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Python/Flask backend with pdfplumber parser, Svelte 4 frontend, Docker multi-stage build. Includes category analysis, insights, monthly/weekly charts, subscription audit, and annualised projections. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
50 lines
2.1 KiB
Markdown
50 lines
2.1 KiB
Markdown
Plan
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What we're building
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A Python-based tool with two main capabilities:
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Statement Analysis — Upload AMEX PDF statements, parse transactions, and produce spending insights (categories, trends, totals, etc.)
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Automated Statement Retrieval — A web interface that can log into your AMEX account and download statements automatically.
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Architecture
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Component 1: PDF Statement Parser & Analyser
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Python script using pdfplumber or tabula-py to extract transaction data from AMEX PDF statements
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Parse out: date, description, amount, and (optionally) category
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Produce summary stats: total spend, category breakdown, merchant frequency, monthly trends
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Output as structured data (CSV/JSON) + summary report
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Component 2: Web App with AMEX Login Automation
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A lightweight Flask or FastAPI web app
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File upload endpoint for manual PDF analysis
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Automated login to AMEX using Playwright (headless browser) — this handles the JS-heavy AMEX site, MFA challenges, etc.
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Statement download triggered from the web UI
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Component 3: Dashboard / Reporting
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Either a simple HTML dashboard (React artifact) or generated charts
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Spending by category, merchant, time period
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Key Considerations
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AMEX login automation is fragile — AMEX actively blocks bots, requires MFA, and changes their UI. Playwright is the most resilient approach but you'll likely need to handle MFA interactively (e.g. pause for SMS/email code entry).
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PDF parsing varies by statement format — NZ AMEX statements may differ from US ones. We'll need a sample to tune the parser.
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Security — credentials should never be stored in code; we'll use environment variables or a .env file.
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Proof of Concept Scope
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For the POC, I'll build:
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amex_parser.py — PDF statement parser that extracts transactions into structured data
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amex_analyser.py — Analysis module (totals, categories, trends)
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app.py — Flask web app with upload UI + results display
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amex_scraper.py — Playwright-based AMEX login & statement downloader (interactive MFA)
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Frontend — Clean upload + dashboard UI
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Tech Stack
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Python 3.11+
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pdfplumber for PDF parsing
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Flask for web app
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Playwright for browser automation
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pandas for data analysis
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plotly or chart.js for visualisation |