MoneyPrinterV2/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

MoneyPrinterV2 (MPV2) is a Python 3.12 CLI tool that automates four online workflows:

1. YouTube Shorts — generate video (LLM script → TTS → images → MoviePy composite) and upload via Selenium
2. Twitter/X Bot — generate and post tweets via Selenium
3. Affiliate Marketing — scrape Amazon product info, generate pitch, share on Twitter
4. Local Business Outreach — scrape Google Maps (Go binary), extract emails, send cold outreach via SMTP

There is no web UI, no REST API, no test suite, no CI, and no linting config.

Running the Application

# First-time setup
cp config.example.json config.json   # then fill in values
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# macOS quick setup (auto-configures Ollama, ImageMagick, Firefox profile)
bash scripts/setup_local.sh

# Preflight check (validates services are reachable)
python scripts/preflight_local.py

# Run
python src/main.py

The app must be run from the project root. python src/main.py adds src/ to sys.path, so all imports use bare module names (e.g., from config import *, not from src.config import *).

Architecture

Entry Points

• src/main.py — interactive menu loop (primary)
• src/cron.py — headless runner invoked by the scheduler as a subprocess: python src/cron.py <platform> <account_uuid>

Provider Pattern

Two service categories use a string-based dispatch pattern configured in config.json:

Category	Config key	Options
LLM	ollama_model	Ollama (via ollama Python SDK). If empty, user picks from available models at startup.
Image gen	—	nanobanana2 (Gemini image API)
STT	stt_provider	local_whisper, third_party_assemblyai

LLM always uses the local Ollama server. Image generation always uses Nano Banana 2.

Key Modules

• src/llm_provider.py — unified generate_text(prompt) function using the Ollama Python SDK
• src/config.py — 30+ getter functions, each re-reads config.json on every call (no caching). ROOT_DIR = project root, computed as os.path.dirname(sys.path[0])
• src/cache.py — JSON file persistence in .mp/ directory (accounts, videos, posts, products)
• src/constants.py — menu strings, Selenium selectors (YouTube Studio, X.com, Amazon)
• src/classes/YouTube.py — most complex class; full pipeline: topic → script → metadata → image prompts → images → TTS → subtitles → MoviePy combine → Selenium upload
• src/classes/Twitter.py — Selenium automation against x.com
• src/classes/AFM.py — Amazon scraping + LLM pitch generation
• src/classes/Outreach.py — Google Maps scraper (requires Go) + email sending via yagmail
• src/classes/Tts.py — KittenTTS wrapper

Data Storage

All persistent state lives in .mp/ at the project root as JSON files (youtube.json, twitter.json, afm.json). This directory also serves as scratch space for temporary WAV, PNG, SRT, and MP4 files — non-JSON files are cleaned on each run by rem_temp_files().

Browser Automation

Selenium uses pre-authenticated Firefox profiles (never handles login). The profile path is stored per-account in the cache JSON and also in config.json as a default.

CRON Scheduling

Uses Python's schedule library (in-process, not OS cron). The scheduled job spawns subprocess.run(["python", "src/cron.py", platform, account_id]).

Configuration

All config lives in config.json at the project root. See config.example.json for the full template and docs/Configuration.md for reference. Key external dependencies to configure:

• ImageMagick — required for MoviePy subtitle rendering (imagemagick_path)
• Firefox profile — must be pre-logged-in to target platforms (firefox_profile)
• Ollama — for LLM text generation (via ollama Python SDK)
• Nano Banana 2 — for image generation (Gemini image API)
• Go — only needed for Outreach (Google Maps scraper)

Contributing

PRs go against main. One feature/fix per PR. Open an issue first. Use WIP label for in-progress PRs.