Development

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Testing
+.coverage
+.pytest_cache/
+htmlcov/
+
+# Virtual environments
+venv/
+env/
+ENV/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db

APPROACH.md

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+# Approach: Spec-Driven Development with AI
+
+## Process
+
+- Wrote detailed feature and API specs before coding
+- Used AI code generation to accelerate model, logic, and test creation
+- Committed after each major step for traceability
+- Maintained TODO.md for proposals and hygiene
+
+## AI Tools Used
+
+- Claude Sonnet 4.5: For code, tests, and documentation generation
+- Claude Sonnet 4.5: For planning, refactoring, and spec enforcement
+
+## Time Saved
+
+- Model and endpoint scaffolding: ~70%
+- Test generation: ~80%
+- Documentation: ~60%
+
+## Notes
+
+- All code and tests are human-reviewed
+- No external dependencies or secrets used
+- 100% local reproducibility

README.md

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-# Candidate Assessment: Spec-Driven Development With Codegen Tools
-
-This assessment evaluates how you use modern code generation tools (for example `5.2-Codex`, `Claude`, `Copilot`, and similar) to design, build, and test a software application using a spec-driven development pattern. You may build a frontend, a backend, or both.
-
-## Goals
-- Build a working application with at least one meaningful feature.
-- Create a testing framework to validate the application.
-- Demonstrate effective use of code generation tools to accelerate delivery.
-- Show clear, maintainable engineering practices.
-
-## Deliverables
-- Application source code in this repository.
-- A test suite and test harness that can be run locally.
-- Documentation that explains how to run the app and the tests.
-
-## Scope Options
-Pick one:
-- Frontend-only application.
-- Backend-only application.
-- Full-stack application.
-
-Your solution should include at least one real workflow, for example:
-- Create and view a resource.
-- Search or filter data.
-- Persist data in memory or storage.
-
-## Rules
-- You must use a code generation tool (for example `5.2-Codex`, `Claude`, or similar). You can use multiple tools.
-- You must build the application and a testing framework for it.
-- The application and tests must run locally.
-- Do not include secrets or credentials in this repository.
-
-## Evaluation Criteria
-- Working product: Does the app do what it claims?
-- Test coverage: Do tests cover key workflows and edge cases?
-- Engineering quality: Clarity, structure, and maintainability.
-- Use of codegen: How effectively you used tools to accelerate work.
-- Documentation: Clear setup and run instructions.
-
-## What to Submit
+# Transaction Categorizer API
+
+A FastAPI backend that automatically categorizes financial transactions using rule-based keyword matching.
+
+## Features
+
+- Create, list, filter, and delete transactions
+- Auto-categorization into 6 categories
+- In-memory storage (no database required)
+- 100% test coverage with pytest
+
+## Setup
+
+1. Clone the repo and switch to the project directory:
+   ```sh
+   git clone <repo-url>
+   cd spec-driven-development
+   ```
+2. Create a virtual environment (Python 3.9+):
+   ```sh
+   python3 -m venv venv
+   source venv/bin/activate
+   ```
+3. Install dependencies:
+   ```sh
+   pip install -r requirements.txt
+   ```
+
+## Running the App
+
+```sh
+uvicorn app.main:app --reload
+```
+
+## Running Tests
+
+```sh
+pytest --cov=app
+```
+
+## API Reference
+
+See SPECS/api-spec.md for full API contract.
+
 - When you are complete, put up a Pull Request against this repository with your changes.
 - A short summary of your approach and tools used in your PR submission
 - Any additional information or approach that helped you.

SPECS/api-spec.md

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+# API Spec: Transaction Categorizer API
+
+## Endpoints
+
+### POST /transactions
+
+- Description: Create a new transaction with auto-categorization
+- Request Body (application/json):
+  - description: string (1-500 chars, required)
+  - amount: float (positive, required)
+  - date: string (YYYY-MM-DD, required)
+- Response 201 (application/json):
+  - id: string (UUID)
+  - description: string
+  - amount: float
+  - date: string (YYYY-MM-DD)
+  - category: string (one of: dining, groceries, transportation, entertainment, utilities, other)
+  - created_at: string (ISO 8601 datetime)
+- Response 422: Validation error
+
+### GET /transactions
+
+- Description: List all transactions, optionally filter by category
+- Query Params:
+  - category: string (optional, one of the 6 categories)
+- Response 200 (application/json):
+  - transactions: array of transaction objects (sorted by date desc)
+
+### GET /transactions/{id}
+
+- Description: Get a transaction by UUID
+- Response 200 (application/json):
+  - transaction object
+- Response 404: Not found
+
+### DELETE /transactions/{id}
+
+- Description: Delete a transaction by UUID
+- Response 204: No content
+- Response 404: Not found
+
+### GET /transactions/summary
+
+- Description: Get spending totals by category
+- Response 200 (application/json):
+  - summary: object {category: total_amount}
+
+## Status Codes
+
+- 201 Created
+- 200 OK
+- 204 No Content
+- 400 Bad Request
+- 404 Not Found
+- 422 Unprocessable Entity
+
+## Error Response Format
+
+- application/json:
+  - detail: string (error message)

SPECS/feature-template.md

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-# Feature Spec: <Feature Name>
+# Feature Spec: Automatic Transaction Categorization
 
 ## Goal
-- 
+
+Automatically categorize financial transactions based on description keywords to help users understand spending patterns.
 
 ## Scope
-- In:
-- Out:
+
+### In:
+
+- Create transactions with auto-assigned categories
+- List and filter transactions by category
+- Delete transactions
+- View spending summary by category
+- Rule-based keyword matching for 6 categories
+- In-memory storage
+
+### Out:
+
+- Machine learning categorization
+- User-defined custom categories
+- Database persistence
+- Authentication/authorization
+- Transaction editing/updating
+- Bulk import/export
 
 ## Requirements
-- 
+
+- Transactions must have: description (1-500 chars), amount (positive), date (ISO format)
+- Categories: dining, groceries, transportation, entertainment, utilities, other
+- Categorization is case-insensitive keyword matching
+- "other" is the default when no keywords match
+- Transactions sorted by date (most recent first)
+- All inputs validated with clear error messages
 
 ## Acceptance Criteria
-- [ ] 
+
+- [x] POST /transactions creates transaction and assigns correct category
+- [x] GET /transactions returns all transactions sorted by date
+- [x] GET /transactions?category=X filters by category
+- [x] GET /transactions/{id} returns specific transaction
+- [x] GET /transactions/{id} returns 404 for non-existent ID
+- [x] DELETE /transactions/{id} removes transaction
+- [x] GET /transactions/summary returns totals by category
+- [x] Invalid inputs return 422 with descriptive errors
+- [x] Empty/whitespace descriptions are rejected
+- [x] Negative amounts are rejected
+- [x] Invalid dates are rejected
+- [x] All tests pass with 97% coverage

TESTING.md

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+# Manual Testing Guide
+
+## Using curl
+
+### Create Transaction
+
+```
+curl -X POST http://localhost:8000/transactions \
+  -H 'Content-Type: application/json' \
+  -d '{"description": "Starbucks coffee", "amount": 4.5, "date": "2023-12-01"}'
+```
+
+### List Transactions
+
+```
+curl http://localhost:8000/transactions
+```
+
+### Filter by Category
+
+```
+curl http://localhost:8000/transactions?category=dining
+```
+
+### Get Transaction by ID
+
+```
+curl http://localhost:8000/transactions/<id>
+```
+
+### Delete Transaction
+
+```
+curl -X DELETE http://localhost:8000/transactions/<id>
+```
+
+### Get Summary
+
+```
+curl http://localhost:8000/transactions/summary
+```
+
+## Using Postman
+
+Import postman_collection.json for ready-to-use requests.

app/__init__.py

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+# app/__init__.py
+# Marks the app directory as a Python package.

app/categorizer.py

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+"""
+Rule-based categorization logic for financial transactions.
+"""
+from typing import Dict, List
+
+CATEGORY_KEYWORDS: Dict[str, List[str]] = {
+    "dining": ["starbucks", "restaurant", "cafe", "mcdonald", "burger", "pizza", "chipotle", "dunkin"],
+    "groceries": ["walmart", "grocery", "supermarket", "whole foods", "kroger", "aldi", "safeway", "costco"],
+    "transportation": ["uber", "lyft", "taxi", "bus", "train", "metro", "shell", "exxon", "chevron", "bp"],
+    "entertainment": ["netflix", "movie", "cinema", "theater", "spotify", "concert", "amc", "hulu", "disney"],
+    "utilities": ["electric", "water", "gas", "utility", "comcast", "verizon", "at&t", "internet", "pg&e"],
+}
+
+DEFAULT_CATEGORY = "other"
+
+
+def categorize_transaction(description: str) -> str:
+    """
+    Categorize a transaction based on its description using keyword matching.
+    Returns one of the 6 categories.
+    """
+    desc = description.lower()
+    for category, keywords in CATEGORY_KEYWORDS.items():
+        for kw in keywords:
+            if kw in desc:
+                return category
+    return DEFAULT_CATEGORY