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CLAUDE.md

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

Project Overview

MoneyPrinter automates YouTube Shorts creation from text topics. It uses Ollama for script generation, TikTok TTS for voiceover, Pexels for stock footage, and moviepy/ImageMagick for video composition. Output: a 9:16 vertical video (output.mp4).

Commands

Setup

cp .env.example .env        # then fill in API keys
uv sync                     # install dependencies
ollama serve                # start Ollama (separate terminal)
ollama pull llama3.1:8b     # pull default model

Run (local)

uv run python Backend/main.py                              # API on :8080
uv run python Backend/worker.py                            # queue worker
python3 -m http.server 3000 --directory Frontend           # frontend on :3000

Run (Docker)

docker compose up --build   # frontend :8001, backend :8080, postgres :5432

Verify

uv run python -m compileall Backend          # syntax check
curl http://localhost:8080/api/models         # API smoke test

Tests

No test suite exists yet. If added, use pytest:

uv run pytest -q                                           # all tests
uv run pytest tests/test_file.py::test_name -q             # single test

Architecture

Video Generation Pipeline (end-to-end flow)

User input (Frontend) → POST /api/generate → generation_jobs (Postgres queue)
  → worker.py claims queued job
  → gpt.py: generate_script() via Ollama
  → gpt.py: get_search_terms() → JSON keywords
  → search.py: Pexels API → download stock clips to temp/
  → tiktokvoice.py: TTS per sentence → MP3 chunks (threaded)
  → video.py: generate_subtitles() → .srt (AssemblyAI or local timestamps)
  → video.py: combine_videos() → concatenate/crop to 9:16
  → video.py: generate_video() → burn subtitles via ImageMagick, merge audio
  → (optional) mix background music from Songs/ at 10% volume
  → (optional) youtube.py: OAuth2 upload
  → output.mp4

Frontend ↔ Backend Communication

  • REST: JSON payloads to Flask endpoints (/api/generate, /api/jobs/:id, /api/jobs/:id/events, /api/jobs/:id/cancel, /api/models, /api/upload-songs)
  • Polling: frontend polls job status and persisted generation events.

Key Backend Modules

File Responsibility
main.py Flask app and queue/job endpoints
worker.py Job consumer that executes generation pipeline
db.py/models.py/repository.py DB engine, schema, queue/event persistence
gpt.py Ollama client: script generation, search terms, YouTube metadata
video.py Video processing: combine clips, burn subtitles, merge audio
search.py Pexels stock video search and download
tiktokvoice.py TikTok TTS API (60+ voices, 300-char chunking, threaded)
youtube.py YouTube upload via Google API with OAuth2
utils.py Path constants, env validation, ImageMagick detection
pipeline.py Reusable generation pipeline used by worker

Frontend

  • index.html: UI with inline CSS, form fields, live log viewer
  • app.js: API client (apiRequest()), job polling UI, localStorage persistence

Runtime Directories

  • temp/: intermediate video/audio files (cleared each generation)
  • subtitles/: generated .srt files (cleared each generation)
  • Songs/: user-uploaded background music MP3s
  • fonts/: subtitle font (bold_font.ttf)

Required Environment Variables

  • TIKTOK_SESSION_ID — TikTok cookie for TTS
  • PEXELS_API_KEY — stock video API
  • IMAGEMAGICK_BINARY — leave empty to auto-detect from PATH

Optional: OLLAMA_BASE_URL, OLLAMA_MODEL, ASSEMBLY_AI_API_KEY, DATABASE_URL

Conventions

  • Python: 4-space indent, snake_case, type hints on all new/modified signatures, pathlib.Path for filesystem ops
  • JS: camelCase, centralized API calls via apiRequest()
  • API responses: {"status": "success|error", ...} with proper HTTP codes
  • Long-running work: database-backed queue and separate worker process
  • Concurrency: multiple jobs can be queued; worker processes them safely via DB locking
  • Update docs/ when setup, env vars, or runtime behavior changes