@okfde/filingcabinet v0.0.4

django-filingcabinet

A Django app that manages documents with pages, page annotations and collections. Optionally can use document feature annotation and prediction.

Quickstart with Docker

Install docker and docker compose plugin.

# Copy example environment and set a secret key
cp .env.example .env
# Create database file to mount into container
touch db.sqlite3
docker-compose run --rm web python manage.py migrate
# Create a user account
docker-compose run --rm web python manage.py createsuperuser
# Start all services (nginx, web, worker, broker)
docker-compose up
# Nginx will be available at localhost:8080 by default

Example User flow

Access the admin interface at: http://localhost:8080/admin/

Set the correct site domain at: http://localhost:8080/admin/sites/site/

Upload documents at: http://localhost:8080/admin/filingcabinet/document/

Integrate into a Django project

See the src/fc_project dir for an example of a Django project that uses django-filingcabinet and the feature prediction in fcdocs-annotate.

Management command to import directory of PDFs

python manage.py import_documents <directory of *.pdf files>

You can provide extra metadata as a JSON file with the same name as the PDF file. E.g.:

{
  "title": "",
  "description": "",
  "language": "<ISO language code>",
  "published_at": "<ISO date string>",
  "public": true,
  "listed": true,
  "properties": {
    "custom": "properties"
  },
  "data": {
    "filterable": "data"
  },
  "tags": ["Tag"],
  "collection": 123
}

Manual feature annotation

You can generate training data by annotating documents in your database. Create features in the admin and then visit:

http://localhost:8080/documents/features/

Feature prediction on documents

Use a ZIP-export of a kedro feature model: https://github.com/okfde/fcdocs#packaging-the-models

Upload a packaged feature model as .zip: http://localhost:8080/admin/fcdocs_annotation/feature/

Start feature prediction tasks on documents via document admin action dropdown.

Prediction microservice

You can use the prediction API stand-alone as a microservice. Send JSON with a document URL and a callback URL to a feature prediction API endpoint:

curl --request POST \
  --url http://localhost:8080/api/feature/1/predict/ \
  --header 'Content-Type: application/json' \
  --data '{"document_url": "http://example.com/document.pdf",
           "callback_url": "http://example.com/callback/"}'

This will return a JSON document like this:

{
  "callback_url": "http://example.com/callback/",
  "document_url": "http://example.com/document.pdf",
  "feature_id": 1,
  "task_id": "93e84b09-78ca-4c27-97ce-90b23d13fae5",
  "result": null,
  "status": "pending",
  "details": ""
}

The callback URL will be POSTed a JSON document like this:

{
  "callback_url": "http://example.com/callback/",
  "document_url": "http://example.com/document.pdf",
  "feature_id": 1,
  "task_id": "93e84b09-78ca-4c27-97ce-90b23d13fae5",
  "result": false,
  "status": "complete",
  "details": ""
}

Tests

In this project we use pytest and playwright to test the application. To install all dependencies for the tests, use:

python3 -m venv fc-env
source  fc-env/bin/activate
pip install -e ".[test]"
playwright install --with-deps chromium
yarn install
yarn run build

To run the tests, use:

pytest

or to run the tests and see the end-to-end tests running in the browser, use:

pytest --headed