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"allow": [
"Bash(python manage.py migrate:*)",
"Bash(tree:*)",
"Bash(dir /b \"c:\\\\dev_projects\\\\ScalesApp\\\\frontend\\\\src\\\\contexts\")",
"Bash(ls:*)",
"Bash(python manage.py makemigrations:*)",
"Bash(tasklist:*)",
"Bash(findstr:*)",
"Bash(python manage.py:*)",
"Bash(cat:*)",
"Bash(netstat:*)",
"Bash(taskkill:*)",
"Bash(DJANGO_SETTINGS_MODULE=scalesapp.settings python:*)",
"Bash(cd:*)",
"Bash(python - << 'PYEOF'\nfrom pathlib import Path\n\npath = Path\\(\"C:/dev_projects/ScalesApp/Project-Assignment-unpacked/word/document.xml\"\\)\ntext = path.read_text\\(encoding=\"utf-8\"\\)\n\n# Unique start and end markers\nSTART = ' \n Module 1: Serial Bridge \\(Flask + pyserial\\)\n \n \n Approach & reasoning: \n The scale outputs weight data as ASCII strings over RS-232. A dedicated Flask process was chosen instead of integrating with Django so the COM connection stays alive independently of backend restarts. SSE was selected as the transport because the browser’s native EventSource API handles reconnection automatically without extra client code.\n \n \n Step-by-step workflow: \n Asked Claude Code to generate the SerialPortReader threading class and background queue, then the Flask /events SSE endpoint, then the Windows system-tray icon via pystray, and finally the PyInstaller spec for a single-file executable.\n \n \n Testing strategy: \n Used com0com to create a virtual COM port pair and a test_writer.py script that emits simulated weight readings at a configurable interval, enabling end-to-end testing without physical hardware.\n \n \n AI tool choice: \n Claude Code — it maintained context across all four source files simultaneously and produced the threading/queue pattern correctly in one pass.\n \n \n Key prompts: \n “Create a SerialPortReader class that reads lines from a COM port in a background thread and pushes them to a thread-safe queue”; “Write a Flask SSE endpoint that drains the queue and sends keep-alive heartbeat comments”; “Generate a PyInstaller spec that bundles app.py with the .env file into a single Windows executable with a hidden console window”.\n \n \n Module 2: Django REST API Backend\n \n \n Approach & reasoning: \n Standard DRF ModelViewSet pattern for all resources, with custom @action decorators for domain operations \\(set_tare, set_gross\\). An ASGI server \\(uvicorn\\) was required once the async SSE broadcast endpoint was introduced.\n \n \n Step-by-step workflow: \n Scaffolded three Django apps \\(api, vehicles, nomenclatures\\), then for each app generated models → serializers → viewsets → URL registration. Added sse_broadcast_update calls to every mutation, then migrated from WSGI runserver to uvicorn/ASGI.\n \n \n Testing strategy: \n Django’s built-in test client for CRUD endpoints; Postman for manual JWT token-flow and weight-setting action tests.\n \n \n AI tool choice: \n Claude Code — handles Django’s repetitive boilerplate and tracks the relationships between models, serializers, viewsets, and URL patterns across multiple files without losing consistency.\n \n \n Key prompts: \n “Create a Vehicle ModelViewSet with set_tare and set_gross @action endpoints that record the current user and timestamp, then call sse_broadcast_update”; “Write an async ASGI SSE handler that authenticates via a JWT query parameter and replays up to 100 missed events using the Last-Event-ID header”.\n \n \n Module 3: React SPA Frontend\n \n \n Approach & reasoning: \n State is split into focused Contexts: AuthContext owns the JWT lifecycle, NomenclatureContext owns all server data and the live SSE subscription. Components receive only what they need through context hooks, keeping the main view component \\(Main.jsx\\) free of data-fetching logic.\n \n \n Step-by-step workflow: \n Built AuthContext with axios interceptors and the refresh-queue pattern first; then NomenclatureContext with SSE subscription and live state updates; then Main.jsx with the split-panel layout \\(vehicle list left, live weight top, detail panel right\\).\n \n \n Testing strategy: \n Manual browser testing. SSE reconnection was validated by restarting the uvicorn server mid-session and confirming the UI recovered without a page reload.\n \n \n AI tool choice: \n Claude Code — generated complete, functional JSX components and custom hooks in a single pass while keeping multi-file context coherent \\(e.g., knowing which context a new component should consume\\).\n \n \n Key prompts: \n “Write a React Context that opens an EventSource, handles insert and update events, and maintains a sorted vehicle array in state”; “Create an axios interceptor that catches 401 responses, queues concurrent requests, refreshes the token once, then replays all queued requests with the new token”.\n \n \n Module 4: Nomenclature / Dynamic Field System\n \n \n Approach & reasoning: \n Field schemas are stored in three related models \\(Nomenclature, NomenclatureField, NomenclatureEntry\\) and values are saved as a JSON blob on VehicleExtra. Validation is enforced in the serializer rather than by DB constraints, so the schema can be modified at runtime without any migration.\n \n \n Step-by-step workflow: \n Designed the three models together with Claude Code, then wrote the VehicleExtraSerializer validate method with the dynamic type-checking loop, then implemented the frontend rendering logic that maps each NomenclatureField type to the appropriate React input component.\n \n \n Testing strategy: \n Created test nomenclatures in Django admin \\(cargo type as a lookup dropdown, driver name as free text, axle count as a number\\) and confirmed the frontend rendered the correct controls and rejected out-of-range values.\n \n \n AI tool choice: \n Claude Code — generated the serializer validation loop that iterates over active NomenclatureFields and applies type-specific checks on the submitted JSON, which would have been tedious to write by hand.\n \n \n Key prompts: \n “Write a DRF serializer validate method that reads active NomenclatureField definitions and enforces text/number/bool/choice type constraints on a JSONField”; “Render a list of nomenclature field definitions in React, switching the input component based on the kind and field_type values”.\n \n \n Module 5: Authentication & Authorisation Layer\n \n \n Approach & reasoning: \n djangorestframework-simplejwt issues short-lived access tokens \\(15 min\\) and long-lived refresh tokens \\(7 days\\). Role-based access is enforced via DRF permission classes on each viewset so viewers cannot trigger any write operations.\n \n \n Step-by-step workflow: \n Configured simplejwt settings and a custom TokenObtainPairSerializer that embeds role and is_admin into the payload. Wrote AuthContext with a 14-minute refresh interval and a promise queue that pauses concurrent requests during a refresh cycle. Added permission classes to viewsets.\n \n \n Testing strategy: \n Temporarily reduced the access token lifetime to 30 seconds in development settings and confirmed the frontend refreshed silently and continued working without any user-visible interruption.\n \n \n AI tool choice: \n Claude Code — produced the promise-queue interceptor pattern \\(non-trivial async code that must handle race conditions correctly\\) and the custom simplejwt serializer in a single interaction.\n \n \n Key prompts: \n “Write an axios interceptor that catches 401 errors, queues all concurrent requests, refreshes the token once, then replays every queued request with the new access token”; “Add a custom TokenObtainPairSerializer that includes the user’s role and is_admin fields in the JWT payload”.\n \n \n Module 6: Report Builder\n \n \n Approach & reasoning: \n A character-grid coordinate system \\(80×66 cells\\) was chosen to avoid pixel-rounding issues and map naturally to fixed-width print output. Element types are modelled as a JS class hierarchy with a shared base class and serialised to plain JSON for storage in the Django Report model.\n \n \n Step-by-step workflow: \n Built the Element class hierarchy \\(TextElement, DBTextElement, FrameElement, BandElement, etc.\\) first, then EditorCanvas with drag-drop and resize-handle logic, then ObjectInspector for property editing. Band elements \\(header/detail/footer/summary\\) and BandContext for preview iteration were added last.\n \n \n Testing strategy: \n Visual testing — designed a sample vehicle weighing report, saved it to Django, reloaded the editor, and verified the layout was restored identically. Preview mode was tested against the live vehicles API endpoint to confirm data-bound fields rendered correct values.\n \n \n AI tool choice: \n Claude Code — provided the coordinate math for grid-snapping and multi-element drag, and generated the BandContext provider/consumer pattern correctly in one pass.\n \n \n Key prompts: \n “Calculate the snapped grid position from a mouse drag event on a character-grid canvas, accounting for scroll offset and the element’s grab origin”; “Write a React Context that iterates over an array of data rows and exposes the current row to child DBTextElements during print preview”; “Add multi-select with a rubber-band selection rectangle and group drag on the editor canvas”.\n \n'''\n\nnew_text = text[:start_idx] + REPLACEMENT + text[end_idx:]\npath.write_text\\(new_text, encoding=\"utf-8\"\\)\nprint\\(\"Done. Replaced\", len\\(text\\), \"->\", len\\(new_text\\), \"chars\"\\)\nPYEOF)"
]
}
}