Store receipts with reviewable extraction and corrections

Application background

An employee uploads a photograph of a receipt for 19.99. A text-extraction service reads the image and proposes fields such as merchant, currency and total. The accountant reviews those fields before accepting them for bookkeeping.

Reading an image is imperfect. The service might propose 199.90 instead of 19.99. If extraction runs again after the accountant corrects the amount, the new machine result must not erase the human-approved value.

Example walkthrough

Treat extracted fields as proposals. Source versions and review status tell the application which proposals still apply and which values a human has accepted.

Your assignment

Deliver: Build receipt upload and review records. Keep original evidence, machine suggestions and human-confirmed amounts separate so a later extraction cannot erase a correction.

Required behavior: Keep source evidence, extracted suggestions and human-confirmed values as distinct records. Money is stored in integer minor units with currency. Reports use confirmed values or clearly label unreviewed entries.

The required first milestone is a working local implementation of the behavior above. The numbered implementation steps define the scope. The cloud architecture is a later extension, not something the starter has already provisioned.

Get the code and run the supplied example

The code is in the public junior-to-staff repository. Install Git and Python 3.12+. No AWS account or Python packages are required for this first run. If you already have a checkout, use it and skip cloning.

git clone https://github.com/Soulful-Iris/junior-to-staff.git
cd junior-to-staff
python3 examples/architecture-starts/a_receipt_tracker.py

Supplied file: examples/architecture-starts/a_receipt_tracker.py. You can also read or download the source here (download file, source below).

"""Local mechanism demonstration for a-receipt-tracker. No AWS resources are created."""
from decimal import Decimal
receipt={'suggested_minor':19990,'confirmed_minor':None,'revision':1}
confirmed=int(Decimal('19.99')*100)
receipt.update(confirmed_minor=confirmed,revision=2)
receipt['suggested_minor']=19990
print('Machine suggestion:',receipt['suggested_minor'])
print('Report amount:',receipt['confirmed_minor'],'minor units; revision',receipt['revision'])

This program is a mechanism demonstration: it runs the small scenario in one process and prints the result. It is not an HTTP service, a complete application, or an AWS deployment. A successful run demonstrates this mechanism only. It does not establish the workload or failure guarantees of the application you will build.

Example output from the supplied run:

Generated IDs and timestamps may differ. Compare the state transitions and outcomes.

Machine suggestion: 19990
Report amount: 1999 minor units; revision 2

Set up your implementation workspace

Create work/a-receipt-tracker/ in your checkout (or use a separate repository). Copy the supplied mechanism into that directory as mechanism.py, then extract its state transitions into functions you can call from your implementation. The record and module names below describe what you must implement. They are not a promise that files with those names already exist. Keep a README.md beside your implementation with its exact run commands and observed results.

Local components and state to implement

This table names the records, interfaces or decision inputs for your deliverable. Unless a name is explicitly linked to supplied source above, it is something you create. Implement the local state transitions first, then connect the HTTP, storage or worker boundaries required by the steps.

Implement the assignment

1. Store one receipt with evidence

Save the private original image and checksum, then create a receipt record with owner and upload time. Keep object identity immutable so an accountant can inspect exactly what supported a correction.

2. Add extraction as a suggestion

Record each extractor/model version, raw suggested fields and confidence/evidence location. Validate currency and decimal conversion explicitly. Low confidence, inconsistent totals or missing fields route to review rather than becoming silently accepted financial data.

3. Implement conditional human review

Display image, suggestion and editable confirmed fields side by side. Save against expected revision and record actor plus previous/new values. An extraction retry appends a new suggestion. It has no authority to overwrite confirmed fields.

4. Build useful reports and recovery

Filter reports by owner/date/currency and distinguish reviewed from pending amounts. Export integer-money-derived decimal strings without binary floating-point rounding. Restore a database backup together with referenced object versions and show that every confirmed record still has source evidence.

Demonstrate the completed local result

Handoff: In your implementation README, include the start command, one successful operation, the failure case above and the resulting stored state or decision. State which dependencies are simulated. Someone with a fresh checkout should be able to reproduce this without your chat history.

Workload assumptions and capacity decisions

These are constructed exercise assumptions. The stated workload is a design target. The local demonstration does not establish that throughput. Use the estimation constants to check units before choosing capacity.

Map the local implementation to AWS

Deployment status: local only. Running the supplied command creates no AWS resources and configures no cloud connections. The diagram is a proposed deployment of the completed application. Each box needs either a deployed runtime, a provisioned service or an explicitly external dependency.

Read the diagram by following the arrows from the entry point: application code accepts the request or event, the state owner commits it, and any worker produces the later result. The table ties those roles to code and adapter work. Multiple boxes do not imply multiple Python files already exist.

Textract supplies candidate data. The review transaction establishes the confirmed financial record. Keeping those authorities separate makes correction durable across retries.

Provision resources, then connect the application

Use one disposable AWS environment for the cloud exercise. Put the named resources in infra/template.yaml or your existing IaC tool, pass resource IDs through configuration, and scope each runtime role to its own tables, buckets and queues. The diagram is a design to implement. It is not a claim that these resources have been deployed. Record the commands you used to deploy and remove the exercise resources.

For concrete provisioning commands, configuration wiring and cleanup, use the AWS foundation guide. It includes a deployable table/queue/object-storage foundation and explains which application and service adapters you still implement.

A provisioned queue or table does not make the local program use it. Configure resource IDs in the deployed runtime, replace the local adapter, and replay the same successful and failing operation against that runtime. Record the deployed commit and observable result, then remove the disposable resources using your infrastructure tool.

Extend the design after the baseline works

Add currencies with different minor-unit conventions and tax-line reconciliation. Make the currency exponent part of the conversion policy instead of multiplying every amount by 100.