/pawls', + name: 'skiff-files', + readOnly: false + }, + { + name: 'users', + mountPath: '/users', + readOnly: true + } + ], + # The "probes" below allow Kubernetes to determine + # if your application is working properly. + # + # The readiness probe is used to determine if + # an instance of your application can accept live + # requests. The configuration below tells Kubernetes + # to stop sending live requests to your application + # if it returns 3 non 2XX responses over 30 seconds. + # When this happens the application instance will + # be taken out of rotation and given time to "catch-up". + # Once it returns a single 2XX, Kubernetes will put + # it back in rotation. + # + # The liveness probe is used to determine if an + # instance needs to be restarted. The configuration + # below tells Kubernetes to restart the application + # if it's unhealthy for 90 seconds. You can increase + # the `failureThreshold` if your API is slow. + # + # The route that's used by these probes should not + # depend on any external services, it should purely + # assess the health of your local application. + # + # Lastly, the `initialDelaySeconds` instructs + # Kubernetes to wait 30 seconds before starting the + # liveness probe. This is to give your application + # time to start. If your application needs more time + # you should increase this value and give things + # a little headroom, things are always a little slower + # in the cloud :). + readinessProbe: { + httpGet: apiHealthCheck + { + path: '/?check=readiness_probe' + }, + periodSeconds: 10, + failureThreshold: 3 + }, + livenessProbe: { + httpGet: apiHealthCheck + { + path: '/?check=liveness_probe' + }, + periodSeconds: 10, + failureThreshold: 9, + initialDelaySeconds: 30 + }, + # This tells Kubernetes what CPU and memory resources your API needs. + # We set these values low by default, as most applications receive + # bursts of activity and accordingly don't need dedicated resources + # at all times. + # + # Your application will be allowed to use more resources than what's + # specified below. That said your application might be killed if it + # uses more than what's requested. If you know you need more memory + # or that your workload is CPU intensive, consider increasing the + # values below. + # + # Currently all pods (the collection of containers defined here) + # cannot request more than 23.9 GB of RAM or 3.92 (3920m) CPU + # cycles. If you need more resources reach out to reviz@allenai.org, + # we can provide accomodations on a case by case basis. + # + # If your application is of high criticality and you'd like to ensure + # maximum availability, reach out to reviz@allenai.org for help modifying + # these settings. + # + # See the following for more information on these values: + # https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/#how-pods-with-resource-requests-are-scheduled + resources: { + requests: { + cpu: '50m', + memory: '500Mi' + } + }, + }, + { + name: fullyQualifiedName + '-proxy', + image: proxyImage, + readinessProbe: { + httpGet: proxyHealthCheck + { + path: '/?check=rdy' + } + }, + livenessProbe: { + failureThreshold: 6, + httpGet: proxyHealthCheck + { + path: '/?check=live' + } + }, + resources: { + requests: { + cpu: '50m', + memory: '100Mi' + } + } + } + ] + } + } + } + }; + + local service = { + apiVersion: 'v1', + kind: 'Service', + metadata: { + name: fullyQualifiedName, + namespace: namespaceName, + labels: labels, + annotations: annotations + }, + spec: { + selector: labels, + ports: [ + { + port: proxyPort, + name: 'http' + } + ] + } + }; + + [ + namespace, + ingress, + deployment, + service + ] diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..9b259bdf --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + https://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright {yyyy} {name of copyright owner} + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 00000000..1d131e25 --- /dev/null +++ b/README.md @@ -0,0 +1,168 @@ +
+ +
+ + PDF Annotations with Labels and Structure is software that makes it easy + to collect a series of annotations associated with a PDF document. It was written + specifically for annotating academic papers within the [Semantic Scholar](https://www.semanticscholar.org) corpus, but can be used with any collection of PDF documents. + +### Quick Start + +*Quick start will download some pre-processed PDFs and get the UI set up so that you can see them. If you want to pre-process your own pdfs, keep reading! If it's your first time working with PAWLS, we recommend you try the quick start first though.* + +First, we need to download some processed PDFs to view in the UI. PAWLS uses the PDFs themselves to render in the browser, as well as using a JSON file of extracted token bounding boxes per page, called `pdf_structure.json`. The [PAWLS CLI](cli/readme.md) can be used to do this pre-processing, but for the quick start, we have done it for you. Download them from the provided AWS S3 Bucket like so: + +```bash +aws s3 sync s3://ai2-s2-pawls-public/example-data ./skiff_files/apps/pawls/papers/ +``` + +Configuration in PAWLS is controlled by a json file, located in the [`api/config`](./api/config/configuration.json) directory. The location that we downloaded the pdfs to above corresponds to the location in the config file, where it is mounted in using [`docker-compose.yaml`](./docker-compose.yaml). So, when PAWLS starts up, the API knows where to look to serve the PDFs we want. + +Next, we can start the services required to use PAWLS using `docker-compose`: + +``` +~ docker-compose up --build +``` + +This process launches 4 services: +- the `ui`, which renders the user interface that PAWLS uses +- the `api`, which serves PDFs and saves/recieves annotations +- a `proxy` responsible for forwarding traffic to the appropriate services. +- A `grobid` service, running [a fork of Grobid](https://github.com/allenai/grobid). This is not actually necessary for the application, but is useful for the CLI. + +You'll see output from each. + +Once all of these have come up, navigate to `localhost:8080` in your browser and you should see the PAWLS UI! Happy annotating. + + +### Getting Started + +In order to run a local environment, you'll need to use the [PAWLS CLI](cli/readme.md) to download the PDFs and metadata you want to serve. The PDFs should be put in `skiff_files/apps/pawls`. + +For instance, you can run this command to download the specified PDF: + +```bash + # Fetches pdfs from semantic scholar's S3 buckets. + python scripts/ai2-internal/fetch_pdfs.py skiff_files/apps/pawls/papers 34f25a8704614163c4095b3ee2fc969b60de4698 3febb2bed8865945e7fddc99efd791887bb7e14f 553c58a05e25f794d24e8db8c2b8fdb9603e6a29 + # ensure that the papers are pre-processed with grobid so that they have token information. + pawls preprocess grobid skiff_files/apps/pawls/papers + # Assign the development user to all the papers we've downloaded. + pawls assign skiff_files/apps/pawls/papers development_user --all --name-file skiff_files/apps/pawls/papers/name_mapping.json +``` + +and then open up the UI locally by running `docker-compose up`. + +### Authentication and Authorization + +*Authentication* is simply checking that users are who they say they are. Whether +or not these users' requests are allowed (e.g., to view a PDFs) is considerd +*authorization*. See more about this distinction at [Skiff +Login](https://skiff.allenai.org/login.html). + +#### Authentication + +All requests must be authenticated. + +* The production deployment of PAWLS uses [Skiff + Login](https://skiff.allenai.org/login.html) to authenticate requests. New + users are bounced to a Google login workflow, and redirected back to the site + if they authenticate with Google. Authenticated requests carry an HTTP header + that identifies the user. +* For local development, there is no login workflow. Instead, all requests are + supplemented with a hard-coded authentication header in + [proxy/local.conf](proxy/local.conf) specifying that the user is + `development_user@example.com`. + +Look at the function `get_user_from_header` in [main.py](api/main.py) for +details. + +#### Authorization + +Authorization is enforced by the PAWLS app. A file of allowed user email +addresses is consulted on every request. + +* In production, this file is sourced from [the secret named + "users"](http://marina.apps.allenai.org/a/pawls/s/users) in Marina, which is + projected to `/users/allowed.txt` in the container. +* For local development, this file is sourced from + [allowed_users_local_development.txt](api/config/allowed_users_local_development.txt), + and also projected to `/users/allowed.txt` in the Docker container. + +The format of the file is simply a list of allowed email addresses. + +There's a special case when an allowed email address in this file starts with +"@", meaning all users in that domain are allowed. That is, an entry +"@allenai.org" will grant access to all AI2 people. + +Look at the function `user_is_allowed` in [main.py](api/main.py) for details. + +### Python Development + +The Python service and Python cli are formatted using `black` and `flake8`. Currently this is run in a local environment +using the app's `requirements.txt`. To run the linters: + +``` +black api/ +flake8 api/ +``` + + +## Prerequisites + +Make sure that you have the latest version of [Docker 🐳](https://www.docker.com/get-started) +installed on your local machine. + +To start a version of the application locally for development purposes, run +this command: + +``` +~ docker-compose up --build +``` + +This process launches 3 services, the `ui`, `api` and a `proxy` responsible +for forwarding traffic to the appropriate services. You'll see output +from each. + +It might take a minute or two for the application to start, particularly +if it's the first time you've executed this command. Be patience and wait +for a clear message indicating that all of the required services have +started up. + +As you make changes the running application will be automatically updated. +Simply refresh your browser to see them. + +Sometimes one portion of your application will crash due to errors in the code. +When this occurs resolve the related issue and re-run `docker-compose up --build` +to start things back up. + +## Development Tips and Tricks + +The skiff template contains some features which are ideal for a robust web application, but might be un-intuitive for researchers. +Below are some small technical points that might help you if you are making substantial changes to the skiff template. + +* Skiff uses `sonar` to check that all parts of the application (frontend, backend) are up and running before serving requests. +To do this, it checks that your api returns 2XX codes from its root url - if you change the server, you'll need to make sure to add +code which returns a 2XX response from your server. + +* To ease development/deployment differences, skiff uses a proxy to route different urls to different containers in your application. +The TL;DR of this is the following: + +| External URL | Internal URL | Container | +|------------------------|:------------------:|:---------:| +| `localhost:8080/*` | `localhost:3000/*` | `ui` | +| `localhost:8080/api/*` | `localhost:8000/*` | `api` | + +So, in your web application, you would make a request, e.g `axios.get("/api/route", data)`, which the server recieves at `localhost:8000/route`. +This makes it easy to develop without worrying about where apis will be hosted in production vs development, and also allows for things like +rate limiting. The configuration for the proxy lives [here](./proxy/local.conf) for development and [here](./proxy/prod.conf) for production. + +For example, if you wanted to expose the `docs` route `localhost:8000/docs` from your `api` container to users of your app in production, you would add this to `prod.conf`: + +``` +location /docs/ { + limit_req zone=api; + proxy_pass http://api:8000/; +} +``` + +PAWLS is an open-source project developed by [the Allen Institute for Artificial Intelligence (AI2)](http://www.allenai.org). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering. diff --git a/api/.dockerignore b/api/.dockerignore new file mode 100644 index 00000000..232db3d7 --- /dev/null +++ b/api/.dockerignore @@ -0,0 +1,8 @@ +# Python cache files +**.pyc +**/__pycache__ +.dockerignore + +# Docker artifacts +Dockerfile + diff --git a/api/.flake8 b/api/.flake8 new file mode 100644 index 00000000..ebb4370f --- /dev/null +++ b/api/.flake8 @@ -0,0 +1,12 @@ +[flake8] +max-line-length = 115 + +ignore = + # these rules don't play well with black + E203 # whitespace before : + W503 # line break before binary operator + +per-file-ignores = + # __init__.py files are allowed to have unused imports and lines-too-long + */__init__.py:F401 + */**/**/__init__.py:F401,E501 diff --git a/api/Dockerfile b/api/Dockerfile new file mode 100644 index 00000000..905ee00c --- /dev/null +++ b/api/Dockerfile @@ -0,0 +1,22 @@ +FROM python:3.7.2 + +# Setup a spot for the api code +WORKDIR /usr/local/src/skiff/app/api + +ARG GITHUB_ACCESS_TOKEN + +# Install Python dependencies +RUN pip install --upgrade pip setuptools wheel +COPY requirements.txt . + +RUN pip install -r requirements.txt + + +# Copy over the source code +COPY app app/ +COPY config config/ +COPY main.py main.py + +# Kick things off +ENTRYPOINT [ "uvicorn" ] +CMD ["main:app", "--host", "0.0.0.0"] diff --git a/api/app/__init__.py b/api/app/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/api/app/annotations.py b/api/app/annotations.py new file mode 100644 index 00000000..e4fe6d27 --- /dev/null +++ b/api/app/annotations.py @@ -0,0 +1,38 @@ +from typing import Optional, List +from pydantic import BaseModel + + +class Bounds(BaseModel): + left: float + top: float + right: float + bottom: float + + +class Label(BaseModel): + text: str + color: str + + +class TokenId(BaseModel): + pageIndex: int + tokenIndex: int + + +class Annotation(BaseModel): + id: str + page: int + label: Label + bounds: Bounds + tokens: Optional[List[TokenId]] = None + + +class RelationGroup(BaseModel): + sourceIds: List[str] + targetIds: List[str] + label: Label + + +class PdfAnnotation(BaseModel): + annotations: List[Annotation] + relations: List[RelationGroup] diff --git a/api/app/metadata.py b/api/app/metadata.py new file mode 100644 index 00000000..00120c13 --- /dev/null +++ b/api/app/metadata.py @@ -0,0 +1,32 @@ +from typing import Optional, List + +from pydantic import BaseModel + + +class PaperStatus(BaseModel): + sha: str + name: str + annotations: int + relations: int + finished: bool + junk: bool + comments: str + completedAt: Optional[str] + + @staticmethod + def empty(sha: str, name: str): + return PaperStatus( + sha=sha, + name=name, + annotations=0, + relations=0, + finished=False, + junk=False, + comments="", + completedAt=None, + ) + + +class Allocation(BaseModel): + papers: List[PaperStatus] + hasAllocatedPapers: bool diff --git a/api/app/pre_serve.py b/api/app/pre_serve.py new file mode 100644 index 00000000..05962f6d --- /dev/null +++ b/api/app/pre_serve.py @@ -0,0 +1,38 @@ +from typing import NamedTuple, List, Dict +import json + + +class Configuration(NamedTuple): + """ + General configuration for the annotation tool. + + output_directory: str, required. + The directory where the pdfs and + annotation output will be stored. + labels: List[Dict[str, str]], required. + The labels in use for annotation. + relations: List[Dict[str, str]], required. + The relations in use for annotation. + users_file: Name str, required + Filename where list of allowed users is specified. + """ + + output_directory: str + labels: List[Dict[str, str]] + relations: List[Dict[str, str]] + users_file: str + + +def load_configuration(filepath: str) -> Configuration: + try: + blob = json.load(open(filepath)) + return Configuration(**blob) + except TypeError as e: + print( + "Error loading configuration file %s, maybe you're missing a required field? Exception string: %s" + % (filepath, e) + ) + raise e + except Exception as e: + print("Error loading configuration file %s" % filepath) + raise e diff --git a/api/app/utils.py b/api/app/utils.py new file mode 100644 index 00000000..c343d893 --- /dev/null +++ b/api/app/utils.py @@ -0,0 +1,20 @@ +from pythonjsonlogger import jsonlogger + + +class StackdriverJsonFormatter(jsonlogger.JsonFormatter): + """ + Custom log JSON log formatter that adds the severity member, allowing + end users to filter logs by the level of the log message emitted. + + TODO: Parse request logs and add fields for each request element (user-agent + processing time, etc) + + TODO:Add a timestamp that's used in place of Stackdriver's records (which + reflect the time the log was written to Stackdriver, I think). + """ + + def add_fields(self, log_record, record, message_dict): + super(StackdriverJsonFormatter, self).add_fields( + log_record, record, message_dict + ) + log_record["severity"] = record.levelname diff --git a/api/config/allowed_users_local_development.txt b/api/config/allowed_users_local_development.txt new file mode 100644 index 00000000..2fd6bacb --- /dev/null +++ b/api/config/allowed_users_local_development.txt @@ -0,0 +1 @@ +development_user@example.com diff --git a/api/config/configuration.json b/api/config/configuration.json new file mode 100644 index 00000000..8422e566 --- /dev/null +++ b/api/config/configuration.json @@ -0,0 +1,87 @@ +{ + "output_directory": "/skiff_files/apps/pawls/papers/", + "labels": [ + { + "text": "Title", + "color": "#70DDBA" + }, + { + "text": "Author", + "color": "#B8DE6F" + }, + { + "text": "Abstract", + "color": "#A8DDA8" + }, + { + "text": "Keywords", + "color": "#64ED96" + }, + { + "text": "Section", + "color": "#FFDA77" + }, + { + "text": "Paragraph", + "color": "#D9E4DD" + }, + { + "text": "ListItem", + "color": "#CEEBFC" + }, + { + "text": "BibItem", + "color": "#C6CC82" + }, + { + "text": "Default", + "color": "#BFBFBF" + }, + { + "text": "Equation", + "color": "#D6D2C4" + }, + { + "text": "Figure", + "color": "#EFDDF5" + }, + { + "text": "Table", + "color": "#C7C2EB" + }, + { + "text": "Algorithm", + "color": "#CFD4DF" + }, + { + "text": "Caption", + "color": "#2F85F7" + }, + { + "text": "Footer", + "color": "#FFB2A7" + }, + { + "text": "Header", + "color": "#FFB2A7" + }, + { + "text": "Author/Name", + "color": "#F5A25D" + }, + { + "text": "Author/Affiliation", + "color": "#28ABB9" + }, + { + "text": "Author/Email", + "color": "#706897" + }, + { + "text": "Paper/Venue", + "color": "#34626C" + } + ], + "relations": [], + "users_file": "/users/allowed.txt" +} diff --git a/api/main.py b/api/main.py new file mode 100644 index 00000000..ef251db8 --- /dev/null +++ b/api/main.py @@ -0,0 +1,321 @@ +from typing import List, Optional, Dict, Any +import logging +import os +import json +import glob + +from fastapi import FastAPI, HTTPException, Header, Response, Body +from fastapi.responses import FileResponse +from fastapi.encoders import jsonable_encoder + +from app.metadata import PaperStatus, Allocation +from app.annotations import Annotation, RelationGroup, PdfAnnotation +from app.utils import StackdriverJsonFormatter +from app import pre_serve + +IN_PRODUCTION = os.getenv("IN_PRODUCTION", "dev") + +CONFIGURATION_FILE = os.getenv( + "PAWLS_CONFIGURATION_FILE", "/usr/local/src/skiff/app/api/config/configuration.json" +) + +handlers = None + +if IN_PRODUCTION == "prod": + json_handler = logging.StreamHandler() + json_handler.setFormatter(StackdriverJsonFormatter()) + handlers = [json_handler] + +logging.basicConfig( + level=os.environ.get("LOG_LEVEL", default=logging.INFO), handlers=handlers +) +logger = logging.getLogger("uvicorn") + +# boto3 logging is _super_ verbose. +logging.getLogger("boto3").setLevel(logging.CRITICAL) +logging.getLogger("botocore").setLevel(logging.CRITICAL) +logging.getLogger("nose").setLevel(logging.CRITICAL) +logging.getLogger("s3transfer").setLevel(logging.CRITICAL) + +# The annotation app requires a bit of set up. +configuration = pre_serve.load_configuration(CONFIGURATION_FILE) + +app = FastAPI() + + +def get_user_from_header(user_email: Optional[str]) -> Optional[str]: + """ + Call this function with the X-Auth-Request-Email header value. This must + include an "@" in its value. + + * In production, this is provided by Skiff after the user authenticates. + * In development, it is provided in the NGINX proxy configuration file local.conf. + + If the value isn't well formed, or the user isn't allowed, an exception is + thrown. + """ + if "@" not in user_email: + raise HTTPException(403, "Forbidden") + + if not user_is_allowed(user_email): + raise HTTPException(403, "Forbidden") + + return user_email + + +def user_is_allowed(user_email: str) -> bool: + """ + Return True if the user_email is in the users file, False otherwise. + """ + try: + with open(configuration.users_file) as file: + for line in file: + entry = line.strip() + if user_email == entry: + return True + # entries like "@allenai.org" mean anyone in that domain @allenai.org is granted access + if entry.startswith("@") and user_email.endswith(entry): + return True + except FileNotFoundError: + logger.warning("file not found: %s", configuration.users_file) + pass + + return False + + +def all_pdf_shas() -> List[str]: + pdfs = glob.glob(f"{configuration.output_directory}/*/*.pdf") + return [p.split("/")[-2] for p in pdfs] + + +def update_status_json(status_path: str, sha: str, data: Dict[str, Any]): + + with open(status_path, "r+") as st: + status_json = json.load(st) + status_json[sha] = {**status_json[sha], **data} + st.seek(0) + json.dump(status_json, st) + st.truncate() + + +@app.get("/", status_code=204) +def read_root(): + """ + Skiff's sonar, and the Kubernetes health check, require + that the server returns a 2XX response from it's + root URL, so it can tell the service is ready for requests. + """ + return Response(status_code=204) + + +@app.get("/api/doc/{sha}/pdf") +async def get_pdf(sha: str): + """ + Fetches a PDF. + + sha: str + The sha of the pdf to return. + """ + pdf = os.path.join(configuration.output_directory, sha, f"{sha}.pdf") + pdf_exists = os.path.exists(pdf) + if not pdf_exists: + raise HTTPException(status_code=404, detail=f"pdf {sha} not found.") + + return FileResponse(pdf, media_type="application/pdf") + + +@app.get("/api/doc/{sha}/title") +async def get_pdf_title(sha: str) -> Optional[str]: + """ + Fetches a PDF's title. + + sha: str + The sha of the pdf title to return. + """ + pdf_info = os.path.join(configuration.output_directory, "pdf_metadata.json") + + with open(pdf_info, "r") as f: + info = json.load(f) + + data = info.get("sha", None) + + if data is None: + return None + + return data.get("title", None) + + +@app.post("/api/doc/{sha}/comments") +def set_pdf_comments( + sha: str, comments: str = Body(...), x_auth_request_email: str = Header(None) +): + user = get_user_from_header(x_auth_request_email) + status_path = os.path.join(configuration.output_directory, "status", f"{user}.json") + exists = os.path.exists(status_path) + + if not exists: + # Not an allocated user. Do nothing. + return {} + + update_status_json(status_path, sha, {"comments": comments}) + return {} + + +@app.post("/api/doc/{sha}/junk") +def set_pdf_junk( + sha: str, junk: bool = Body(...), x_auth_request_email: str = Header(None) +): + user = get_user_from_header(x_auth_request_email) + status_path = os.path.join(configuration.output_directory, "status", f"{user}.json") + exists = os.path.exists(status_path) + if not exists: + # Not an allocated user. Do nothing. + return {} + + update_status_json(status_path, sha, {"junk": junk}) + return {} + + +@app.post("/api/doc/{sha}/finished") +def set_pdf_finished( + sha: str, finished: bool = Body(...), x_auth_request_email: str = Header(None) +): + user = get_user_from_header(x_auth_request_email) + status_path = os.path.join(configuration.output_directory, "status", f"{user}.json") + exists = os.path.exists(status_path) + if not exists: + # Not an allocated user. Do nothing. + return {} + + update_status_json(status_path, sha, {"finished": finished}) + return {} + + +@app.get("/api/doc/{sha}/annotations") +def get_annotations( + sha: str, x_auth_request_email: str = Header(None) +) -> PdfAnnotation: + user = get_user_from_header(x_auth_request_email) + annotations = os.path.join( + configuration.output_directory, sha, f"{user}_annotations.json" + ) + exists = os.path.exists(annotations) + + if exists: + with open(annotations) as f: + blob = json.load(f) + + return blob + + else: + return {"annotations": [], "relations": []} + + +@app.post("/api/doc/{sha}/annotations") +def save_annotations( + sha: str, + annotations: List[Annotation], + relations: List[RelationGroup], + x_auth_request_email: str = Header(None), +): + """ + sha: str + PDF sha to save annotations for. + annotations: List[Annotation] + A json blob of the annotations to save. + relations: List[RelationGroup] + A json blob of the relations between the annotations to save. + x_auth_request_email: str + This is a header sent with the requests which specifies the user login. + For local development, this will be None, because the authentication + is controlled by the Skiff Kubernetes cluster. + """ + # Update the annotations in the annotation json file. + user = get_user_from_header(x_auth_request_email) + annotations_path = os.path.join( + configuration.output_directory, sha, f"{user}_annotations.json" + ) + json_annotations = [jsonable_encoder(a) for a in annotations] + json_relations = [jsonable_encoder(r) for r in relations] + + # Update the annotation counts in the status file. + status_path = os.path.join(configuration.output_directory, "status", f"{user}.json") + exists = os.path.exists(status_path) + if not exists: + # Not an allocated user. Do nothing. + return {} + + with open(annotations_path, "w+") as f: + json.dump({"annotations": json_annotations, "relations": json_relations}, f) + + update_status_json( + status_path, sha, {"annotations": len(annotations), "relations": len(relations)} + ) + + return {} + + +@app.get("/api/doc/{sha}/tokens") +def get_tokens(sha: str): + """ + sha: str + PDF sha to retrieve tokens for. + """ + pdf_tokens = os.path.join(configuration.output_directory, sha, "pdf_structure.json") + if not os.path.exists(pdf_tokens): + raise HTTPException(status_code=404, detail="No tokens for pdf.") + with open(pdf_tokens, "r") as f: + response = json.load(f) + + return response + + +@app.get("/api/annotation/labels") +def get_labels() -> List[Dict[str, str]]: + """ + Get the labels used for annotation for this app. + """ + return configuration.labels + + +@app.get("/api/annotation/relations") +def get_relations() -> List[Dict[str, str]]: + """ + Get the relations used for annotation for this app. + """ + return configuration.relations + + +@app.get("/api/annotation/allocation/info") +def get_allocation_info(x_auth_request_email: str = Header(None)) -> Allocation: + + # In development, the app isn't passed the x_auth_request_email header, + # meaning this would always fail. Instead, to smooth local development, + # we always return all pdfs, essentially short-circuiting the allocation + # mechanism. + user = get_user_from_header(x_auth_request_email) + + status_dir = os.path.join(configuration.output_directory, "status") + status_path = os.path.join(status_dir, f"{user}.json") + exists = os.path.exists(status_path) + + if not exists: + # If the user doesn't have allocated papers, they can see all the + # pdfs but they can't save anything. + papers = [PaperStatus.empty(sha, sha) for sha in all_pdf_shas()] + response = Allocation( + papers=papers, + hasAllocatedPapers=False + ) + + else: + with open(status_path) as f: + status_json = json.load(f) + + papers = [] + for sha, status in status_json.items(): + papers.append(PaperStatus(**status)) + + response = Allocation(papers=papers, hasAllocatedPapers=True) + + return response diff --git a/api/pytest.ini b/api/pytest.ini new file mode 100644 index 00000000..9e65da02 --- /dev/null +++ b/api/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +env = + D:PAWLS_CONFIGURATION_FILE=test/fixtures/config/configuration.json + D:PAWLS_ANNOTATORS_FILE=test/fixtures/config/annotators.json \ No newline at end of file diff --git a/api/requirements.txt b/api/requirements.txt new file mode 100644 index 00000000..19f8fc28 --- /dev/null +++ b/api/requirements.txt @@ -0,0 +1,21 @@ + +# Skiff logging +python-json-logger==0.1.10 + +# Server +fastapi +uvicorn + +# used for testing/formatting +pytest +pytest-env +flake8 +black==20.8b1 +requests + +# used for form parsing +python-multipart + +# Allows us to use FastAPI's FileResponse for serving pdfs. +aiofiles + diff --git a/api/test/__init__.py b/api/test/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/api/test/fixtures/config/configuration.json b/api/test/fixtures/config/configuration.json new file mode 100644 index 00000000..f919909f --- /dev/null +++ b/api/test/fixtures/config/configuration.json @@ -0,0 +1,24 @@ +{ + "output_directory": 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z)Kry_go#Uw!&D?yRMb^9)MQkkQtBG&Qi{NThv4Jy{_crjvb>APJAF10DqMO~*H)jlWVyTx zRhbhax$a)ep-guF{q^@W+9PzI^m!71}%Sm^p>nfwvMbboWQ{`*> zMMk~0bRXTByw7elzA~(e@jXyyR9}fAqXeeMiK?$CpSDuHP4UoCRNE;(L6Di3M~Z$S zk_F8+crh(ngvXltPSgUBvHX;~BcC=!nR2}Dn!{yKgBC|POwhcf6J~W*GP=T1PEK5f zsgq{XEsrlHpYMv2o9X$dtUH^c9=+AxaYC6K{GzUM{iU^<#)Z#((xQYltuxKRy_;v$ z=Vx`(Yu^k*j1CkX3gvRCzBhzP*iOAiKI1rz63Q{p^E|ctwTj`3vW)bpec!3?&$u?g zlZvk@{X?UE3|Jc{%9x_&LzbtPg*`3n-_;cYJMiyFHmmQ1s@Tk86!J6|%;`Vd^Gr@~ zPiBj{_>iOsNv&mjksAp0FF}kP9{?6zr@eLsr7s{$x_I(ZB!W_(FtTetv9My9kG#&l zA;`Yr(-VM=bQan^$A2>>Ak*P}7MF84eUDP8cis8>##SLtTNea|$pTH_vTssZ174<- zX|`9e$z#U~ar;xeH>W39urb4V6g$zd84+oU?;{I_@eLn;fb;4E^1MgaRw5cr?+ot*q~m8d894yvG2gmnqkIH8(!A5!wZt$BmQD(gm z!eznfHh@3PmbXknE)QH6g9*k{ sxuU(uA^whhcIHVWb!GcM*Cz&xKx2dO7dim^0+ND50DOEJMw)>C07M0<8vp markn@gmail.com` + + To assign all current pdfs in the project to an annotator, use: + + `pawls assign --all` + """ + shas = set(shas) + + pdfs = glob.glob(os.path.join(path, "*/*.pdf")) + project_shas = {p.split("/")[-2] for p in pdfs} + if all: + # If --all flag, we use all pdfs in the current project. + shas.update(project_shas) + + if sha_file is not None: + extra_ids = [x.strip("\n") for x in open(sha_file, "r")] + shas.extend(extra_ids) + + diff = shas.difference(project_shas) + if diff: + error = f"Found shas which are not present in path {path} .\n" + error = ( + error + + f"Add pdf files in the specified directory, one per sub-directory." + ) + for sha in diff: + error = error + f"{sha}\n" + raise UsageError(error) + + if all: + # If --all flag, we use all pdfs in the current project. + shas.update(project_shas) + + if sha_file is not None: + extra_ids = [x.strip("\n") for x in open(sha_file, "r")] + shas.extend(extra_ids) + + result = re.match(r"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)", annotator) + + if not result or result.group(0) != annotator: + raise BadArgumentUsage("Provided annotator was not a valid email.") + + status_dir = os.path.join(path, "status") + os.makedirs(status_dir, exist_ok=True) + + status_path = os.path.join(status_dir, f"{annotator}.json") + + pdf_status = {} + if os.path.exists(status_path): + pdf_status = json.load(open(status_path)) + + name_mapping = {} + if name_file is not None: + name_mapping = json.load(open(name_file)) + else: + print("Warning: --name-file was not provided, using shas as pdf names.") + + for sha in shas: + if sha in pdf_status: + continue + else: + + name = name_mapping.get(sha, None) + if name is None: + name = sha + + pdf_status[sha] = { + "sha": sha, + "name": name, + "annotations": 0, + "relations": 0, + "finished": False, + "junk": False, + "comments": "", + "completedAt": None, + } + + with open(status_path, "w+") as out: + json.dump(pdf_status, out) diff --git a/cli/pawls/commands/export.py b/cli/pawls/commands/export.py new file mode 100644 index 00000000..b5299706 --- /dev/null +++ b/cli/pawls/commands/export.py @@ -0,0 +1,394 @@ +import os +import json +from typing import List, NamedTuple, Union, Dict, Any + +import click +import pandas as pd +from tqdm import tqdm +from pdf2image import convert_from_path + +from pawls.commands.utils import ( + load_json, + get_pdf_sha, + get_pdf_pages_and_sizes, + LabelingConfiguration, + AnnotationFolder, + AnnotationFiles, +) + +ALL_SUPPORTED_EXPORT_TYPE = ["coco", "token"] + + +def _convert_bounds_to_coco_bbox(bounds: Dict[str, Union[int, float]]): + x1, y1, x2, y2 = bounds["left"], bounds["top"], bounds["right"], bounds["bottom"] + return x1, y1, x2 - x1, y2 - y1 + + +class COCOBuilder: + class CategoryTemplate(NamedTuple): + id: int + name: str + supercategory: str = None + + class PaperTemplate(NamedTuple): + id: int + paper_sha: str + pages: int + + class ImageTemplate(NamedTuple): + id: int + file_name: str + height: Union[float, int] + width: Union[float, int] + paper_id: int + page_number: int + + class AnnoTemplate(NamedTuple): + id: int + bbox: List + image_id: int + category_id: int + area: Union[float, int] + iscrowd: bool = False + + def __init__(self, categories: List, save_path: str): + """COCOBuilder generates the coco-format dataset based on + source annotation files. + + It will create a COCO-format annotation json file for every + annotated page and convert all the labeled pdf pages into + images, which is stored in `/images/_.jpg`. + + Args: + categories (List): + All the labeling categories in the dataset + save_path (str): + The folder for saving all the annotation files. + + Examples:: + >>> anno_files = AnnotationFiles(**configs) # Initialize anno_files based on configs + >>> coco_builder = COCOBuilder(["title", "abstract"], "export_path") + >>> coco_builder.build_annotations(anno_files) + >>> coco_builder.export() + """ + # Create Paths + self.save_path = save_path + self.save_path_image = f"{self.save_path}/images" + os.makedirs(self.save_path, exist_ok=True) + os.makedirs(self.save_path_image, exist_ok=True) + + # Internal COCO information storage + self._categories = self._create_coco_categories(categories) + self._name2catid = {ele["name"]: ele["id"] for ele in self._categories} + self._images = [] + self._papers = [] + + def get_image_data(self, paper_sha: str, page_id: int): + """Find the image data with the given paper_sha and page_id.""" + filename = self._create_pdf_page_image_filename(paper_sha, page_id) + for data in self._images: + if data["file_name"] == filename: + return data + + def _create_pdf_page_image_filename(self, paper_sha: str, page_id: int) -> str: + return f"{paper_sha}_{page_id}.jpg" + + def _create_coco_categories(self, categories: List) -> List[str]: + return [ + self.CategoryTemplate(idx, category)._asdict() + for idx, category in enumerate(categories) + ] + + def create_paper_data( + self, annotation_folder: AnnotationFolder, save_images: bool = True + ): + + _papers = [] + _images = [] + pbar = tqdm(annotation_folder.all_pdf_paths) + for pdf_path in pbar: + paper_sha = get_pdf_sha(pdf_path) + pbar.set_description(f"Working on {paper_sha[:10]}...") + + num_pages, page_sizes = get_pdf_pages_and_sizes(pdf_path) + pdf_page_images = convert_from_path(pdf_path) + + # Add paper information + paper_id = len(_papers) # Start from zero + paper_info = self.PaperTemplate( + paper_id, + paper_sha, + pages=num_pages, + ) + + current_images = [] + previous_image_id = len(_images) # Start from zero + + for page_id, page_size in enumerate(page_sizes): + image_filename = self._create_pdf_page_image_filename( + paper_sha, page_id + ) + width, height = page_size + current_images.append( + self.ImageTemplate( + id=previous_image_id + len(current_images), + file_name=image_filename, + height=height, + width=width, + paper_id=paper_id, + page_number=page_id, + )._asdict() + ) + if save_images and not os.path.exists( + f"{self.save_path_image}/{image_filename}" + ): + pdf_page_images[page_id].resize((width, height)).save( + f"{self.save_path_image}/{image_filename}" + ) + + _papers.append(paper_info._asdict()) + _images.extend(current_images) + + self._papers = _papers + self._images = _images + + def create_annotation_for_annotator(self, anno_files: AnnotationFiles) -> None: + """Create the annotations for the given annotation files""" + + _annotations = [] + anno_id = 0 + pbar = tqdm(anno_files) + for anno_file in pbar: + + paper_sha = anno_file["paper_sha"] + + pbar.set_description(f"Working on {paper_sha[:10]}...") + pawls_annotations = load_json(anno_file["annotation_path"])["annotations"] + + for anno in pawls_annotations: + page_id = anno["page"] + category = anno["label"]["text"] + + # Skip if current category is not in the specified categories + cat_id = self._name2catid.get(category, None) + if cat_id is None: + continue + + image_data = self.get_image_data(paper_sha, page_id) + width, height = image_data["width"], image_data["height"] + + x, y, w, h = _convert_bounds_to_coco_bbox(anno["bounds"]) + + _annotations.append( + self.AnnoTemplate( + id=anno_id, + bbox=[x, y, w, h], + category_id=cat_id, + image_id=image_data["id"], + area=w * h, + )._asdict() + ) + anno_id += 1 + + return _annotations + + def create_combined_json_for_annotations( + self, annotations: List[Dict] + ) -> Dict[str, Any]: + return { + "papers": self._papers, + "images": self._images, + "annotations": annotations, + "categories": self._categories, + } + + def build_annotations(self, anno_files: AnnotationFiles) -> None: + + annotations = self.create_annotation_for_annotator(anno_files) + coco_json = self.create_combined_json_for_annotations(annotations) + self.export(coco_json, f"{anno_files.annotator}.json") + + def export(self, coco_json: Dict, annotation_name="annotations.json") -> None: + + with open(f"{self.save_path}/{annotation_name}", "w") as fp: + + json.dump(coco_json, fp) + + +class TokenTableBuilder: + def __init__(self, categories, save_path: str): + + self.categories = categories + self.save_path = save_path + + def create_paper_data(self, annotation_folder: AnnotationFolder): + + all_page_token_data = {} + for pdf in annotation_folder.all_pdfs: + all_page_tokens = annotation_folder.get_pdf_tokens(pdf) + + # Get page token data + page_token_data = [] + for page_tokens in all_page_tokens: + token_data = [ + (page_tokens.page.index, idx, token.text) + for idx, token in enumerate(page_tokens.tokens) + ] + df = pd.DataFrame(token_data, columns=["page_index", "index", "text"]) + df = df.set_index(["page_index", "index"]) + page_token_data.append(df) + page_token_data = pd.concat(page_token_data) + + all_page_token_data[get_pdf_sha(pdf)] = page_token_data + + self.all_page_token_data = all_page_token_data + + def create_annotation_for_annotator(self, anno_files: AnnotationFiles) -> None: + + # Firstly initialize the annotation tables with the annotator name + annotator = anno_files.annotator + for token_data in self.all_page_token_data.values(): + token_data[annotator] = None + + pbar = tqdm(anno_files) + + for anno_file in pbar: + paper_sha = anno_file["paper_sha"] + df = self.all_page_token_data[paper_sha] + + pawls_annotations = load_json(anno_file["annotation_path"])["annotations"] + for anno in pawls_annotations: + if anno["tokens"] is None: + continue + + # Skip if current category is not in the specified categories + label = anno["label"]["text"] + if label not in self.categories: + continue + + anno_token_indices = [ + (ele["pageIndex"], ele["tokenIndex"]) for ele in anno["tokens"] + ] + + df.loc[anno_token_indices, annotator] = label + + def export(self): + + for pdf, df in self.all_page_token_data.items(): + df["pdf"] = pdf + + df = ( + pd.concat(self.all_page_token_data.values()) + .reset_index() + .set_index(["pdf", "page_index", "index"]) + ) + + df.to_csv(self.save_path) + return df + + +@click.command(context_settings={"help_option_names": ["--help", "-h"]}) +@click.argument("path", type=click.Path(exists=True, file_okay=False)) +@click.argument("config", type=str) +@click.argument("output", type=click.Path(file_okay=True)) +@click.argument("format", type=click.Path(file_okay=False)) +@click.option( + "--annotator", + "-u", + multiple=True, + help="Export annotations of the specified annotator.", +) +@click.option( + "--categories", + "-c", + multiple=True, + help="Export specified categories in the annotations.", +) +@click.option( + "--include-unfinished", + "-i", + is_flag=True, + help="A flag to export all annotation by the specified annotator including unfinished ones.", +) +@click.option( + "--export-images/--no-export-images", + default=True, + help="A flag to not to export images of PDFs", +) +def export( + path: click.Path, + config: click.File, + output: click.Path, + format: str, + annotator: List, + categories: List, + include_unfinished: bool = False, + export_images: bool = True, +): + """ + Export the COCO annotations for an annotation project. + + To export all annotations of a project of all annotators, use: + `pawls export ` + + To export only finished annotations of from specified annotators, e.g. markn and shannons, use: + `pawls export -u markn -u shannons`. + + To export all annotations of from a given annotator, use: + `pawls export -u markn --include-unfinished`. + """ + + assert ( + format in ALL_SUPPORTED_EXPORT_TYPE + ), f"Invalid export format {format}. Should be one of {ALL_SUPPORTED_EXPORT_TYPE}." + print(f"Export the annotations to the {format} format.") + + config = LabelingConfiguration(config) + annotation_folder = AnnotationFolder(path) + + if len(annotator) == 0: + all_annotators = annotation_folder.all_annotators + print(f"Export annotations from all available annotators {all_annotators}") + else: + all_annotators = annotator + + if len(categories) == 0: + categories = config.categories + print(f"Export annotations from all available categories {categories}") + + if format == "coco": + + coco_builder = COCOBuilder(categories, output) + print(f"Creating paper data for annotation folder {annotation_folder.path}") + coco_builder.create_paper_data(annotation_folder, save_images=export_images) + + for annotator in all_annotators: + print(f"Export annotations from annotators {annotator}") + + anno_files = AnnotationFiles(path, annotator, include_unfinished) + + coco_builder.build_annotations(anno_files) + + print( + f"Successfully exported {len(anno_files)} annotations of annotator {annotator} to {output}." + ) + + elif format == "token": + + if not output.endswith(".csv"): + output = f"{output}.csv" + token_builder = TokenTableBuilder(categories, output) + + print(f"Creating paper data for annotation folder {annotation_folder.path}") + token_builder.create_paper_data(annotation_folder) + + for annotator in all_annotators: + + # print(f"Export annotations from annotators {annotator}") + anno_files = AnnotationFiles(path, annotator, include_unfinished) + token_builder.create_annotation_for_annotator(anno_files) + + df = token_builder.export() + print( + f"Successfully exported annotations for {len(df)} tokens from annotators {all_annotators} to {output}." + ) \ No newline at end of file diff --git a/cli/pawls/commands/metric.py b/cli/pawls/commands/metric.py new file mode 100644 index 00000000..c0d6a2dc --- /dev/null +++ b/cli/pawls/commands/metric.py @@ -0,0 +1,365 @@ +import os +import sys +import tempfile +from glob import glob +from copy import deepcopy +from collections import defaultdict +from typing import List, NamedTuple, Union, Dict, Any, Set, Tuple + +import click +import pandas as pd +import numpy as np +from tabulate import tabulate +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + +from pawls.commands.export import export + + +def get_unique_image_ids(coco: COCO) -> Set[int]: + return set([anno["image_id"] for anno in coco.dataset["annotations"]]) + + +def get_mutually_annotated_image_ids(coco1: COCO, coco2: COCO) -> Set[int]: + return get_unique_image_ids(coco1).intersection(get_unique_image_ids(coco2)) + + +def filter_annotation_with_image_ids(coco: COCO, image_ids: Set[int]) -> COCO: + coco = deepcopy(coco) + coco.dataset["annotations"] = [ + anno for anno in coco.dataset["annotations"] if anno["image_id"] in image_ids + ] + coco.dataset["images"] = [ + image for image in coco.dataset["images"] if image["id"] in image_ids + ] + return coco + + +def print_results(calculation_method_msg, df: pd.DataFrame): + + print(calculation_method_msg) + print("-" * 45) + print( + "The (i,j)-th element in the table is calculated by treating the annotations from\n" + "i as the 'ground-truth's, and those from j are considered as 'predictions'." + ) + print( + tabulate( + df[sorted(df.columns)].loc[sorted(df.columns)], + headers="keys", + tablefmt="psql", + ) + ) + print("\n") + + +class PythonLiteralOption(click.Option): + """Used for parsing list-like stings from the input. + + A technique adapted from https://stackoverflow.com/a/47730333 + """ + + def type_cast_value(self, ctx, value): + try: + return [ele.strip() for ele in value.split(",")] + except: + raise click.BadParameter(value) + + +class HiddenPrints: + """Used for hiding unnecessary prints of COCO Eval + A trick learned from https://stackoverflow.com/a/45669280 + """ + + def __enter__(self): + self._original_stdout = sys.stdout + sys.stdout = open(os.devnull, "w") + + def __exit__(self, exc_type, exc_val, exc_tb): + sys.stdout.close() + sys.stdout = self._original_stdout + + +class COCOEvaluator: + + COCO_METRICS = ["AP", "AP50", "AP75", "APs", "APm", "APl"] + + def __init__(self, coco_save_path: str, class_names: List[str] = None): + + all_cocos = {} + annotators = [] + for userfile in sorted(glob(f"{coco_save_path}/*.json")): + with HiddenPrints(): + username = userfile.split("/")[-1].replace(".json", "") + annotators.append(username) + all_cocos[username] = COCO(userfile) + + # A hack to make use of the current COCOEval API + for coco in all_cocos.values(): + for ele in coco.dataset["annotations"]: + ele["score"] = 1 + + self.all_cocos = all_cocos + self.annotators = annotators + + self.class_names = class_names or [ + val["name"] for _, val in all_cocos[annotators[0]].cats.items() + ] + + def calculate_scores_for_two_cocos( + self, coco1: COCO, coco2: COCO, class_names: List[str] + ) -> Tuple[Dict, Dict]: + + coco_eval = COCOeval(coco1, coco2, iouType="bbox") + with HiddenPrints(): + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + results = { + metric: float( + coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan" + ) + for idx, metric in enumerate(self.COCO_METRICS) + } + precisions = coco_eval.eval["precision"] + + results_per_category = [] + for idx, name in enumerate(class_names): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + ap = np.mean(precision) if precision.size else float("nan") + results_per_category.append(("{}".format(name), float(ap * 100))) + + results_per_category = {name: ap for name, ap in results_per_category} + return results, results_per_category + + def calculate_ap_scores(self) -> Tuple[Dict, Dict]: + + coco_results = defaultdict(dict) + coco_category_results = defaultdict(dict) + + for name1, coco1 in self.all_cocos.items(): + for name2, coco2 in self.all_cocos.items(): + + image_ids = get_mutually_annotated_image_ids(coco1, coco2) + coco1 = filter_annotation_with_image_ids(coco1, image_ids) + coco2 = filter_annotation_with_image_ids(coco2, image_ids) + + results, results_per_category = self.calculate_scores_for_two_cocos( + coco1, coco2, self.class_names + ) + coco_results[name1][name2] = results + coco_category_results[name1][name2] = results_per_category + + return coco_results, coco_category_results + + def show_results(self, results: Dict, metric_names: List[str] = None): + """Show COCO Eval results for the given metric names. + + Args: + results (Dict): + The coco_results dict generated by `COCOEvaluator.calculate_ap_scores`. + + metric_names (List[str], optional): + Metric report of the specified `metric_names` will be displayed. + If not set, all metrics in `COCOEvaluator.COCO_METRICS` will be displayed. + """ + if metric_names is None: + metric_names = self.COCO_METRICS + + for metric_name in metric_names: + df = pd.DataFrame(results).applymap( + lambda ele: ele.get(metric_name) if not pd.isna(ele) else ele + ) + print_results( + f"Inter-annotator agreement based on {metric_name} scores.", df + ) + + def show_category_results(self, results: Dict, class_names: List[str] = None): + """Show COCO Eval results for the given class_names. + + Args: + results (Dict): + The coco_category_results dict generated by `COCOEvaluator.calculate_ap_scores`. + + class_names (List[str], optional): + Metric report of the specified `class_names` will be displayed. + If not set, all classes in `self.class_names` will be displayed. + """ + if class_names is None: + class_names = self.class_names + + for class_name in class_names: + df = pd.DataFrame(results).applymap( + lambda ele: ele.get(class_name) if not pd.isna(ele) else ele + ) + + print_results( + f"Inter-annotator agreement of the {class_name} class based on AP scores.", + df, + ) + + +class TokenEvaluator: + + PDF_FEATURES_IN_SAVED_TABLES = ["pdf", "page_index", "index", "text"] + + def __init__(self, token_save_path: str): + + self.df = pd.read_csv(token_save_path) + self.annotators = list( + self.df.columns[len(self.PDF_FEATURES_IN_SAVED_TABLES) :] + ) + # Assuming all users are stored in email address + + def calculate_scores_for_two_annotators( + self, annotator_gt: str, annotator_pred: str + ): + + cur_df = self.df[[annotator_gt, annotator_pred]].fillna(-1) + acc = (cur_df[annotator_gt] == cur_df[annotator_pred]).mean() + + return acc + + def calculate_token_accuracy(self): + table = defaultdict(dict) + + for i, annotator_gt in enumerate(self.annotators): + for j, annotator_pred in enumerate(self.annotators): + + if j <= i: # Skip the diagonal + continue + + # The token_acc table is symmetric + table[annotator_pred][annotator_gt] = table[annotator_gt][ + annotator_pred + ] = self.calculate_scores_for_two_annotators( + annotator_gt, annotator_pred + ) + + return table + + @staticmethod + def show_results(results: Dict): + + df = pd.DataFrame(results) + + calculation_method_msg = ( + "The token accuracy is calculated by simply comparing" + "the compatibility of tokens labels agasin two annotators." + ) + + print_results(calculation_method_msg, df) + + +@click.command(context_settings={"help_option_names": ["--help", "-h"]}) +@click.argument("path", type=click.Path(exists=True, file_okay=False)) +@click.argument("config", type=str) +@click.option( + "--annotator", + "-u", + multiple=True, + help="Export annotations of the specified annotator.", +) +@click.option( + "--textual-categories", + cls=PythonLiteralOption, + help="The annotations of textual categories will be evaluated based on token accuracy.", +) +@click.option( + "--non-textual-categories", + cls=PythonLiteralOption, + help="The annotations of non-textual categories will be evaluated based on AP scores based on box overlapping.", +) +@click.option( + "--include-unfinished", + "-i", + is_flag=True, + help="A flag to export all annotation by the specified annotator including unfinished ones.", +) +@click.option( + "--verbose", + "-v", + is_flag=True, + help="A flag to show detailed reports.", +) +@click.pass_context +def metric( + ctx, + path: click.Path, + config: str, + annotator: List, + textual_categories: List, + non_textual_categories: List, + include_unfinished: bool = False, + verbose: bool = False, +): + """Calculate the inter-annotator agreement for the annotation project for both textual-categories + + Usage: + + Run evaluation for textual-categories cat1 and cat2, and for non-textual-categories cat3 and cat4: + + pawls metric --textual-categories cat1,cat2 --non-textual-categories cat3,cat4 + + + Specifying annotators for evaluation, and include any unfinished annotations: + + pawls metric -u annotator1 -u annotator2 --textual-categories cat1,cat2 --non-textual-categories cat3,cat4 --include-unfinished + + Generating detailed reports: + + pawls metric --textual-categories cat1,cat2 --non-textual-categories cat3,cat4 --verbose + + """ + + invoke_export = lambda tempdir, format, categories: ctx.invoke( + export, + path=path, + config=config, + output=tempdir, + format=format, + annotator=annotator, + categories=categories, + include_unfinished=include_unfinished, + export_images=False, + ) + + if len(non_textual_categories) > 0: + print( + f"Generating Accuracy report for non-textual categories {non_textual_categories}" + ) + + with tempfile.TemporaryDirectory() as tempdir: + + invoke_export( + tempdir=tempdir, format="coco", categories=non_textual_categories + ) + + coco_eval = COCOEvaluator(tempdir) + coco_results, coco_category_results = coco_eval.calculate_ap_scores() + + if verbose: + coco_eval.show_results(coco_results) + coco_eval.show_category_results(coco_category_results) + else: + coco_eval.show_results(coco_results, ["AP"]) + + if len(textual_categories) > 0: + print(f"Generating Accuracy report for textual categories {textual_categories}") + + with tempfile.TemporaryDirectory() as tempdir: + + tempdir = os.path.join(tempdir, "annotation.csv") + + invoke_export( + tempdir=tempdir, format="token", categories=textual_categories + ) + + token_eval = TokenEvaluator(tempdir) + token_results = token_eval.calculate_token_accuracy() + + token_eval.show_results(token_results) \ No newline at end of file diff --git a/cli/pawls/commands/preannotate.py b/cli/pawls/commands/preannotate.py new file mode 100644 index 00000000..4bb9d95d --- /dev/null +++ b/cli/pawls/commands/preannotate.py @@ -0,0 +1,233 @@ +import os +import json +import logging +from glob import glob +from typing import List, NamedTuple, Union, Dict, Iterable, Any, Optional + +import click +from tqdm import tqdm + +from pawls.commands.utils import LabelingConfiguration, load_json, AnnotationFolder +from pawls.preprocessors.model import Page, Block, PageInfo, union_boxes + +logger = logging.getLogger(__name__) + +PADDING_FOR_SEARCHING_TOKEN_INSIDE_BOX = {"top": 3, "left": 3, "bottom": 3, "right": 3} +PADDING_FOR_RECTIFYING_BLOCK_BOX = {"top": 2, "left": 2, "bottom": 2, "right": 2} + + +class ModelPredictions: + """A class for loading model predictions. + + It supports loading predictions directly from a JSON file with the following format: + + {".pdf": + [ + { + "page": {"height": xx, "width": xx, "index": 0}, + "blocks": [ + [x, y, w, h, "category"] + ] + }, + .... + ], + } + + Or you could load predictions from a folder of json files, each with the name of + .json. And the json format is: + + [ + { + "page": {"height": xx, "width": xx, "index": 0}, + "blocks": [ + [x, y, w, h, "category"] + ] + }, + .... + ] + + """ + + def __init__(self, pred_file: str): + + if os.path.isfile(pred_file): + self.pdf_preds = load_json(pred_file) + elif os.path.isdir(pred_file): + self.pdf_preds = self.load_directory(pred_file) + + def load_directory(self, pred_dir: str) -> Dict[str, Dict]: + + pdf_preds = {} + + for pred_json in glob(f"{pred_dir}/*.json"): + + filename = os.path.basename(pred_json).replace(".json", ".pdf") + self.pdf_preds[filename] = load_json(pred_json) + + return pdf_preds + + @property + def all_pdfs(self) -> List[str]: + """Obtain all pdfs in the ModelPredictions""" + return list(self.pdf_preds.keys()) + + def get_pdf_annotations_per_page(self, pdf_name: str) -> Iterable[Page]: + """For a given pdf_name, return a iterator yielding block annotations for each page.""" + + pdf_pred = self.pdf_preds[pdf_name] + + for page_pred in pdf_pred: + + # Create a pseudo "page", where tokens are actually blocks. + yield Page( + page=PageInfo(**page_pred["page"]), + tokens=self.load_page_blocks(page_pred["blocks"]), + ) + + @staticmethod + def load_page_blocks(boxes: List[Any]) -> List[Block]: + return [ + Block(x=x, y=y, width=w, height=h, label=label) + for (x, y, w, h, label) in boxes + ] + + +def find_token_data(all_token_data: List[Page], index: int) -> Optional[Page]: + """Find the token_data with the given page index. + + Args: + all_token_data (List[Page]): + A list of Page, contating the token data. + index (int): + The index of the target page. + + Returns: + Optional[Page]: + Return the Page with the designated index when found. + Otherwise return None. + """ + for token_data in all_token_data: + if token_data.page.index == index: + return token_data + + return None + + +@click.command(context_settings={"help_option_names": ["--help", "-h"]}) +@click.argument("path", type=click.Path(exists=True, file_okay=False)) +@click.argument("config", type=click.File("r")) +@click.argument("pred_file", type=click.Path(file_okay=True)) +@click.option( + "--annotator", + "-u", + multiple=True, + help="Preannotate for the specified annotator." +) +@click.option( + "--all", + "-a", + is_flag=True, + type=bool, + default=False, + help="Whether to preannotate for all annotators." +) +def preannotate( + path: click.Path, config: click.File, pred_file: click.Path, annotator: List, + all: bool=False +): + """ + Preannotate the PDFs with model prediction results. + + Firstly, you need to generate the region bounding box predictions using some models. + And it should be stored in a format that's compatible with the ModelPredictions format. + + We've provided a exemplar script for generating predictions for PDF files in + https://github.com/allenai/pawls/tree/master/scripts/generate_pdf_layouts.py . + + You also need to preprocess the PDFs in the use the `pawls preprocess` command. + + To prepopulate predictions for PDFs in the for some annotator, you could use: + + pawls preannoate -u markn + + To prepopulate predictions for PDFs all annotators, you can use: + + pawls preannoate --all + """ + + anno_folder = AnnotationFolder(path) + if all: + all_annotators = anno_folder.all_annotators + else: + all_annotators = annotator + for annotator in all_annotators: + assert annotator in anno_folder.all_annotators, f"Invalid Annotator {annotator}" + + model_pred = ModelPredictions(pred_file) + config_labels = LabelingConfiguration(config).get_labels() + + for annotator in all_annotators: + print(f"Adding annotations for the annotator {annotator}") + + pbar = tqdm(model_pred.all_pdfs) + + for pdf_name in pbar: + + pbar.set_description(f"{pdf_name[:10]}...") + + if pdf_name not in anno_folder.all_pdfs: + logger.warning( + f"The {pdf_name} is not in the annotation folder. Skipped" + ) + + source_token_data = anno_folder.get_pdf_tokens(pdf_name) + annotation_file = anno_folder.create_annotation_file(pdf_name, annotator) + + anno_count = 0 + for page_blocks in model_pred.get_pdf_annotations_per_page(pdf_name): + + page_index = page_blocks.page.index + page_tokens = find_token_data(source_token_data, page_index) + + if page_tokens is None: + logger.warning( + f"There's no token data for page {page_index} in {pdf_name}. Skipped" + ) + continue + + page_blocks.scale_like(page_tokens) # Ensure they have the same size + + for block_id, block in enumerate(page_blocks.tokens): + + if block.label not in config_labels: + logger.warning( + f"The {block_id}-th block in page {page_index} of {pdf_name} has labels which are not present in the configuration file. Add all labels produced by your model to the config file to include them. Skipping." + ) + continue + + contained_tokens = page_tokens.filter_tokens_by( + block, soft_margin=PADDING_FOR_SEARCHING_TOKEN_INSIDE_BOX + ) + + token_indices = contained_tokens.keys() + contained_tokens = contained_tokens.values() + + # Rectify the block based on the contained tokens + if len(contained_tokens) >= 1: + rectified_block = union_boxes(contained_tokens) + else: + # Sometimes a valid block does not include any tokens (e.g., figure). + # So we just use the block itself as the rectified_block. + rectified_block = block.copy() + rectified_block.pad(**PADDING_FOR_RECTIFYING_BLOCK_BOX) + + annotation_file.add_annotation( + page_index=page_index, + label=config_labels[block.label], + bounds=rectified_block.as_bounds(), + token_indices=token_indices, + ) + anno_count += 1 + + annotation_file.save() + logger.info(f"Successfully stored {anno_count} annotations for {pdf_name}.") diff --git a/cli/pawls/commands/preprocess.py b/cli/pawls/commands/preprocess.py new file mode 100644 index 00000000..b11b8a5f --- /dev/null +++ b/cli/pawls/commands/preprocess.py @@ -0,0 +1,49 @@ +import os +from pathlib import Path +import json + +from tqdm import tqdm +import click +import glob + +from pawls.preprocessors.grobid import process_grobid +from pawls.preprocessors.pdfplumber import process_pdfplumber + +@click.command(context_settings={"help_option_names": ["--help", "-h"]}) +@click.argument("preprocessor", type=str) +@click.argument("path", type=click.Path(exists=True, file_okay=True, dir_okay=True)) +def preprocess(preprocessor: str, path: click.Path): + """ + Run a pre-processor on a pdf/directory of pawls pdfs and + write the resulting token information to the pdf location. + + Current preprocessor options are: "grobid". + + To send all pawls structured pdfs in the current directory for processing: + + `pawls preprocess grobid ./` + """ + print(f"Processing using the {preprocessor} preprocessor...") + + if os.path.isdir(path): + in_glob = os.path.join(path, "*/*.pdf") + pdfs = glob.glob(in_glob) + else: + if not str(path).endswith(".pdf"): + raise ValueError("Path is not a directory, but also not a pdf.") + pdfs = [str(path)] + + pbar = tqdm(pdfs) + + for p in pbar: + path = Path(p) + sha = path.name.strip(".pdf") + pbar.set_description(f"Processing {sha[:10]}...") + if preprocessor == "grobid": + data = process_grobid(str(path)) + elif preprocessor == "pdfplumber": + data = process_pdfplumber(str(path)) + with open(path.parent / "pdf_structure.json", "w+") as f: + + json.dump(data, f) + diff --git a/cli/pawls/commands/status.py b/cli/pawls/commands/status.py new file mode 100644 index 00000000..40f009ea --- /dev/null +++ b/cli/pawls/commands/status.py @@ -0,0 +1,111 @@ +import os +from glob import glob +import click +from typing import Tuple + +import pandas as pd +from tabulate import tabulate + +from pawls.commands.utils import load_json, get_pdf_pages_and_sizes + + +def get_labeling_status(target_dir: str) -> Tuple[pd.DataFrame, pd.DataFrame]: + + all_json_records = glob(f"{target_dir}/status/*.json") + + all_record = [] + for record in all_json_records: + name = os.path.splitext(os.path.basename(record))[0] + all_annotations = load_json(record) + cur_record = pd.DataFrame(all_annotations).T + cur_record["annotator"] = name + all_record.append(cur_record) + + all_record = pd.concat(all_record) + pdf_pages = { + sha: get_pdf_pages_and_sizes(f"{target_dir}/{sha}/{sha}.pdf")[0] + for sha in set(all_record.index) + } + all_record = all_record.reset_index() + all_record["page_num"] = all_record["index"].map(pdf_pages) + + all_record["junk_or_finished"] = all_record["finished"] | all_record["junk"] + # used for calculating the unfinished tasks + + all_record["valid_page_num"] = all_record.apply( + lambda row: row["page_num"] if row["finished"] else 0, axis=1 + ) + all_record["valid_annotations"] = all_record.apply( + lambda row: row["annotations"] if row["finished"] else 0, axis=1 + ) + + status = all_record.groupby("annotator").agg( + { + "index": ["count"], + "finished": ["sum"], + "valid_annotations": ["sum", "max", "min"], + "junk": ["sum"], + "junk_or_finished": ["sum"], + "valid_page_num": ["sum"], + } + ) + + status.columns = status.columns.map("_".join) + status = status.rename( + columns={ + "index_count": "total_tasks", + "finished_sum": "total_finished", + "junk_sum": "total_junks", + "junk_or_finished_sum": "junk_or_finished", + "valid_page_num_sum": "page_num", + "valid_annotations_sum": "anno_sum", + "valid_annotations_max": "anno_max", + } + ) + + status["total_unfinished"] = status["total_tasks"] - status["junk_or_finished"] + + status["avg_anno_per_page"] = status["anno_sum"] / status["page_num"] + display_status = status[ + [ + "total_tasks", + "total_finished", + "avg_anno_per_page", + "page_num", + "anno_sum", + "anno_max", + ] + ].copy() + + display_status.loc["AGGREGATION", :] = display_status.sum() + display_status.loc["AGGREGATION", "avg_anno_per_page"] = ( + display_status.loc["AGGREGATION", "anno_sum"] + / display_status.loc["AGGREGATION", "page_num"] + ) + + return display_status.fillna(0.0), all_record.rename(columns={"index": "sha"}) + + +@click.command(context_settings={"help_option_names": ["--help", "-h"]}) +@click.argument("path", type=click.Path(exists=True, file_okay=False)) +@click.option("--output", help="Path to save the export data", type=click.Path()) +def status( + path: click.Path, + output: click.Path, +): + """ + Checking the labeling status for some annotation project + + To check the labeling status for some PAWLS annotation folder, use: + `pawls status ` + + To save the labeling record table for some PAWLS annotation folder, use: + `pawls status --output record.csv` + """ + + labeling_status, all_record = get_labeling_status(path) + print(tabulate(labeling_status, headers="keys", tablefmt="psql")) + + if output is not None: + all_record.to_csv(output, index=None) + print(f"Saved the annotation record table to {output}") diff --git a/cli/pawls/commands/utils.py b/cli/pawls/commands/utils.py new file mode 100644 index 00000000..fd2a15fb --- /dev/null +++ b/cli/pawls/commands/utils.py @@ -0,0 +1,252 @@ +import json +from typing import List, Dict, Iterable +from glob import glob +import os +import uuid + +import click +from pdfminer.pdfparser import PDFParser +from pdfminer.pdfdocument import PDFDocument +from pdfminer.pdfpage import PDFPage +from pdfminer.pdfinterp import resolve1 + +from pawls.preprocessors.model import load_tokens_from_file + + +DEVELOPMENT_USER = "development_user@example.com" + + +def load_json(filename: str): + with open(filename, "r") as fp: + return json.load(fp) + + +def get_pdf_pages_and_sizes(filename: str): + """Ref https://stackoverflow.com/a/47686921""" + with open(filename, "rb") as fp: + parser = PDFParser(fp) + document = PDFDocument(parser) + num_pages = resolve1(document.catalog["Pages"])["Count"] + page_sizes = [ + (int(page.mediabox[2]), int(page.mediabox[3])) + for page in PDFPage.create_pages(document) + ] + return num_pages, page_sizes + + +def get_pdf_sha(pdf_file_name: str) -> str: + return os.path.basename(pdf_file_name).replace(".pdf", "") + + +class LabelingConfiguration: + def __init__(self, config: str): + """LabelingConfiguration handles parsing the configuration file. + + Args: + config (str): The config file path. + """ + + self.config = load_json(config) + + @property + def categories(self) -> List[str]: + """Returns all labeling category names in the config file.""" + return [l["text"] for l in self.config["labels"]] + + @property + def relations(self): + raise NotImplementedError + + def get_labels(self) -> Dict[str, Dict]: + """Returns a dictionary for category name, label pairs in the config file.""" + return {l["text"]: l for l in self.config["labels"]} + + +class AnnotationFolder: + DEFAULT_PDF_STRUCTURE_NAME = "pdf_structure.json" + + def __init__(self, path, pdf_structure_name=None): + + self.path = path + self.pdf_structure_name = pdf_structure_name or self.DEFAULT_PDF_STRUCTURE_NAME + + self.all_pdf_paths = [pdf_path for pdf_path in glob(f"{self.path}/*/*.pdf")] + self.all_pdfs = [os.path.basename(pdf_path) for pdf_path in self.all_pdf_paths] + + @property + def all_annotators(self) -> List[str]: + """Fetch all annotators in the labeling folder, + including the default DEVELOPMENT_USER. + """ + + return [DEVELOPMENT_USER] + [ + os.path.splitext(e)[0] for e in os.listdir(f"{self.path}/status") + ] + + def get_pdf_tokens(self, pdf_name: str) -> str: + """Get the pdf tokens for a pdf name by loading from the corresponding pdf_structure file. + + Args: + pdf_name (str): the name of the pdf file, e.g., xxx.pdf + + Raises: + FileNotFoundError: + When the pdf_structure is not found for this pdf_name, raise a FileNotFoundError. + + Returns: + str: the pdf_structure file path for this pdf file. + """ + + sha = get_pdf_sha(pdf_name) + pdf_structure_path = f"{self.path}/{sha}/{self.pdf_structure_name}" + + if os.path.exists(pdf_structure_path): + return load_tokens_from_file(pdf_structure_path) + else: + raise FileNotFoundError( + f"pdf_structure is not found for {sha}.Did you forget run the following command?\n pawls preprocess {self.path}/{sha}/{pdf_name}" + ) + + def create_annotation_file(self, pdf_name: str, annotator: str) -> "AnnotationFile": + """Create an annotation file for the given pdf name and annotator. + + Returns: + AnnotationFile: + An AnnotationFile object that used for creating annotations. + """ + sha = get_pdf_sha(pdf_name) + annotation_file_path = f"{self.path}/{sha}/{annotator}_annotations.json" + + return AnnotationFile(annotation_file_path) + + +class AnnotationFile: + def __init__(self, filepath: str): + """Annotation file is used to help creating the annotation + files manually. + + Args: + filepath (str): the path to store the annotation file. + """ + self.filepath = filepath + self.data = {"annotations": [], "relations": []} + + def add_annotation( + self, + page_index: int, + label: Dict[str, str], + bounds: Dict[str, int], + token_indices: List[int] = [], + ): + """Add an annotation to the given paper. + + Args: + page_index (int): + The page index of this annotation. + label (Dict[str, str]): + The label (text and color) of this annotation. + bounds (Dict[str, int]): + The bounding box coordinates of the block. + token_indices (List[int], optional): + A list of the indices of the contained tokens. + Defaults to []. + """ + annotation = { + "id": str(uuid.uuid4()), + "page": page_index, + "label": label, + "bounds": bounds, + "tokens": [ + {"pageIndex": page_index, "tokenIndex": token_id} + for token_id in token_indices + ], + } + + self.data["annotations"].append(annotation) + + def add_relations(self): + raise NotImplementedError() + + def save(self): + """Save the annotation file in the designated filepath""" + if os.path.exists(self.filepath): + while True: + overwrite = input( + f"Overwrite existing annotations {self.filepath}? [Y/N]\n" + ).lower() + if overwrite in ["y", "n"]: + if overwrite == "n": + return None + break + print("Please enter Y or N.") + + with open(self.filepath, "w") as fp: + json.dump(self.data, fp) + + +class AnnotationFiles: + def __init__( + self, labeling_folder: str, annotator: str, include_unfinished: bool = True + ): + """AnnotationFiles is an iterator for selected annotation files + given the selected annotators and configurations. + + Args: + labeling_folder (str): + The folder to save the pdf annotation files, e.g., + `./skiff_files/apps/pawls/papers`. + annotator (str, optional): + The name of the annotator. + If not set, then changed to the default user + `AnnotationFiles.DEVELOPMENT_USER`. + include_unfinished (bool, optional): + Whether output unfinished annotations of the given user. + Defaults to True. + """ + self.labeling_folder = labeling_folder + + self.annotator = annotator + self.include_unfinished = include_unfinished + + if self.include_unfinished: + self._files = self.get_all_annotation_files() + else: + self._files = self.get_finished_annotation_files() + + def get_all_annotation_files(self) -> List[str]: + return glob( + os.path.join(f"{self.labeling_folder}/*/{self.annotator}_annotations.json") + ) + + def get_finished_annotation_files(self) -> List[str]: + + user_assignment_file = f"{self.labeling_folder}/status/{self.annotator}.json" + if not os.path.exists(user_assignment_file): + print( + "Warning:", + f"The user annotation file does not exist: {user_assignment_file}", + ) + return self.get_all_annotation_files() + + user_assignment = load_json(user_assignment_file) + return [ + f"{self.labeling_folder}/{pdf_sha}/{self.annotator}_annotations.json" + for pdf_sha, assignment in user_assignment.items() + if (assignment["finished"] and not assignment["junk"]) + ] + + def __iter__(self) -> Iterable[Dict]: + + for _file in self._files: + paper_sha = _file.split("/")[-2] + pdf_path = f"{self.labeling_folder}/{paper_sha}/{paper_sha}.pdf" + + yield dict( + paper_sha=paper_sha, + pdf_path=pdf_path, + annotation_path=_file, + ) + + def __len__(self): + + return len(self._files) \ No newline at end of file diff --git a/cli/pawls/preprocessors/__init__.py b/cli/pawls/preprocessors/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/cli/pawls/preprocessors/grobid.py b/cli/pawls/preprocessors/grobid.py new file mode 100644 index 00000000..6f6e9611 --- /dev/null +++ b/cli/pawls/preprocessors/grobid.py @@ -0,0 +1,63 @@ +import json +from typing import List +import requests + +from pawls.preprocessors.model import Page + + +def fetch_grobid_structure(pdf_file: str, grobid_host: str = "http://localhost:8070"): + files = { + "input": (pdf_file, open(pdf_file, "rb"), "application/pdf", {"Expires": "0"}) + } + url = "{}/api/processPdfStructure".format(grobid_host) + resp = requests.post(url, files=files) + if resp.status_code == 200: + return json.loads(resp.text) + else: + raise Exception("Grobid returned status code {}".format(resp.status_code)) + + +def parse_annotations(grobid_structure) -> List[Page]: + pages = [] + for grobid_page in grobid_structure["tokens"]["pages"]: + tokens = [] + for token in grobid_page["tokens"]: + tokens.append( + dict( + text=token["text"], + x=token["x"], + y=token["y"], + width=token["width"], + height=token["height"], + ) + ) + page = dict( + page=dict( + width=grobid_page["page"]["width"], + height=grobid_page["page"]["height"], + index=grobid_page["page"]["pageNumber"] - 1, + ), + tokens=tokens, + ) + pages.append(page) + + return pages + +def process_grobid( + pdf_file: str, + grobid_host: str = "http://localhost:8070" +): + """ + Integration for importing annotations from grobid. + Depends on a grobid API built from our fork https://github.com/allenai/grobid. + Fetches a PDF by sha, sends it to the Grobid API and returns them. + + pdf_file: str + The path to the pdf file to process. + grobid_host: str (optional, default="http://localhost:8070") + The forked grobid API which we use to produce the annotations. + """ + grobid_structure = fetch_grobid_structure(pdf_file, grobid_host) + annotations = parse_annotations(grobid_structure) + + return annotations diff --git a/cli/pawls/preprocessors/model.py b/cli/pawls/preprocessors/model.py new file mode 100644 index 00000000..d1e97ed1 --- /dev/null +++ b/cli/pawls/preprocessors/model.py @@ -0,0 +1,198 @@ +import json +from dataclasses import dataclass # enables inheritance +from typing import NamedTuple, List, Tuple, Dict, Union + + +def union_boxes(boxes: List["Box"]) -> "Box": + """Find the outside boundary of the given boxes. + + Args: + boxes (List[Box]): + A list of Box-like instances + + Returns: + Box: the unioned box. + """ + left, top, right, bottom = float("inf"), float("inf"), float("-inf"), float("-inf") + for box in boxes: + l, t, r, b = box.coordinates + left = min(left, l) + top = min(top, t) + right = max(right, r) + bottom = max(bottom, b) + return Box(left, top, right - left, bottom - top) + + +@dataclass +class Box: + + x: float + y: float + width: float + height: float + + @property + def center(self) -> Tuple[float, float]: + """Return the center of the token box""" + return self.x + self.width / 2, self.y + self.height / 2 + + @property + def coordinates(self) -> Tuple[float, float, float, float, float]: + """Returns the left, top, right, bottom coordinates of the box""" + return (self.x, self.y, self.x + self.width, self.y + self.height) + + def is_in(self, other: "Box", soft_margin: Dict = None) -> bool: + """Determines whether the center of this box is contained + inside another box with a soft margin. + + Args: + other (Box): + The other box object. + soft_margin (Dict, optional): + Alllowing soft margin of the box boundaries. If set, enlarge + the outside box (other) by the coordinates. + Defaults to {}. + """ + other = other.copy() + + x, y = self.center + if soft_margin is not None: + other.pad(**soft_margin) + xa, ya, xb, yb = other.coordinates + + return xa <= x <= xb and ya <= y <= yb + + def pad(self, left=0, top=0, bottom=0, right=0): + """Change the boundary positions of the box""" + + self.x -= left + self.y -= top + self.width += left + right + self.height += top + bottom + + def copy(self): + """Create a copy of the box""" + return self.__class__(**vars(self)) + + def scale(self, scale_factor: Union[float, Tuple[float, float]]): + """Scale the box according to the given scale factor. + + Args: + scale_factor (Union[float, Tuple[float, float]]): + it can be either a float, indicating the same scale factor + for the two axes, or a two-element tuple for x and y axis + scaling factor, respectively. + + """ + + if isinstance(scale_factor, float): + self.x *= scale_factor + self.y *= scale_factor + self.width *= scale_factor + self.height *= scale_factor + + elif isinstance(scale_factor, tuple): + scale_x, scale_y = scale_factor + self.x *= scale_x + self.y *= scale_y + self.width *= scale_x + self.height *= scale_y + + def as_bounds(self) -> Dict[str, float]: + """Convert the box into a the bounds format.""" + return { + "left": self.x, + "top": self.y, + "right": self.x + self.width, + "bottom": self.y + self.height, + } + + +@dataclass +class Token(Box): + text: str + + +@dataclass +class Block(Box): + label: str + + +@dataclass +class PageInfo: + width: float + height: float + index: int + + def scale(self, scale_factor: Union[float, Tuple[float, float]]): + """Scale the page box according to the given scale factor + + Args: + scale_factor (Union[float, Tuple[float, float]]): + it can be either a float, indicating the same scale factor + for the two axes, or a two-element tuple for x and y axis + scaling factor, respectively. + """ + + if isinstance(scale_factor, float): + self.width *= scale_factor + self.height *= scale_factor + elif isinstance(scale_factor, tuple): + scale_x, scale_y = scale_factor + self.width *= scale_x + self.height *= scale_y + + +@dataclass +class Page: + page: PageInfo + tokens: List[Union[Token, Block]] + + def scale(self, scale_factor: Union[float, Tuple[float, float]]): + """Scale the page according to the given scale factor. + + Args: + scale_factor (Union[float, Tuple[float, float]]): + it can be either a float, indicating the same scale factor + for the two axes, or a two-element tuple for x and y axis + scaling factor, respectively. + """ + self.page.scale(scale_factor) + for token in self.tokens: + token.scale(scale_factor) + + def scale_like(self, other: "Page"): + """Scale the page based on the other page.""" + + scale_x = other.page.width / self.page.width + scale_y = other.page.height / self.page.height + + self.scale((scale_x, scale_y)) + + def filter_tokens_by(self, box: Box, soft_margin: Dict = None) -> Dict[int, Token]: + """Select tokens in the Page that inside the input box""" + return { + idx: token + for idx, token in enumerate(self.tokens) + if token.is_in(box, soft_margin) + } + + +def load_tokens_from_file(filename: str) -> List[Page]: + """Load tokens files into the data model + + Returns: + List[Page]: + A list of `Page` object for eac page. + """ + + with open(filename, "r") as fp: + source_data = json.load(fp) + + return [ + Page( + page=PageInfo(**page_data["page"]), + tokens=[Token(**token) for token in page_data["tokens"]], + ) + for page_data in source_data + ] \ No newline at end of file diff --git a/cli/pawls/preprocessors/pdfplumber.py b/cli/pawls/preprocessors/pdfplumber.py new file mode 100644 index 00000000..3b9ae442 --- /dev/null +++ b/cli/pawls/preprocessors/pdfplumber.py @@ -0,0 +1,98 @@ +from typing import List + +import pandas as pd +import pdfplumber + +from pawls.preprocessors.model import Page, Token + + +class PDFPlumberTokenExtractor: + + @staticmethod + def convert_to_pagetoken(row: pd.Series) -> Token: + """Convert a row in a DataFrame to pagetoken""" + return dict( + text=row["text"], + x=row["x0"], + width=row["width"], + y=row["top"], + height=row["height"], + ) + + def extract(self, pdf_path: str) -> List[Page]: + """Extracts token text, positions, and style information from a PDF file. + + Args: + pdf_path (str): the path to the pdf file. + include_lines (bool, optional): Whether to include line tokens. Defaults to False. + + Returns: + PdfAnnotations: A `PdfAnnotations` containing all the paper token information. + """ + plumber_pdf_object = pdfplumber.open(pdf_path) + + pages = [] + for page_id in range(len(plumber_pdf_object.pages)): + cur_page = plumber_pdf_object.pages[page_id] + + tokens = self.obtain_word_tokens(cur_page) + + page = dict( + page=dict( + width=float(cur_page.width), + height=float(cur_page.height), + index=page_id + ), + tokens=tokens + ) + pages.append(page) + + return pages + + def obtain_word_tokens(self, cur_page: pdfplumber.page.Page) -> List[Token]: + """Obtain all words from the current page. + Args: + cur_page (pdfplumber.page.Page): + the pdfplumber.page.Page object with PDF token information + + Returns: + List[PageToken]: + A list of page tokens stored in PageToken format. + """ + words = cur_page.extract_words( + x_tolerance=1.5, + y_tolerance=3, + keep_blank_chars=False, + use_text_flow=False, + horizontal_ltr=True, + vertical_ttb=True, + extra_attrs=["fontname", "size"], + ) + + df = pd.DataFrame(words) + + # Avoid boxes outside the page + df[["x0", "x1"]] = ( + df[["x0", "x1"]].clip(lower=0, upper=int(cur_page.width)).astype("float") + ) + df[["top", "bottom"]] = ( + df[["top", "bottom"]].clip(lower=0, upper=int(cur_page.height)).astype("float") + ) + + df["height"] = df["bottom"] - df["top"] + df["width"] = df["x1"] - df["x0"] + + word_tokens = df.apply(self.convert_to_pagetoken, axis=1).tolist() + return word_tokens + + +def process_pdfplumber(pdf_file: str): + """ + Integration for importing annotations from pdfplumber. + pdf_file: str + The path to the pdf file to process. + """ + pdf_extractors = PDFPlumberTokenExtractor() + annotations = pdf_extractors.extract(pdf_file) + + return annotations diff --git a/cli/readme.md b/cli/readme.md new file mode 100644 index 00000000..ee447ab8 --- /dev/null +++ b/cli/readme.md @@ -0,0 +1,62 @@ +## PAWLS CLI + +The PAWLS CLI helps manage annotation tasks based on PDFs. + +### Installation + +1. Install dependencies + + ```bash + cd pawls/cli + python setup.py install + ``` + +2. Install poppler, the PDF renderer, which is used to export the annotations into a COCO-format Dataset by converting the PDF pages to images. +Please follow the [instructions here](https://github.com/Belval/pdf2image#windows). + +### Usage + +1. Download PDFs based on s into the (e.g., `skiff_files/apps/pawls/papers` ). If you work at AI2, see the internal usage script for doing this [here](../../scripts/ai2-internal). Otherwise, pdfs are expected to be in a directory structure with a single pdf per folder, where each folder's name is a unique id corresponding to that pdf. For example: +``` + top_level/ + ├───pdf1/ + │ └───pdf1.pdf + └───pdf2/ + └───pdf2.pdf +``` +By default, pawls will use the name of the containing directory to refer to the pdf in the ui. + +2. Process the token information for each PDF document with the given PDF preprocessor `grobid/pdfplumber`. *Note: to use the grobid preprocessor, you need to run `docker-compose up` in a separate shell, because grobid needs to be running as a service.*: + ```bash + pawls preprocess grobid skiff_files/apps/pawls/papers + ``` +At this point, you will be able to view a UI by running `docker-compose up` at the root of the repository and navigating to `localhost:8080`. + +3. Assign annotation tasks (s) to specific users : + ```bash + pawls assign ./skiff_files/apps/pawls/papers + ``` + Optionally at this stage, you can provide a `--name-file` argument to `pawls assign`, + which allows you to specify a name for a given pdf (for example the title of a paper). + This file should be a json file containing `sha:name` mappings. +4. (optional) Create pre-annotations for the PDFs based on some model predictions `anno.json`: + ```bash + pawls preannotate anno.json -u + ``` + You could find an example for generating the pre-annotations in `scripts/generate_pdf_layouts.py`. +5. Export the annotated dataset to the COCO format: + + 1. Export all annotations of a project of the default annotator (development_user): + ```bash + pawls export + ``` + + 2. Export only finished annotations of from a given annotator, e.g. markn: + ```bash + pawls export -u markn + ``` + + 3. Export all annotations of from a given annotator: + ```bash + pawls export -u markn --all + ``` \ No newline at end of file diff --git a/cli/requirements.txt b/cli/requirements.txt new file mode 100644 index 00000000..7fe94c1f --- /dev/null +++ b/cli/requirements.txt @@ -0,0 +1,21 @@ +click>=6.7 +tqdm +pdfminer +pdf2image +pandas +tabulate + +# S3 access +boto3 + +# Grobid integration +requests + +# used for testing +pytest + +# Used for pdf-token extraction +pdfplumber + +# Used for calculating scores +pycocotools \ No newline at end of file diff --git a/cli/setup.py b/cli/setup.py new file mode 100644 index 00000000..c3908359 --- /dev/null +++ b/cli/setup.py @@ -0,0 +1,37 @@ +from setuptools import setup, find_packages + +with open("readme.md", "r") as readme_file: + readme = readme_file.read() + + +setup( + name="pawls", + version="0.0.1", + description="PAWLS (PDF Annotation with Labels and Structure)", + packages=find_packages(), + long_description=readme, + url="http://github.com/allenai/pawls", + author="Mark Neumann", + author_email="markn@allenai.org", + keywords="", + classifiers=[ + "Development Status :: 3 - Alpha", + "Programming Language :: Python :: 3.6", + "License :: OSI Approved :: Apache Software License", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + ], + license="Apache 2.0", + install_requires=[ + "click>=6.7", + "requests", + "boto3", + "tqdm", + "pdf2image==1.14.0", + "pdfminer" + ], + python_requires=">=3.6", + entry_points={"console_scripts": ["pawls=pawls.__main__:pawls_cli"]}, + zip_safe=False, +) diff --git a/cli/test/__init__.py b/cli/test/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/cli/test/assign_test.py b/cli/test/assign_test.py new file mode 100644 index 00000000..f91b4d8c --- /dev/null +++ b/cli/test/assign_test.py @@ -0,0 +1,96 @@ +import os +import unittest +import tempfile +import json +import shutil + +from click.testing import CliRunner + +from pawls.commands import assign + + +class TestAssign(unittest.TestCase): + def test_assign_annotator(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + result = runner.invoke(assign, [tempdir, "mark@example.org"]) + assert result.exit_code == 0 + assert os.path.exists(os.path.join(tempdir, "status", "mark@example.org.json")) + + def test_assign_annotator_with_bad_name(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + result = runner.invoke(assign, [tempdir, "mark!!! ABC"]) + assert result.exit_code == 2 + assert "Provided annotator was not a valid email." in result.output + + def test_assign_unfetched_pdfs(self): + runner = CliRunner() + sha = "34f25a8704614163c4095b3ee2fc969b60de4698" + with tempfile.TemporaryDirectory() as tempdir: + result = runner.invoke(assign, [tempdir, "mark@example.org", sha]) + assert result.exit_code == 2 + assert "Found shas which are not present" in result.output + + def test_assign_pdfs(self): + runner = CliRunner() + sha = "34f25a8704614163c4095b3ee2fc969b60de4698" + with tempfile.TemporaryDirectory() as tempdir: + + # Copy the fixture in, as though it was there already. + sub_temp_dir = os.path.join(tempdir, "pdfs") + shutil.copytree(f"test/fixtures/pawls/", sub_temp_dir) + result = runner.invoke(assign, [sub_temp_dir, "mark@example.org", sha]) + assert result.exit_code == 0 + status_path = os.path.join(sub_temp_dir, "status", "mark@example.org.json") + + annotator_json = json.load(open(status_path)) + assert annotator_json == { + sha: { + "sha": sha, + "name": sha, + "annotations": 0, + "relations": 0, + "finished": False, + "junk": False, + "comments": "", + "completedAt": None, + } + } + + def test_assign_pdfs_with_name_file(self): + runner = CliRunner() + sha = "34f25a8704614163c4095b3ee2fc969b60de4698" + with tempfile.TemporaryDirectory() as tempdir: + + # Copy the fixture in, as though it was there already. + sub_temp_dir = os.path.join(tempdir, "pdfs") + shutil.copytree(f"test/fixtures/pawls/", sub_temp_dir) + # This time we pass a file containing the name mapping, + # so we should find the name in the resulting status. + result = runner.invoke( + assign, + [ + sub_temp_dir, + "mark@example.org", + sha, + "--name-file", + "test/fixtures/pawls/name_mapping.json", + ], + ) + assert result.exit_code == 0 + status_path = os.path.join(sub_temp_dir, "status", "mark@example.org.json") + + annotator_json = json.load(open(status_path)) + assert annotator_json == { + sha: { + "sha": sha, + "name": "Dropout: a simple way to prevent neural networks from overfitting", + "annotations": 0, + "relations": 0, + "finished": False, + "junk": False, + "comments": "", + "completedAt": None, + } + } diff --git a/cli/test/export_test.py b/cli/test/export_test.py new file mode 100644 index 00000000..df27d622 --- /dev/null +++ b/cli/test/export_test.py @@ -0,0 +1,228 @@ +import os +import unittest +import tempfile +import json + +import pandas as pd +from click.testing import CliRunner + +from pawls.commands import export + +""" +Details of annotations in test/fixtures/pawls/ + +There are three task: +0. 3febb2bed8865945e7fddc99efd791887bb7e14f +1. 34f25a8704614163c4095b3ee2fc969b60de4698 +2. 553c58a05e25f794d24e8db8c2b8fdb9603e6a29 + +* Development user has annotations for all images +* markn is assigned to task 0 and task 1 +* markn finishes task 1 but not task 0 +* shannons is assigned to task 1 and task 2 +* shannons finishes neither of the tasks +* shannons does not have any annotations for task 1 +""" + + +def _load_json(filename: str): + with open(filename, "r") as fp: + return json.load(fp) + + +class TestExportCOCO(unittest.TestCase): + def setUp(self): + super().setUp() + self.TEST_ANNO_DIR = "test/fixtures/pawls/" + self.TEST_CONFIG_FILE = "test/fixtures/configuration.json" + self.TEST_SELECTED_CATEGORY = "Figure Text" + self.PDF_SHAS = [ + "3febb2bed8865945e7fddc99efd791887bb7e14f", + "34f25a8704614163c4095b3ee2fc969b60de4698", + "553c58a05e25f794d24e8db8c2b8fdb9603e6a29", + ] + self.USERS = ["markn@example.com", "shannons@example.com"] + self.DEFAULT_USER = "development_user@example.com" + + def test_export_annotation_from_all_annotators(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + result = runner.invoke( + export, [self.TEST_ANNO_DIR, self.TEST_CONFIG_FILE, tempdir, "coco"] + ) + assert result.exit_code == 0 + + assert os.path.exists(os.path.join(tempdir, self.USERS[0] + ".json")) + assert os.path.exists(os.path.join(tempdir, self.USERS[1] + ".json")) + assert os.path.exists(os.path.join(tempdir, self.DEFAULT_USER + ".json")) + assert os.path.exists(os.path.join(tempdir, "images")) + + def test_export_annotation_from_multiple_annotators(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + result = runner.invoke( + export, + [ + self.TEST_ANNO_DIR, + self.TEST_CONFIG_FILE, + tempdir, + "coco", + "-u", + self.USERS[0], + "-u", + self.USERS[1], + ], + ) + assert result.exit_code == 0 + + assert os.path.exists(os.path.join(tempdir, self.USERS[0] + ".json")) + assert os.path.exists(os.path.join(tempdir, self.USERS[1] + ".json")) + assert not os.path.exists( + os.path.join(tempdir, self.DEFAULT_USER + ".json") + ) + assert os.path.exists(os.path.join(tempdir, "images")) + + def test_export_annotation_with_all_annotators_annotations(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + result = runner.invoke( + export, + [ + self.TEST_ANNO_DIR, + self.TEST_CONFIG_FILE, + tempdir, + "coco", + "-u", + self.USERS[0], + "--include-unfinished", + ], + ) + assert result.exit_code == 0 + + anno = _load_json(os.path.join(tempdir, f"{self.USERS[0]}.json")) + paper_shas = [ele["paper_sha"] for ele in anno["papers"]] + + assert self.PDF_SHAS[1] in paper_shas + assert self.PDF_SHAS[0] in paper_shas + + def test_export_annotation_with_missing_annotation_file(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + result = runner.invoke( + export, + [ + self.TEST_ANNO_DIR, + self.TEST_CONFIG_FILE, + tempdir, + "coco", + "-u", + self.USERS[1], + "--include-unfinished", + ], + ) + assert result.exit_code == 0 + + anno_file = _load_json(os.path.join(tempdir, f"{self.USERS[1]}.json")) + paper_sha_map = {ele["id"]: ele["paper_sha"] for ele in anno_file["papers"]} + image_paper_map = { + ele["id"]: ele["paper_id"] for ele in anno_file["images"] + } + all_paper_shas = [ + paper_sha_map[image_paper_map[anno["image_id"]]] + for anno in anno_file["annotations"] + ] + + assert self.PDF_SHAS[2] in all_paper_shas + assert self.PDF_SHAS[1] not in all_paper_shas + + def test_export_annotation_with_specific_category(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + result = runner.invoke( + export, + [ + self.TEST_ANNO_DIR, + self.TEST_CONFIG_FILE, + tempdir, + "coco", + "-u", + self.USERS[0], + "-c", + self.TEST_SELECTED_CATEGORY, + "--include-unfinished", + ], + ) + assert result.exit_code == 0 + + anno = _load_json(os.path.join(tempdir, f"{self.USERS[0]}.json")) + + assert len(anno["categories"]) == 1 + assert anno["categories"][0]['name'] == self.TEST_SELECTED_CATEGORY + +class TestExportToken(TestExportCOCO): + def test_export_annotation_from_all_annotators(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + saved_path = f"{tempdir}/annotations.csv" + result = runner.invoke( + export, [self.TEST_ANNO_DIR, self.TEST_CONFIG_FILE, saved_path, "token"] + ) + assert result.exit_code == 0 + assert os.path.exists(saved_path) + + df = pd.read_csv(saved_path) + for annotator in self.USERS + [self.DEFAULT_USER]: + assert annotator in df.columns + + def test_export_annotation_from_multiple_annotators(self): + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + saved_path = f"{tempdir}/annotations.csv" + result = runner.invoke( + export, + [ + self.TEST_ANNO_DIR, + self.TEST_CONFIG_FILE, + saved_path, + "token", + "-u", + self.USERS[0], + "-u", + self.USERS[1], + ], + ) + assert result.exit_code == 0 + assert os.path.exists(saved_path) + + df = pd.read_csv(saved_path) + for annotator in self.USERS: + assert annotator in df.columns + + def test_export_annotation_with_specific_category(self): + + runner = CliRunner() + with tempfile.TemporaryDirectory() as tempdir: + saved_path = f"{tempdir}/annotations.csv" + result = runner.invoke( + export, + [ + self.TEST_ANNO_DIR, + self.TEST_CONFIG_FILE, + saved_path, + "token", + "-u", + self.USERS[0], + "-c", + self.TEST_SELECTED_CATEGORY + ] + ) + assert result.exit_code == 0 + + df = pd.read_csv(saved_path) + + all_exported_labels = df[self.USERS[0]].dropna().unique() + assert len(all_exported_labels) == 1 + assert self.TEST_SELECTED_CATEGORY in all_exported_labels[0] + +if __name__ == "__main__": + unittest.main() diff --git a/cli/test/fixtures/anno.json b/cli/test/fixtures/anno.json new file mode 100644 index 00000000..a49feb5f --- /dev/null +++ b/cli/test/fixtures/anno.json @@ -0,0 +1 @@ +{"34f25a8704614163c4095b3ee2fc969b60de4698.pdf": [{"page": {"height": 2200, "width": 1700, "index": 0}, "blocks": [[304.4071960449219, 983.696533203125, 1086.7208557128906, 425.0955810546875, "Paragraph"], [263.661865234375, 277.7256164550781, 1167.4327392578125, 95.8544921875, "Title"], [254.3060760498047, 1509.86474609375, 248.92869567871094, 40.469482421875, "Title"], [301.1861877441406, 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