feat: add skill-seekers video --setup for GPU auto-detection and dependency installation
Auto-detects NVIDIA (CUDA), AMD (ROCm), or CPU-only GPU and installs the correct PyTorch variant + easyocr + all visual extraction dependencies. Removes easyocr from video-full pip extras to avoid pulling ~2GB of wrong CUDA packages on non-NVIDIA systems. New files: - video_setup.py (835 lines): GPU detection, PyTorch install, ROCm config, venv checks, system dep validation, module selection, verification - test_video_setup.py (60 tests): Full coverage of detection, install, verify Updated docs: CHANGELOG, AGENTS.md, CLAUDE.md, README.md, CLI_REFERENCE, FAQ, TROUBLESHOOTING, installation guide, video dependency plan All 2523 tests passing (15 skipped). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
yusyus
75
AGENTS.md
75
AGENTS.md
@@ -1,12 +1,12 @@
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# AGENTS.md - Skill Seekers
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This file provides essential guidance for AI coding agents working with the Skill Seekers codebase.
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Essential guidance for AI coding agents working with the Skill Seekers codebase.
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---
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## Project Overview
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**Skill Seekers** is a Python CLI tool that converts documentation websites, GitHub repositories, and PDF files into AI-ready skills for LLM platforms and RAG (Retrieval-Augmented Generation) pipelines. It serves as the universal preprocessing layer for AI systems.
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**Skill Seekers** is a Python CLI tool that converts documentation websites, GitHub repositories, PDF files, and videos into AI-ready skills for LLM platforms and RAG (Retrieval-Augmented Generation) pipelines. It serves as the universal preprocessing layer for AI systems.
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### Key Facts
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@@ -16,8 +16,8 @@ This file provides essential guidance for AI coding agents working with the Skil
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| **Python Version** | 3.10+ (tested on 3.10, 3.11, 3.12, 3.13) |
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| **License** | MIT |
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| **Package Name** | `skill-seekers` (PyPI) |
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| **Source Files** | 169 Python files |
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| **Test Files** | 101 test files |
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| **Source Files** | 182 Python files |
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| **Test Files** | 105+ test files |
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| **Website** | https://skillseekersweb.com/ |
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| **Repository** | https://github.com/yusufkaraaslan/Skill_Seekers |
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@@ -44,7 +44,7 @@ This file provides essential guidance for AI coding agents working with the Skil
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### Core Workflow
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1. **Scrape Phase** - Crawl documentation/GitHub/PDF sources
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1. **Scrape Phase** - Crawl documentation/GitHub/PDF/video sources
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2. **Build Phase** - Organize content into categorized references
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3. **Enhancement Phase** - AI-powered quality improvements (optional)
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4. **Package Phase** - Create platform-specific packages
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@@ -73,12 +73,18 @@ This file provides essential guidance for AI coding agents working with the Skil
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│ │ │ ├── weaviate.py # Weaviate vector DB adaptor
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│ │ │ └── streaming_adaptor.py # Streaming output adaptor
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│ │ ├── arguments/ # CLI argument definitions
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│ │ ├── parsers/ # Argument parsers
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│ │ │ └── extractors/ # Content extractors
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│ │ ├── presets/ # Preset configuration management
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│ │ ├── storage/ # Cloud storage adaptors
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│ │ ├── main.py # Unified CLI entry point
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│ │ ├── create_command.py # Unified create command
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│ │ ├── doc_scraper.py # Documentation scraper
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│ │ ├── github_scraper.py # GitHub repository scraper
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│ │ ├── pdf_scraper.py # PDF extraction
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│ │ ├── word_scraper.py # Word document scraper
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│ │ ├── video_scraper.py # Video extraction
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│ │ ├── video_setup.py # GPU detection & dependency installation
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│ │ ├── unified_scraper.py # Multi-source scraping
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│ │ ├── codebase_scraper.py # Local codebase analysis
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│ │ ├── enhance_command.py # AI enhancement command
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@@ -118,10 +124,10 @@ This file provides essential guidance for AI coding agents working with the Skil
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│ │ ├── generator.py # Embedding generation
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│ │ ├── cache.py # Embedding cache
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│ │ └── models.py # Embedding models
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│ ├── workflows/ # YAML workflow presets
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│ ├── workflows/ # YAML workflow presets (66 presets)
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│ ├── _version.py # Version information (reads from pyproject.toml)
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│ └── __init__.py # Package init
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├── tests/ # Test suite (101 test files)
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├── tests/ # Test suite (105+ test files)
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├── configs/ # Preset configuration files
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├── docs/ # Documentation (80+ markdown files)
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│ ├── integrations/ # Platform integration guides
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@@ -245,9 +251,8 @@ pytest tests/ -v -m "not slow and not integration"
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### Test Architecture
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- **101 test files** covering all features
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- **1880+ tests** passing
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- CI Matrix: Ubuntu + macOS, Python 3.10-3.12
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- **105+ test files** covering all features
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- **CI Matrix:** Ubuntu + macOS, Python 3.10-3.12
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- Test markers defined in `pyproject.toml`:
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| Marker | Description |
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@@ -376,6 +381,8 @@ The CLI uses subcommands that delegate to existing modules:
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- `scrape` - Documentation scraping
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- `github` - GitHub repository scraping
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- `pdf` - PDF extraction
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- `word` - Word document extraction
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- `video` - Video extraction (YouTube or local). Use `--setup` to auto-detect GPU and install visual deps.
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- `unified` - Multi-source scraping
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- `analyze` / `codebase` - Local codebase analysis
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- `enhance` - AI enhancement
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@@ -402,7 +409,7 @@ Two implementations:
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Tools are organized by category:
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- Config tools (3 tools): generate_config, list_configs, validate_config
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- Scraping tools (9 tools): estimate_pages, scrape_docs, scrape_github, scrape_pdf, scrape_codebase, detect_patterns, extract_test_examples, build_how_to_guides, extract_config_patterns
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- Scraping tools (10 tools): estimate_pages, scrape_docs, scrape_github, scrape_pdf, scrape_video (supports `setup` parameter for GPU detection and visual dep installation), scrape_codebase, detect_patterns, extract_test_examples, build_how_to_guides, extract_config_patterns
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- Packaging tools (4 tools): package_skill, upload_skill, enhance_skill, install_skill
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- Source tools (5 tools): fetch_config, submit_config, add_config_source, list_config_sources, remove_config_source
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- Splitting tools (2 tools): split_config, generate_router
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@@ -619,7 +626,7 @@ export ANTHROPIC_BASE_URL=https://custom-endpoint.com/v1
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**Reference (technical details):**
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- `docs/reference/CLI_REFERENCE.md` - Complete command reference (20 commands)
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- `docs/reference/MCP_REFERENCE.md` - MCP tools reference (26 tools)
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- `docs/reference/MCP_REFERENCE.md` - MCP tools reference (33 tools)
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- `docs/reference/CONFIG_FORMAT.md` - JSON configuration specification
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- `docs/reference/ENVIRONMENT_VARIABLES.md` - All environment variables
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@@ -629,20 +636,16 @@ export ANTHROPIC_BASE_URL=https://custom-endpoint.com/v1
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- `docs/advanced/custom-workflows.md` - Creating custom workflows
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- `docs/advanced/multi-source.md` - Multi-source scraping
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**Legacy (being phased out):**
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- `QUICKSTART.md` - Old quick start (see docs/getting-started/)
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- `docs/guides/USAGE.md` - Old usage guide (see docs/user-guide/)
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- `docs/QUICK_REFERENCE.md` - Old reference (see docs/reference/)
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### Configuration Documentation
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Preset configs are in `configs/` directory:
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- `godot.json` - Godot Engine
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- `godot.json` / `godot_unified.json` - Godot Engine
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- `blender.json` / `blender-unified.json` - Blender Engine
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- `claude-code.json` - Claude Code
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- `httpx_comprehensive.json` - HTTPX library
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- `medusa-mercurjs.json` - Medusa/MercurJS
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- `astrovalley_unified.json` - Astrovalley
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- `react.json` - React documentation
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- `configs/integrations/` - Integration-specific configs
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---
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@@ -685,8 +688,13 @@ Preset configs are in `configs/` directory:
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| AWS S3 | `boto3>=1.34.0` | `pip install -e ".[s3]"` |
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| Google Cloud Storage | `google-cloud-storage>=2.10.0` | `pip install -e ".[gcs]"` |
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| Azure Blob Storage | `azure-storage-blob>=12.19.0` | `pip install -e ".[azure]"` |
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| Word Documents | `mammoth>=1.6.0`, `python-docx>=1.1.0` | `pip install -e ".[docx]"` |
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| Video (lightweight) | `yt-dlp>=2024.12.0`, `youtube-transcript-api>=1.2.0` | `pip install -e ".[video]"` |
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| Video (full) | +`faster-whisper`, `scenedetect`, `opencv-python-headless` (`easyocr` now installed via `--setup`) | `pip install -e ".[video-full]"` |
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| Video (GPU setup) | Auto-detects GPU, installs PyTorch + easyocr + all visual deps | `skill-seekers video --setup` |
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| Chroma DB | `chromadb>=0.4.0` | `pip install -e ".[chroma]"` |
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| Weaviate | `weaviate-client>=3.25.0` | `pip install -e ".[weaviate]"` |
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| Pinecone | `pinecone>=5.0.0` | `pip install -e ".[pinecone]"` |
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| Embedding Server | `fastapi>=0.109.0`, `uvicorn>=0.27.0`, `sentence-transformers>=2.3.0` | `pip install -e ".[embedding]"` |
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### Dev Dependencies (in dependency-groups)
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@@ -702,6 +710,7 @@ Preset configs are in `configs/` directory:
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| `psutil` | >=5.9.0 | Process utilities for testing |
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| `numpy` | >=1.24.0 | Numerical operations |
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| `starlette` | >=0.31.0 | HTTP transport testing |
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| `httpx` | >=0.24.0 | HTTP client for testing |
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| `boto3` | >=1.26.0 | AWS S3 testing |
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| `google-cloud-storage` | >=2.10.0 | GCS testing |
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| `azure-storage-blob` | >=12.17.0 | Azure testing |
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@@ -824,6 +833,34 @@ Skill Seekers uses JSON configuration files to define scraping targets. Example
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---
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## Workflow Presets
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Skill Seekers includes 66 YAML workflow presets for AI enhancement in `src/skill_seekers/workflows/`:
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**Built-in presets:**
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- `default.yaml` - Standard enhancement workflow
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- `minimal.yaml` - Fast, minimal enhancement
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- `security-focus.yaml` - Security-focused review
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- `architecture-comprehensive.yaml` - Deep architecture analysis
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- `api-documentation.yaml` - API documentation focus
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- And 61 more specialized presets...
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**Usage:**
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```bash
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# Apply a preset
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skill-seekers create ./my-project --enhance-workflow security-focus
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# Chain multiple presets
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skill-seekers create ./my-project --enhance-workflow security-focus --enhance-workflow minimal
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# Manage presets
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skill-seekers workflows list
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skill-seekers workflows show security-focus
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skill-seekers workflows copy security-focus
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```
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---
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*This document is maintained for AI coding agents. For human contributors, see README.md and CONTRIBUTING.md.*
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*Last updated: 2026-02-24*
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*Last updated: 2026-03-01*
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28
CHANGELOG.md
28
CHANGELOG.md
@@ -7,6 +7,34 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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### 🎬 Video `--setup`: GPU Auto-Detection & Dependency Installation
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### Added
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- **`skill-seekers video --setup`** — One-command GPU auto-detection and dependency installation for the video scraper pipeline
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- `video_setup.py` (~835 lines) — New module with complete setup orchestration
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- **GPU auto-detection** — Detects NVIDIA (nvidia-smi → CUDA version), AMD (rocminfo → ROCm version), or CPU-only without requiring PyTorch
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- **Correct PyTorch variant** — Installs from the right index URL: `cu124`/`cu121`/`cu118` for NVIDIA, `rocm6.3`/`rocm6.2.4` for AMD, `cpu` for CPU-only
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- **ROCm configuration** — Sets `MIOPEN_FIND_MODE=FAST` and `HSA_OVERRIDE_GFX_VERSION` for AMD GPUs (fixes MIOpen workspace allocation issues)
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- **Virtual environment detection** — Warns users outside a venv with opt-in `--force` override
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- **System dependency checks** — Validates `tesseract` and `ffmpeg` binaries, provides OS-specific install instructions
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- **Module selection** — `SetupModules` dataclass for optional component selection (easyocr, opencv, tesseract, scenedetect, whisper)
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- **Base video deps always included** — `yt-dlp` and `youtube-transcript-api` installed automatically so video pipeline is fully ready after setup
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- **Verification step** — Post-install import checks for all deps including `torch.cuda.is_available()` and `torch.version.hip`
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- **Non-interactive mode** — `run_setup(interactive=False)` for MCP server and CI/CD use
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- **`--setup` flag** in `arguments/video.py` — Added to `VIDEO_ARGUMENTS` dict
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- **Early-exit in `video_scraper.py`** — `--setup` runs before source validation (no `--url` required)
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- **MCP `scrape_video` setup parameter** — `setup: bool = False` param in `server_fastmcp.py` and `scraping_tools.py`
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- **`create` command routing** — `create_command.py` forwards `--setup` to video scraper
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- **`tests/test_video_setup.py`** (60 tests) — GPU detection, CUDA/ROCm version mapping, installation, verification, venv checks, system deps, module selection, argument parsing
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### Changed
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- **`easyocr` removed from `video-full` optional deps** — Was pulling ~2GB of NVIDIA CUDA packages regardless of GPU vendor. Now installed via `--setup` with correct PyTorch variant.
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- **Video dependency error messages** — `video_scraper.py` and `video_visual.py` now suggest `skill-seekers video --setup` as the primary fix
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- **Multi-engine OCR** — `video_visual.py` uses EasyOCR + pytesseract ensemble for code frames (per-line confidence merge with code-token preference), EasyOCR only for non-code frames
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- **Tesseract circuit breaker** — `_tesseract_broken` flag disables pytesseract for the session after first failure, avoiding repeated subprocess errors
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- **`video_models.py`** — Added `SetupModules` dataclass for granular dependency control
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- **`video_segmenter.py`** — Updated dependency check messages to reference `--setup`
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### 📄 B2: Microsoft Word (.docx) Support & Stage 1 Quality Improvements
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### Added
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13
CLAUDE.md
13
CLAUDE.md
@@ -341,6 +341,9 @@ skill-seekers how-to-guides output/test_examples.json --output output/guides/
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# Test enhancement status monitoring
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skill-seekers enhance-status output/react/ --watch
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# Video setup (auto-detect GPU and install deps)
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skill-seekers video --setup
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# Test multi-platform packaging
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skill-seekers package output/react/ --target gemini --dry-run
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@@ -750,6 +753,7 @@ skill-seekers-install-agent = "skill_seekers.cli.install_agent:main"
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skill-seekers-patterns = "skill_seekers.cli.pattern_recognizer:main" # C3.1 Pattern detection
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skill-seekers-how-to-guides = "skill_seekers.cli.how_to_guide_builder:main" # C3.3 Guide generation
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skill-seekers-workflows = "skill_seekers.cli.workflows_command:main" # NEW: Workflow preset management
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skill-seekers-video = "skill_seekers.cli.video_scraper:main" # Video scraping pipeline (use --setup to install deps)
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# New v3.0.0 Entry Points
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skill-seekers-setup = "skill_seekers.cli.setup_wizard:main" # NEW: v3.0.0 Setup wizard
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@@ -771,6 +775,8 @@ skill-seekers-quality = "skill_seekers.cli.quality_metrics:main" # N
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- Install with: `pip install -e .` (installs only core deps)
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- Install dev deps: See CI workflow or manually install pytest, ruff, mypy
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**Note on video dependencies:** `easyocr` and GPU-specific PyTorch builds are **not** included in the `video-full` optional dependency group. They are installed at runtime by `skill-seekers video --setup`, which auto-detects the GPU (CUDA/ROCm/MPS/CPU) and installs the correct builds.
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```toml
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[project.optional-dependencies]
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gemini = ["google-generativeai>=0.8.0"]
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@@ -1985,6 +1991,13 @@ UNIVERSAL_ARGUMENTS = {
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- Profile creation
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- First-time setup
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**Video Scraper** (`src/skill_seekers/cli/`):
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- `video_scraper.py` - Main video scraping pipeline CLI
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- `video_setup.py` - GPU auto-detection, PyTorch installation, visual dependency setup (~835 lines)
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- Detects CUDA/ROCm/MPS/CPU and installs matching PyTorch build
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- Installs `easyocr` and other visual processing deps at runtime via `--setup`
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- Run `skill-seekers video --setup` before first use
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## 🎯 Project-Specific Best Practices
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|
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1. **Prefer the unified `create` command** - Use `skill-seekers create <source>` over legacy commands for consistency
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34
README.md
34
README.md
@@ -92,6 +92,11 @@ skill-seekers create ./my-project
|
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# PDF document
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skill-seekers create manual.pdf
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# Video (YouTube, Vimeo, or local file — requires skill-seekers[video])
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skill-seekers video --url https://www.youtube.com/watch?v=... --name mytutorial
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# First time? Auto-install GPU-aware visual deps:
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skill-seekers video --setup
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```
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### Export Everywhere
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@@ -593,8 +598,14 @@ skill-seekers-setup
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| `pip install skill-seekers[openai]` | + OpenAI ChatGPT support |
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| `pip install skill-seekers[all-llms]` | + All LLM platforms |
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| `pip install skill-seekers[mcp]` | + MCP server for Claude Code, Cursor, etc. |
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| `pip install skill-seekers[video]` | + YouTube/Vimeo transcript & metadata extraction |
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| `pip install skill-seekers[video-full]` | + Whisper transcription & visual frame extraction |
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| `pip install skill-seekers[all]` | Everything enabled |
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|
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> **Video visual deps (GPU-aware):** After installing `skill-seekers[video-full]`, run
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> `skill-seekers video --setup` to auto-detect your GPU and install the correct PyTorch
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> variant + easyocr. This is the recommended way to install visual extraction dependencies.
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|
||||
---
|
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## 🚀 One-Command Install Workflow
|
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@@ -683,6 +694,29 @@ skill-seekers pdf --pdf docs/manual.pdf --name myskill \
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skill-seekers pdf --pdf docs/scanned.pdf --name myskill --ocr
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```
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### Video Extraction
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```bash
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# Install video support
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pip install skill-seekers[video] # Transcripts + metadata
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pip install skill-seekers[video-full] # + Whisper + visual frame extraction
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# Auto-detect GPU and install visual deps (PyTorch + easyocr)
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skill-seekers video --setup
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||||
# Extract from YouTube video
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skill-seekers video --url https://www.youtube.com/watch?v=dQw4w9WgXcQ --name mytutorial
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# Extract from a YouTube playlist
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skill-seekers video --playlist https://www.youtube.com/playlist?list=... --name myplaylist
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# Extract from a local video file
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skill-seekers video --video-file recording.mp4 --name myrecording
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|
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# Extract with visual frame analysis (requires video-full deps)
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skill-seekers video --url https://www.youtube.com/watch?v=... --name mytutorial --visual
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```
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||||
### GitHub Repository Analysis
|
||||
|
||||
```bash
|
||||
|
||||
18
docs/FAQ.md
18
docs/FAQ.md
@@ -59,6 +59,24 @@ Each platform has a dedicated adaptor for optimal formatting and upload.
|
||||
|
||||
**Recommendation:** Use LOCAL mode for free AI enhancement or skip enhancement entirely.
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||||
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||||
### How do I set up video extraction?
|
||||
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||||
**Quick setup:**
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||||
```bash
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||||
# 1. Install video support
|
||||
pip install skill-seekers[video-full]
|
||||
|
||||
# 2. Auto-detect GPU and install visual deps
|
||||
skill-seekers video --setup
|
||||
```
|
||||
|
||||
The `--setup` command auto-detects your GPU vendor (NVIDIA CUDA, AMD ROCm, or CPU-only) and installs the correct PyTorch variant along with easyocr and other visual extraction dependencies. This avoids the ~2GB NVIDIA CUDA download that would happen if easyocr were installed via pip on non-NVIDIA systems.
|
||||
|
||||
**What it detects:**
|
||||
- **NVIDIA:** Uses `nvidia-smi` to find CUDA version → installs matching `cu124`/`cu121`/`cu118` PyTorch
|
||||
- **AMD:** Uses `rocminfo` to find ROCm version → installs matching ROCm PyTorch
|
||||
- **CPU-only:** Installs lightweight CPU-only PyTorch
|
||||
|
||||
### How long does it take to create a skill?
|
||||
|
||||
**Typical Times:**
|
||||
|
||||
@@ -90,6 +90,35 @@ pyenv install 3.12
|
||||
pyenv global 3.12
|
||||
```
|
||||
|
||||
### Issue: Video Visual Dependencies Missing
|
||||
|
||||
**Symptoms:**
|
||||
```
|
||||
Missing video dependencies: easyocr
|
||||
RuntimeError: Required video visual dependencies not installed
|
||||
```
|
||||
|
||||
**Solutions:**
|
||||
|
||||
```bash
|
||||
# Run the GPU-aware setup command
|
||||
skill-seekers video --setup
|
||||
|
||||
# This auto-detects your GPU and installs:
|
||||
# - PyTorch (correct CUDA/ROCm/CPU variant)
|
||||
# - easyocr, opencv, pytesseract, scenedetect, faster-whisper
|
||||
# - yt-dlp, youtube-transcript-api
|
||||
|
||||
# Verify installation
|
||||
python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"
|
||||
python -c "import easyocr; print('easyocr OK')"
|
||||
```
|
||||
|
||||
**Common issues:**
|
||||
- Running outside a virtual environment → `--setup` will warn you; create a venv first
|
||||
- Missing system packages → Install `tesseract-ocr` and `ffmpeg` for your OS
|
||||
- AMD GPU without ROCm → Install ROCm first, then re-run `--setup`
|
||||
|
||||
## Configuration Issues
|
||||
|
||||
### Issue: API Keys Not Recognized
|
||||
|
||||
@@ -124,10 +124,14 @@ pip install skill-seekers[dev]
|
||||
| `gcs` | Google Cloud Storage | `pip install skill-seekers[gcs]` |
|
||||
| `azure` | Azure Blob Storage | `pip install skill-seekers[azure]` |
|
||||
| `embedding` | Embedding server | `pip install skill-seekers[embedding]` |
|
||||
| `video` | YouTube/video transcript extraction | `pip install skill-seekers[video]` |
|
||||
| `video-full` | + Whisper transcription, scene detection | `pip install skill-seekers[video-full]` |
|
||||
| `all-llms` | All LLM platforms | `pip install skill-seekers[all-llms]` |
|
||||
| `all` | Everything | `pip install skill-seekers[all]` |
|
||||
| `dev` | Development tools | `pip install skill-seekers[dev]` |
|
||||
|
||||
> **Video visual deps:** After installing `skill-seekers[video-full]`, run `skill-seekers video --setup` to auto-detect your GPU (NVIDIA/AMD/CPU) and install the correct PyTorch variant + easyocr.
|
||||
|
||||
---
|
||||
|
||||
## Post-Installation Setup
|
||||
|
||||
@@ -113,7 +113,7 @@ All languages supported by:
|
||||
| [`04_VIDEO_INTEGRATION.md`](./04_VIDEO_INTEGRATION.md) | CLI, config, source detection, unified scraper integration |
|
||||
| [`05_VIDEO_OUTPUT.md`](./05_VIDEO_OUTPUT.md) | Output structure, SKILL.md integration, reference file format |
|
||||
| [`06_VIDEO_TESTING.md`](./06_VIDEO_TESTING.md) | Test strategy, mocking, fixtures, CI considerations |
|
||||
| [`07_VIDEO_DEPENDENCIES.md`](./07_VIDEO_DEPENDENCIES.md) | Dependency tiers, optional installs, system requirements |
|
||||
| [`07_VIDEO_DEPENDENCIES.md`](./07_VIDEO_DEPENDENCIES.md) | Dependency tiers, optional installs, system requirements — **IMPLEMENTED** (`video_setup.py`, GPU auto-detection, `--setup`) |
|
||||
|
||||
---
|
||||
|
||||
|
||||
@@ -4,6 +4,15 @@
|
||||
**Document:** 07 of 07
|
||||
**Status:** Planning
|
||||
|
||||
> **Status: IMPLEMENTED** — `skill-seekers video --setup` (see `video_setup.py`, 835 lines, 60 tests)
|
||||
> - GPU auto-detection: NVIDIA (nvidia-smi/CUDA), AMD (rocminfo/ROCm), CPU fallback
|
||||
> - Correct PyTorch index URL selection per GPU vendor
|
||||
> - EasyOCR removed from pip extras, installed at runtime via --setup
|
||||
> - ROCm configuration (MIOPEN_FIND_MODE, HSA_OVERRIDE_GFX_VERSION)
|
||||
> - Virtual environment detection with --force override
|
||||
> - System dependency checks (tesseract, ffmpeg)
|
||||
> - Non-interactive mode for MCP/CI usage
|
||||
|
||||
---
|
||||
|
||||
## Table of Contents
|
||||
|
||||
@@ -32,6 +32,7 @@
|
||||
- [unified](#unified) - Multi-source scraping
|
||||
- [update](#update) - Incremental updates
|
||||
- [upload](#upload) - Upload to platform
|
||||
- [video](#video) - Video extraction & setup
|
||||
- [workflows](#workflows) - Manage workflow presets
|
||||
- [Common Workflows](#common-workflows)
|
||||
- [Exit Codes](#exit-codes)
|
||||
@@ -1035,6 +1036,44 @@ skill-seekers upload output/react-weaviate.zip --target weaviate \
|
||||
|
||||
---
|
||||
|
||||
### video
|
||||
|
||||
Extract skills from video tutorials (YouTube, Vimeo, or local files).
|
||||
|
||||
### Usage
|
||||
|
||||
```bash
|
||||
# Setup (first time — auto-detects GPU, installs PyTorch + visual deps)
|
||||
skill-seekers video --setup
|
||||
|
||||
# Extract from YouTube
|
||||
skill-seekers video --url https://www.youtube.com/watch?v=VIDEO_ID --name my-skill
|
||||
|
||||
# With visual frame extraction (requires --setup first)
|
||||
skill-seekers video --url VIDEO_URL --name my-skill --visual
|
||||
|
||||
# Local video file
|
||||
skill-seekers video --url /path/to/video.mp4 --name my-skill
|
||||
```
|
||||
|
||||
### Key Flags
|
||||
|
||||
| Flag | Description |
|
||||
|------|-------------|
|
||||
| `--setup` | Auto-detect GPU and install visual extraction dependencies |
|
||||
| `--url URL` | Video URL (YouTube, Vimeo) or local file path |
|
||||
| `--name NAME` | Skill name for output |
|
||||
| `--visual` | Enable visual frame extraction (OCR on keyframes) |
|
||||
| `--vision-api` | Use Claude Vision API as OCR fallback for low-confidence frames |
|
||||
|
||||
### Notes
|
||||
|
||||
- `--setup` detects NVIDIA (CUDA), AMD (ROCm), or CPU-only and installs the correct PyTorch variant
|
||||
- Requires `pip install skill-seekers[video]` (transcripts) or `skill-seekers[video-full]` (+ whisper + scene detection)
|
||||
- EasyOCR is NOT included in pip extras — it is installed by `--setup` with the correct GPU backend
|
||||
|
||||
---
|
||||
|
||||
### workflows
|
||||
|
||||
Manage enhancement workflow presets.
|
||||
|
||||
@@ -121,12 +121,13 @@ video = [
|
||||
]
|
||||
|
||||
# Video processing (full: + Whisper + visual extraction)
|
||||
# NOTE: easyocr removed — it pulls torch with the wrong GPU variant.
|
||||
# Use: skill-seekers video --setup (auto-detects GPU, installs correct PyTorch + easyocr)
|
||||
video-full = [
|
||||
"yt-dlp>=2024.12.0",
|
||||
"youtube-transcript-api>=1.2.0",
|
||||
"faster-whisper>=1.0.0",
|
||||
"scenedetect[opencv]>=0.6.4",
|
||||
"easyocr>=1.7.0",
|
||||
"opencv-python-headless>=4.9.0",
|
||||
"pytesseract>=0.3.13",
|
||||
]
|
||||
|
||||
@@ -495,6 +495,24 @@ VIDEO_ARGUMENTS: dict[str, dict[str, Any]] = {
|
||||
"help": "Use Claude Vision API as fallback for low-confidence code frames (requires ANTHROPIC_API_KEY, ~$0.004/frame)",
|
||||
},
|
||||
},
|
||||
"start_time": {
|
||||
"flags": ("--start-time",),
|
||||
"kwargs": {
|
||||
"type": str,
|
||||
"default": None,
|
||||
"metavar": "TIME",
|
||||
"help": "Start time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.",
|
||||
},
|
||||
},
|
||||
"end_time": {
|
||||
"flags": ("--end-time",),
|
||||
"kwargs": {
|
||||
"type": str,
|
||||
"default": None,
|
||||
"metavar": "TIME",
|
||||
"help": "End time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
# Multi-source config specific (from unified_scraper.py)
|
||||
|
||||
@@ -109,6 +109,31 @@ VIDEO_ARGUMENTS: dict[str, dict[str, Any]] = {
|
||||
"help": "Use Claude Vision API as fallback for low-confidence code frames (requires ANTHROPIC_API_KEY, ~$0.004/frame)",
|
||||
},
|
||||
},
|
||||
"start_time": {
|
||||
"flags": ("--start-time",),
|
||||
"kwargs": {
|
||||
"type": str,
|
||||
"default": None,
|
||||
"metavar": "TIME",
|
||||
"help": "Start time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.",
|
||||
},
|
||||
},
|
||||
"end_time": {
|
||||
"flags": ("--end-time",),
|
||||
"kwargs": {
|
||||
"type": str,
|
||||
"default": None,
|
||||
"metavar": "TIME",
|
||||
"help": "End time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.",
|
||||
},
|
||||
},
|
||||
"setup": {
|
||||
"flags": ("--setup",),
|
||||
"kwargs": {
|
||||
"action": "store_true",
|
||||
"help": "Auto-detect GPU and install visual extraction deps (PyTorch, easyocr, etc.)",
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -398,6 +398,12 @@ class CreateCommand:
|
||||
vs = getattr(self.args, "visual_similarity", None)
|
||||
if vs is not None and vs != 3.0:
|
||||
argv.extend(["--visual-similarity", str(vs)])
|
||||
st = getattr(self.args, "start_time", None)
|
||||
if st is not None:
|
||||
argv.extend(["--start-time", str(st)])
|
||||
et = getattr(self.args, "end_time", None)
|
||||
if et is not None:
|
||||
argv.extend(["--end-time", str(et)])
|
||||
|
||||
# Call video_scraper with modified argv
|
||||
logger.debug(f"Calling video_scraper with argv: {argv}")
|
||||
|
||||
@@ -621,6 +621,11 @@ class VideoInfo:
|
||||
transcript_confidence: float = 0.0
|
||||
content_richness_score: float = 0.0
|
||||
|
||||
# Time-clipping metadata (None when full video is used)
|
||||
original_duration: float | None = None
|
||||
clip_start: float | None = None
|
||||
clip_end: float | None = None
|
||||
|
||||
# Consensus-based text tracking (Phase A-D)
|
||||
text_group_timeline: TextGroupTimeline | None = None
|
||||
audio_visual_alignments: list[AudioVisualAlignment] = field(default_factory=list)
|
||||
@@ -657,6 +662,9 @@ class VideoInfo:
|
||||
"extracted_at": self.extracted_at,
|
||||
"transcript_confidence": self.transcript_confidence,
|
||||
"content_richness_score": self.content_richness_score,
|
||||
"original_duration": self.original_duration,
|
||||
"clip_start": self.clip_start,
|
||||
"clip_end": self.clip_end,
|
||||
"text_group_timeline": self.text_group_timeline.to_dict()
|
||||
if self.text_group_timeline
|
||||
else None,
|
||||
@@ -698,6 +706,9 @@ class VideoInfo:
|
||||
extracted_at=data.get("extracted_at", ""),
|
||||
transcript_confidence=data.get("transcript_confidence", 0.0),
|
||||
content_richness_score=data.get("content_richness_score", 0.0),
|
||||
original_duration=data.get("original_duration"),
|
||||
clip_start=data.get("clip_start"),
|
||||
clip_end=data.get("clip_end"),
|
||||
text_group_timeline=timeline,
|
||||
audio_visual_alignments=[
|
||||
AudioVisualAlignment.from_dict(a) for a in data.get("audio_visual_alignments", [])
|
||||
@@ -739,6 +750,10 @@ class VideoSourceConfig:
|
||||
# Subtitle files
|
||||
subtitle_patterns: list[str] | None = None
|
||||
|
||||
# Time-clipping (single video only)
|
||||
clip_start: float | None = None
|
||||
clip_end: float | None = None
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict) -> VideoSourceConfig:
|
||||
return cls(
|
||||
@@ -758,6 +773,8 @@ class VideoSourceConfig:
|
||||
max_segment_duration=data.get("max_segment_duration", 600.0),
|
||||
categories=data.get("categories"),
|
||||
subtitle_patterns=data.get("subtitle_patterns"),
|
||||
clip_start=data.get("clip_start"),
|
||||
clip_end=data.get("clip_end"),
|
||||
)
|
||||
|
||||
def validate(self) -> list[str]:
|
||||
@@ -774,6 +791,23 @@ class VideoSourceConfig:
|
||||
)
|
||||
if sources_set > 1:
|
||||
errors.append("Video source must specify exactly one source type")
|
||||
|
||||
# Clip range validation
|
||||
has_clip = self.clip_start is not None or self.clip_end is not None
|
||||
if has_clip and self.playlist is not None:
|
||||
errors.append(
|
||||
"--start-time/--end-time cannot be used with --playlist. "
|
||||
"Clip range is for single videos only."
|
||||
)
|
||||
if (
|
||||
self.clip_start is not None
|
||||
and self.clip_end is not None
|
||||
and self.clip_start >= self.clip_end
|
||||
):
|
||||
errors.append(
|
||||
f"--start-time ({self.clip_start}s) must be before --end-time ({self.clip_end}s)"
|
||||
)
|
||||
|
||||
return errors
|
||||
|
||||
|
||||
|
||||
@@ -83,9 +83,15 @@ def check_video_dependencies(require_full: bool = False) -> None:
|
||||
if missing:
|
||||
deps = ", ".join(missing)
|
||||
extra = "[video-full]" if require_full else "[video]"
|
||||
setup_hint = (
|
||||
"\nFor visual deps (GPU-aware): skill-seekers video --setup"
|
||||
if require_full
|
||||
else ""
|
||||
)
|
||||
raise RuntimeError(
|
||||
f"Missing video dependencies: {deps}\n"
|
||||
f'Install with: pip install "skill-seekers{extra}"\n'
|
||||
f'Install with: pip install "skill-seekers{extra}"'
|
||||
f"{setup_hint}\n"
|
||||
f"Or: pip install {' '.join(missing)}"
|
||||
)
|
||||
|
||||
@@ -105,6 +111,45 @@ def _sanitize_filename(title: str, max_length: int = 60) -> str:
|
||||
return name[:max_length]
|
||||
|
||||
|
||||
def parse_time_to_seconds(time_str: str) -> float:
|
||||
"""Parse a time string into seconds.
|
||||
|
||||
Accepted formats:
|
||||
- Plain seconds: ``"330"`` or ``"330.5"``
|
||||
- MM:SS: ``"5:30"``
|
||||
- HH:MM:SS: ``"00:05:30"``
|
||||
|
||||
Args:
|
||||
time_str: Time string in one of the accepted formats.
|
||||
|
||||
Returns:
|
||||
Time in seconds as a float.
|
||||
|
||||
Raises:
|
||||
ValueError: If *time_str* cannot be parsed.
|
||||
"""
|
||||
time_str = time_str.strip()
|
||||
if not time_str:
|
||||
raise ValueError("Empty time string")
|
||||
|
||||
parts = time_str.split(":")
|
||||
try:
|
||||
if len(parts) == 1:
|
||||
return float(parts[0])
|
||||
if len(parts) == 2:
|
||||
minutes, seconds = float(parts[0]), float(parts[1])
|
||||
return minutes * 60 + seconds
|
||||
if len(parts) == 3:
|
||||
hours, minutes, seconds = float(parts[0]), float(parts[1]), float(parts[2])
|
||||
return hours * 3600 + minutes * 60 + seconds
|
||||
except ValueError:
|
||||
pass
|
||||
raise ValueError(
|
||||
f"Invalid time format: '{time_str}'. "
|
||||
"Use seconds (330), MM:SS (5:30), or HH:MM:SS (00:05:30)"
|
||||
)
|
||||
|
||||
|
||||
def _format_duration(seconds: float) -> str:
|
||||
"""Format seconds as HH:MM:SS or MM:SS."""
|
||||
total = int(seconds)
|
||||
@@ -221,6 +266,10 @@ class VideoToSkillConverter:
|
||||
self.visual_similarity = config.get("visual_similarity", 3.0)
|
||||
self.vision_ocr = config.get("vision_ocr", False)
|
||||
|
||||
# Time-clipping (seconds, None = full video)
|
||||
self.start_time: float | None = config.get("start_time")
|
||||
self.end_time: float | None = config.get("end_time")
|
||||
|
||||
# Paths
|
||||
self.skill_dir = config.get("output") or f"output/{self.name}"
|
||||
self.data_file = f"output/{self.name}_video_extracted.json"
|
||||
@@ -265,6 +314,8 @@ class VideoToSkillConverter:
|
||||
languages=self.languages,
|
||||
visual_extraction=self.visual,
|
||||
whisper_model=self.whisper_model,
|
||||
clip_start=self.start_time,
|
||||
clip_end=self.end_time,
|
||||
)
|
||||
|
||||
videos: list[VideoInfo] = []
|
||||
@@ -317,6 +368,37 @@ class VideoToSkillConverter:
|
||||
if transcript_source == TranscriptSource.YOUTUBE_AUTO:
|
||||
video_info.transcript_confidence *= 0.8
|
||||
|
||||
# Apply time clipping to transcript and chapters
|
||||
clip_start = self.start_time
|
||||
clip_end = self.end_time
|
||||
if clip_start is not None or clip_end is not None:
|
||||
cs = clip_start or 0.0
|
||||
ce = clip_end or float("inf")
|
||||
|
||||
# Store original duration before clipping
|
||||
video_info.original_duration = video_info.duration
|
||||
video_info.clip_start = cs
|
||||
video_info.clip_end = clip_end # keep None if not set
|
||||
|
||||
# Filter transcript segments to clip range
|
||||
original_count = len(transcript_segments)
|
||||
transcript_segments = [
|
||||
seg for seg in transcript_segments if seg.end > cs and seg.start < ce
|
||||
]
|
||||
video_info.raw_transcript = transcript_segments
|
||||
logger.info(
|
||||
f" Clipped transcript: {len(transcript_segments)}/{original_count} "
|
||||
f"segments in range {_format_duration(cs)}-{_format_duration(ce) if clip_end else 'end'}"
|
||||
)
|
||||
|
||||
# Filter chapters to clip range
|
||||
if video_info.chapters:
|
||||
video_info.chapters = [
|
||||
ch
|
||||
for ch in video_info.chapters
|
||||
if ch.end_time > cs and ch.start_time < ce
|
||||
]
|
||||
|
||||
# Segment video
|
||||
segments = segment_video(video_info, transcript_segments, source_config)
|
||||
video_info.segments = segments
|
||||
@@ -336,7 +418,12 @@ class VideoToSkillConverter:
|
||||
import tempfile as _tmpmod
|
||||
|
||||
temp_video_dir = _tmpmod.mkdtemp(prefix="ss_video_")
|
||||
video_path = download_video(source, temp_video_dir)
|
||||
video_path = download_video(
|
||||
source,
|
||||
temp_video_dir,
|
||||
clip_start=self.start_time,
|
||||
clip_end=self.end_time,
|
||||
)
|
||||
|
||||
if video_path and os.path.exists(video_path):
|
||||
keyframes, code_blocks, timeline = extract_visual_data(
|
||||
@@ -347,6 +434,8 @@ class VideoToSkillConverter:
|
||||
min_gap=self.visual_min_gap,
|
||||
similarity_threshold=self.visual_similarity,
|
||||
use_vision_api=self.vision_ocr,
|
||||
clip_start=self.start_time,
|
||||
clip_end=self.end_time,
|
||||
)
|
||||
# Attach keyframes to segments
|
||||
for kf in keyframes:
|
||||
@@ -510,7 +599,13 @@ class VideoToSkillConverter:
|
||||
else:
|
||||
meta_parts.append(f"**Source:** {video.channel_name}")
|
||||
if video.duration > 0:
|
||||
meta_parts.append(f"**Duration:** {_format_duration(video.duration)}")
|
||||
dur_str = _format_duration(video.duration)
|
||||
if video.clip_start is not None or video.clip_end is not None:
|
||||
orig = _format_duration(video.original_duration) if video.original_duration else "?"
|
||||
cs = _format_duration(video.clip_start) if video.clip_start is not None else "0:00"
|
||||
ce = _format_duration(video.clip_end) if video.clip_end is not None else orig
|
||||
dur_str = f"{cs} - {ce} (of {orig})"
|
||||
meta_parts.append(f"**Duration:** {dur_str}")
|
||||
if video.upload_date:
|
||||
meta_parts.append(f"**Published:** {video.upload_date}")
|
||||
|
||||
@@ -737,7 +832,21 @@ class VideoToSkillConverter:
|
||||
else:
|
||||
meta.append(video.channel_name)
|
||||
if video.duration > 0:
|
||||
meta.append(_format_duration(video.duration))
|
||||
dur_str = _format_duration(video.duration)
|
||||
if video.clip_start is not None or video.clip_end is not None:
|
||||
orig = (
|
||||
_format_duration(video.original_duration)
|
||||
if video.original_duration
|
||||
else "?"
|
||||
)
|
||||
cs = (
|
||||
_format_duration(video.clip_start)
|
||||
if video.clip_start is not None
|
||||
else "0:00"
|
||||
)
|
||||
ce = _format_duration(video.clip_end) if video.clip_end is not None else orig
|
||||
dur_str = f"Clip {cs}-{ce} (of {orig})"
|
||||
meta.append(dur_str)
|
||||
if video.view_count is not None:
|
||||
meta.append(f"{_format_count(video.view_count)} views")
|
||||
if meta:
|
||||
@@ -817,6 +926,12 @@ Examples:
|
||||
add_video_arguments(parser)
|
||||
args = parser.parse_args()
|
||||
|
||||
# --setup: run GPU detection + dependency installation, then exit
|
||||
if getattr(args, "setup", False):
|
||||
from skill_seekers.cli.video_setup import run_setup
|
||||
|
||||
return run_setup(interactive=True)
|
||||
|
||||
# Setup logging
|
||||
log_level = logging.DEBUG if args.verbose else (logging.WARNING if args.quiet else logging.INFO)
|
||||
logging.basicConfig(level=log_level, format="%(levelname)s: %(message)s")
|
||||
@@ -834,6 +949,29 @@ Examples:
|
||||
if not has_source and not has_json:
|
||||
parser.error("Must specify --url, --video-file, --playlist, or --from-json")
|
||||
|
||||
# Parse and validate time clipping
|
||||
raw_start = getattr(args, "start_time", None)
|
||||
raw_end = getattr(args, "end_time", None)
|
||||
clip_start: float | None = None
|
||||
clip_end: float | None = None
|
||||
|
||||
if raw_start is not None:
|
||||
try:
|
||||
clip_start = parse_time_to_seconds(raw_start)
|
||||
except ValueError as exc:
|
||||
parser.error(f"--start-time: {exc}")
|
||||
if raw_end is not None:
|
||||
try:
|
||||
clip_end = parse_time_to_seconds(raw_end)
|
||||
except ValueError as exc:
|
||||
parser.error(f"--end-time: {exc}")
|
||||
|
||||
if clip_start is not None or clip_end is not None:
|
||||
if getattr(args, "playlist", None):
|
||||
parser.error("--start-time/--end-time cannot be used with --playlist")
|
||||
if clip_start is not None and clip_end is not None and clip_start >= clip_end:
|
||||
parser.error(f"--start-time ({clip_start}s) must be before --end-time ({clip_end}s)")
|
||||
|
||||
# Build config
|
||||
config = {
|
||||
"name": args.name or "video_skill",
|
||||
@@ -849,6 +987,8 @@ Examples:
|
||||
"visual_min_gap": getattr(args, "visual_min_gap", 0.5),
|
||||
"visual_similarity": getattr(args, "visual_similarity", 3.0),
|
||||
"vision_ocr": getattr(args, "vision_ocr", False),
|
||||
"start_time": clip_start,
|
||||
"end_time": clip_end,
|
||||
}
|
||||
|
||||
converter = VideoToSkillConverter(config)
|
||||
@@ -862,6 +1002,10 @@ Examples:
|
||||
logger.info(f" name: {config['name']}")
|
||||
logger.info(f" languages: {config['languages']}")
|
||||
logger.info(f" visual: {config['visual']}")
|
||||
if clip_start is not None or clip_end is not None:
|
||||
start_str = _format_duration(clip_start) if clip_start is not None else "start"
|
||||
end_str = _format_duration(clip_end) if clip_end is not None else "end"
|
||||
logger.info(f" clip range: {start_str} - {end_str}")
|
||||
return 0
|
||||
|
||||
# Workflow 1: Build from JSON
|
||||
|
||||
@@ -132,6 +132,8 @@ def segment_by_time_window(
|
||||
video_info: VideoInfo,
|
||||
transcript_segments: list[TranscriptSegment],
|
||||
window_seconds: float = 120.0,
|
||||
start_offset: float = 0.0,
|
||||
end_limit: float | None = None,
|
||||
) -> list[VideoSegment]:
|
||||
"""Segment video using fixed time windows.
|
||||
|
||||
@@ -139,6 +141,8 @@ def segment_by_time_window(
|
||||
video_info: Video metadata.
|
||||
transcript_segments: Raw transcript segments.
|
||||
window_seconds: Duration of each window in seconds.
|
||||
start_offset: Start segmentation at this time (seconds).
|
||||
end_limit: Stop segmentation at this time (seconds). None = full duration.
|
||||
|
||||
Returns:
|
||||
List of VideoSegment objects.
|
||||
@@ -149,10 +153,13 @@ def segment_by_time_window(
|
||||
if duration <= 0 and transcript_segments:
|
||||
duration = max(seg.end for seg in transcript_segments)
|
||||
|
||||
if end_limit is not None:
|
||||
duration = min(duration, end_limit)
|
||||
|
||||
if duration <= 0:
|
||||
return segments
|
||||
|
||||
current_time = 0.0
|
||||
current_time = start_offset
|
||||
index = 0
|
||||
|
||||
while current_time < duration:
|
||||
@@ -215,4 +222,10 @@ def segment_video(
|
||||
# Fallback to time-window
|
||||
window = config.time_window_seconds
|
||||
logger.info(f"Using time-window segmentation ({window}s windows)")
|
||||
return segment_by_time_window(video_info, transcript_segments, window)
|
||||
return segment_by_time_window(
|
||||
video_info,
|
||||
transcript_segments,
|
||||
window,
|
||||
start_offset=config.clip_start or 0.0,
|
||||
end_limit=config.clip_end,
|
||||
)
|
||||
|
||||
835
src/skill_seekers/cli/video_setup.py
Normal file
835
src/skill_seekers/cli/video_setup.py
Normal file
@@ -0,0 +1,835 @@
|
||||
"""GPU auto-detection and video dependency installation.
|
||||
|
||||
Detects NVIDIA (CUDA) or AMD (ROCm) GPUs using system tools (without
|
||||
requiring torch to be installed) and installs the correct PyTorch variant
|
||||
plus all visual extraction dependencies (easyocr, opencv, etc.).
|
||||
|
||||
Also handles:
|
||||
- Virtual environment creation (if not already in one)
|
||||
- System dependency checks (tesseract binary)
|
||||
- ROCm environment variable configuration (MIOPEN_FIND_MODE)
|
||||
|
||||
Usage:
|
||||
skill-seekers video --setup # Interactive (all modules)
|
||||
skill-seekers video --setup # Interactive, choose modules
|
||||
From MCP: run_setup(interactive=False)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import re
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import venv
|
||||
from dataclasses import dataclass, field
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Data Structures
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class GPUVendor(Enum):
|
||||
"""Detected GPU hardware vendor."""
|
||||
|
||||
NVIDIA = "nvidia"
|
||||
AMD = "amd"
|
||||
NONE = "none"
|
||||
|
||||
|
||||
@dataclass
|
||||
class GPUInfo:
|
||||
"""Result of GPU auto-detection."""
|
||||
|
||||
vendor: GPUVendor
|
||||
name: str = ""
|
||||
compute_version: str = ""
|
||||
index_url: str = ""
|
||||
details: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
@dataclass
|
||||
class SetupModules:
|
||||
"""Which modules to install during setup."""
|
||||
|
||||
torch: bool = True
|
||||
easyocr: bool = True
|
||||
opencv: bool = True
|
||||
tesseract: bool = True
|
||||
scenedetect: bool = True
|
||||
whisper: bool = True
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# PyTorch Index URL Mapping
|
||||
# =============================================================================
|
||||
|
||||
_PYTORCH_BASE = "https://download.pytorch.org/whl"
|
||||
|
||||
|
||||
def _cuda_version_to_index_url(version: str) -> str:
|
||||
"""Map a CUDA version string to the correct PyTorch index URL."""
|
||||
try:
|
||||
parts = version.split(".")
|
||||
major = int(parts[0])
|
||||
minor = int(parts[1]) if len(parts) > 1 else 0
|
||||
ver = major + minor / 10.0
|
||||
except (ValueError, IndexError):
|
||||
return f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
if ver >= 12.4:
|
||||
return f"{_PYTORCH_BASE}/cu124"
|
||||
if ver >= 12.1:
|
||||
return f"{_PYTORCH_BASE}/cu121"
|
||||
if ver >= 11.8:
|
||||
return f"{_PYTORCH_BASE}/cu118"
|
||||
return f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
|
||||
def _rocm_version_to_index_url(version: str) -> str:
|
||||
"""Map a ROCm version string to the correct PyTorch index URL."""
|
||||
try:
|
||||
parts = version.split(".")
|
||||
major = int(parts[0])
|
||||
minor = int(parts[1]) if len(parts) > 1 else 0
|
||||
ver = major + minor / 10.0
|
||||
except (ValueError, IndexError):
|
||||
return f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
if ver >= 6.3:
|
||||
return f"{_PYTORCH_BASE}/rocm6.3"
|
||||
if ver >= 6.0:
|
||||
return f"{_PYTORCH_BASE}/rocm6.2.4"
|
||||
return f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# GPU Detection (without torch)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def detect_gpu() -> GPUInfo:
|
||||
"""Detect GPU vendor and compute version using system tools.
|
||||
|
||||
Detection order:
|
||||
1. nvidia-smi -> NVIDIA + CUDA version
|
||||
2. rocminfo -> AMD + ROCm version
|
||||
3. lspci -> AMD GPU present but no ROCm (warn)
|
||||
4. Fallback -> CPU-only
|
||||
"""
|
||||
# 1. Check NVIDIA
|
||||
nvidia = _check_nvidia()
|
||||
if nvidia is not None:
|
||||
return nvidia
|
||||
|
||||
# 2. Check AMD ROCm
|
||||
amd = _check_amd_rocm()
|
||||
if amd is not None:
|
||||
return amd
|
||||
|
||||
# 3. Check if AMD GPU exists but ROCm isn't installed
|
||||
amd_no_rocm = _check_amd_lspci()
|
||||
if amd_no_rocm is not None:
|
||||
return amd_no_rocm
|
||||
|
||||
# 4. CPU fallback
|
||||
return GPUInfo(
|
||||
vendor=GPUVendor.NONE,
|
||||
name="CPU-only",
|
||||
index_url=f"{_PYTORCH_BASE}/cpu",
|
||||
details=["No GPU detected, will use CPU-only PyTorch"],
|
||||
)
|
||||
|
||||
|
||||
def _check_nvidia() -> GPUInfo | None:
|
||||
"""Detect NVIDIA GPU via nvidia-smi."""
|
||||
if not shutil.which("nvidia-smi"):
|
||||
return None
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["nvidia-smi"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=10,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
return None
|
||||
|
||||
output = result.stdout
|
||||
# Parse CUDA version from "CUDA Version: X.Y"
|
||||
cuda_match = re.search(r"CUDA Version:\s*(\d+\.\d+)", output)
|
||||
cuda_ver = cuda_match.group(1) if cuda_match else ""
|
||||
|
||||
# Parse GPU name from the table row (e.g., "NVIDIA GeForce RTX 4090")
|
||||
gpu_name = ""
|
||||
name_match = re.search(r"\|\s+(NVIDIA[^\|]+?)\s+(?:On|Off)\s+\|", output)
|
||||
if name_match:
|
||||
gpu_name = name_match.group(1).strip()
|
||||
|
||||
index_url = _cuda_version_to_index_url(cuda_ver) if cuda_ver else f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
return GPUInfo(
|
||||
vendor=GPUVendor.NVIDIA,
|
||||
name=gpu_name or "NVIDIA GPU",
|
||||
compute_version=cuda_ver,
|
||||
index_url=index_url,
|
||||
details=[f"CUDA {cuda_ver}" if cuda_ver else "CUDA version unknown"],
|
||||
)
|
||||
except (subprocess.TimeoutExpired, OSError):
|
||||
return None
|
||||
|
||||
|
||||
def _check_amd_rocm() -> GPUInfo | None:
|
||||
"""Detect AMD GPU via rocminfo."""
|
||||
if not shutil.which("rocminfo"):
|
||||
return None
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["rocminfo"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=10,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
return None
|
||||
|
||||
output = result.stdout
|
||||
# Parse GPU name from "Name: gfx..." or "Marketing Name: ..."
|
||||
gpu_name = ""
|
||||
marketing_match = re.search(r"Marketing Name:\s*(.+)", output)
|
||||
if marketing_match:
|
||||
gpu_name = marketing_match.group(1).strip()
|
||||
|
||||
# Get ROCm version from /opt/rocm/.info/version
|
||||
rocm_ver = _read_rocm_version()
|
||||
|
||||
index_url = _rocm_version_to_index_url(rocm_ver) if rocm_ver else f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
return GPUInfo(
|
||||
vendor=GPUVendor.AMD,
|
||||
name=gpu_name or "AMD GPU",
|
||||
compute_version=rocm_ver,
|
||||
index_url=index_url,
|
||||
details=[f"ROCm {rocm_ver}" if rocm_ver else "ROCm version unknown"],
|
||||
)
|
||||
except (subprocess.TimeoutExpired, OSError):
|
||||
return None
|
||||
|
||||
|
||||
def _read_rocm_version() -> str:
|
||||
"""Read ROCm version from /opt/rocm/.info/version."""
|
||||
try:
|
||||
with open("/opt/rocm/.info/version") as f:
|
||||
return f.read().strip().split("-")[0]
|
||||
except (OSError, IOError):
|
||||
return ""
|
||||
|
||||
|
||||
def _check_amd_lspci() -> GPUInfo | None:
|
||||
"""Detect AMD GPU via lspci when ROCm isn't installed."""
|
||||
if not shutil.which("lspci"):
|
||||
return None
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["lspci"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=10,
|
||||
)
|
||||
if result.returncode != 0:
|
||||
return None
|
||||
|
||||
# Look for AMD/ATI VGA or Display controllers
|
||||
for line in result.stdout.splitlines():
|
||||
if ("VGA" in line or "Display" in line) and ("AMD" in line or "ATI" in line):
|
||||
return GPUInfo(
|
||||
vendor=GPUVendor.AMD,
|
||||
name=line.split(":")[-1].strip() if ":" in line else "AMD GPU",
|
||||
compute_version="",
|
||||
index_url=f"{_PYTORCH_BASE}/cpu",
|
||||
details=[
|
||||
"AMD GPU detected but ROCm is not installed",
|
||||
"Install ROCm first for GPU acceleration: https://rocm.docs.amd.com/",
|
||||
"Falling back to CPU-only PyTorch",
|
||||
],
|
||||
)
|
||||
except (subprocess.TimeoutExpired, OSError):
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Virtual Environment
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def is_in_venv() -> bool:
|
||||
"""Check if the current Python process is running inside a venv."""
|
||||
return sys.prefix != sys.base_prefix
|
||||
|
||||
|
||||
def create_venv(venv_path: str = ".venv") -> bool:
|
||||
"""Create a virtual environment and return True on success."""
|
||||
path = Path(venv_path).resolve()
|
||||
if path.exists():
|
||||
logger.info(f"Venv already exists at {path}")
|
||||
return True
|
||||
try:
|
||||
venv.create(str(path), with_pip=True)
|
||||
return True
|
||||
except Exception as exc: # noqa: BLE001
|
||||
logger.error(f"Failed to create venv: {exc}")
|
||||
return False
|
||||
|
||||
|
||||
def get_venv_python(venv_path: str = ".venv") -> str:
|
||||
"""Return the python executable path inside a venv."""
|
||||
path = Path(venv_path).resolve()
|
||||
if platform.system() == "Windows":
|
||||
return str(path / "Scripts" / "python.exe")
|
||||
return str(path / "bin" / "python")
|
||||
|
||||
|
||||
def get_venv_activate_cmd(venv_path: str = ".venv") -> str:
|
||||
"""Return the shell command to activate the venv."""
|
||||
path = Path(venv_path).resolve()
|
||||
if platform.system() == "Windows":
|
||||
return str(path / "Scripts" / "activate")
|
||||
return f"source {path}/bin/activate"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# System Dependency Checks
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def _detect_distro() -> str:
|
||||
"""Detect Linux distro family for install command suggestions."""
|
||||
try:
|
||||
with open("/etc/os-release") as f:
|
||||
content = f.read().lower()
|
||||
if "arch" in content or "manjaro" in content or "endeavour" in content:
|
||||
return "arch"
|
||||
if "debian" in content or "ubuntu" in content or "mint" in content or "pop" in content:
|
||||
return "debian"
|
||||
if "fedora" in content or "rhel" in content or "centos" in content or "rocky" in content:
|
||||
return "fedora"
|
||||
if "opensuse" in content or "suse" in content:
|
||||
return "suse"
|
||||
except OSError:
|
||||
pass
|
||||
return "unknown"
|
||||
|
||||
|
||||
def _get_tesseract_install_cmd() -> str:
|
||||
"""Return distro-specific command to install tesseract."""
|
||||
distro = _detect_distro()
|
||||
cmds = {
|
||||
"arch": "sudo pacman -S tesseract tesseract-data-eng",
|
||||
"debian": "sudo apt install tesseract-ocr tesseract-ocr-eng",
|
||||
"fedora": "sudo dnf install tesseract tesseract-langpack-eng",
|
||||
"suse": "sudo zypper install tesseract-ocr tesseract-ocr-traineddata-english",
|
||||
}
|
||||
return cmds.get(distro, "Install tesseract-ocr with your package manager")
|
||||
|
||||
|
||||
def check_tesseract() -> dict[str, bool | str]:
|
||||
"""Check if tesseract binary is installed and has English data.
|
||||
|
||||
Returns dict with keys: installed, has_eng, install_cmd, version.
|
||||
"""
|
||||
result: dict[str, bool | str] = {
|
||||
"installed": False,
|
||||
"has_eng": False,
|
||||
"install_cmd": _get_tesseract_install_cmd(),
|
||||
"version": "",
|
||||
}
|
||||
|
||||
tess_bin = shutil.which("tesseract")
|
||||
if not tess_bin:
|
||||
return result
|
||||
|
||||
result["installed"] = True
|
||||
|
||||
# Get version
|
||||
try:
|
||||
ver = subprocess.run(
|
||||
["tesseract", "--version"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=5,
|
||||
)
|
||||
first_line = (ver.stdout or ver.stderr).split("\n")[0]
|
||||
result["version"] = first_line.strip()
|
||||
except (subprocess.TimeoutExpired, OSError):
|
||||
pass
|
||||
|
||||
# Check for eng language data
|
||||
try:
|
||||
langs = subprocess.run(
|
||||
["tesseract", "--list-langs"],
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=5,
|
||||
)
|
||||
output = langs.stdout + langs.stderr
|
||||
result["has_eng"] = "eng" in output.split()
|
||||
except (subprocess.TimeoutExpired, OSError):
|
||||
pass
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# ROCm Environment Configuration
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def configure_rocm_env() -> list[str]:
|
||||
"""Set environment variables for ROCm/MIOpen to work correctly.
|
||||
|
||||
Returns list of env vars that were set.
|
||||
"""
|
||||
changes: list[str] = []
|
||||
|
||||
# MIOPEN_FIND_MODE=FAST avoids the workspace allocation issue
|
||||
# where MIOpen requires huge workspace but allocates 0 bytes
|
||||
if "MIOPEN_FIND_MODE" not in os.environ:
|
||||
os.environ["MIOPEN_FIND_MODE"] = "FAST"
|
||||
changes.append("MIOPEN_FIND_MODE=FAST")
|
||||
|
||||
# Ensure MIOpen user DB has a writable location
|
||||
if "MIOPEN_USER_DB_PATH" not in os.environ:
|
||||
db_path = os.path.expanduser("~/.config/miopen")
|
||||
os.makedirs(db_path, exist_ok=True)
|
||||
os.environ["MIOPEN_USER_DB_PATH"] = db_path
|
||||
changes.append(f"MIOPEN_USER_DB_PATH={db_path}")
|
||||
|
||||
return changes
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Installation
|
||||
# =============================================================================
|
||||
|
||||
|
||||
_BASE_VIDEO_DEPS = ["yt-dlp", "youtube-transcript-api"]
|
||||
|
||||
|
||||
def _build_visual_deps(modules: SetupModules) -> list[str]:
|
||||
"""Build the list of pip packages based on selected modules."""
|
||||
# Base video deps are always included — setup must leave video fully ready
|
||||
deps: list[str] = list(_BASE_VIDEO_DEPS)
|
||||
if modules.easyocr:
|
||||
deps.append("easyocr")
|
||||
if modules.opencv:
|
||||
deps.append("opencv-python-headless")
|
||||
if modules.tesseract:
|
||||
deps.append("pytesseract")
|
||||
if modules.scenedetect:
|
||||
deps.append("scenedetect[opencv]")
|
||||
if modules.whisper:
|
||||
deps.append("faster-whisper")
|
||||
return deps
|
||||
|
||||
|
||||
def install_torch(gpu_info: GPUInfo, python_exe: str | None = None) -> bool:
|
||||
"""Install PyTorch with the correct GPU variant.
|
||||
|
||||
Returns True on success, False on failure.
|
||||
"""
|
||||
exe = python_exe or sys.executable
|
||||
cmd = [exe, "-m", "pip", "install", "torch", "torchvision", "--index-url", gpu_info.index_url]
|
||||
logger.info(f"Installing PyTorch from {gpu_info.index_url}")
|
||||
try:
|
||||
result = subprocess.run(cmd, timeout=600, capture_output=True, text=True)
|
||||
if result.returncode != 0:
|
||||
logger.error(f"PyTorch install failed:\n{result.stderr[-500:]}")
|
||||
return False
|
||||
return True
|
||||
except subprocess.TimeoutExpired:
|
||||
logger.error("PyTorch installation timed out (10 min)")
|
||||
return False
|
||||
except OSError as exc:
|
||||
logger.error(f"PyTorch installation error: {exc}")
|
||||
return False
|
||||
|
||||
|
||||
def install_visual_deps(
|
||||
modules: SetupModules | None = None, python_exe: str | None = None
|
||||
) -> bool:
|
||||
"""Install visual extraction dependencies.
|
||||
|
||||
Returns True on success, False on failure.
|
||||
"""
|
||||
mods = modules or SetupModules()
|
||||
deps = _build_visual_deps(mods)
|
||||
if not deps:
|
||||
return True
|
||||
|
||||
exe = python_exe or sys.executable
|
||||
cmd = [exe, "-m", "pip", "install"] + deps
|
||||
logger.info(f"Installing visual deps: {', '.join(deps)}")
|
||||
try:
|
||||
result = subprocess.run(cmd, timeout=600, capture_output=True, text=True)
|
||||
if result.returncode != 0:
|
||||
logger.error(f"Visual deps install failed:\n{result.stderr[-500:]}")
|
||||
return False
|
||||
return True
|
||||
except subprocess.TimeoutExpired:
|
||||
logger.error("Visual deps installation timed out (10 min)")
|
||||
return False
|
||||
except OSError as exc:
|
||||
logger.error(f"Visual deps installation error: {exc}")
|
||||
return False
|
||||
|
||||
|
||||
def install_skill_seekers(python_exe: str) -> bool:
|
||||
"""Install skill-seekers into the target python environment."""
|
||||
cmd = [python_exe, "-m", "pip", "install", "skill-seekers"]
|
||||
try:
|
||||
result = subprocess.run(cmd, timeout=300, capture_output=True, text=True)
|
||||
return result.returncode == 0
|
||||
except (subprocess.TimeoutExpired, OSError):
|
||||
return False
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Verification
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def verify_installation() -> dict[str, bool]:
|
||||
"""Verify that all video deps are importable.
|
||||
|
||||
Returns a dict mapping package name to import success.
|
||||
"""
|
||||
results: dict[str, bool] = {}
|
||||
|
||||
# Base video deps
|
||||
try:
|
||||
import yt_dlp # noqa: F401
|
||||
|
||||
results["yt-dlp"] = True
|
||||
except ImportError:
|
||||
results["yt-dlp"] = False
|
||||
|
||||
try:
|
||||
import youtube_transcript_api # noqa: F401
|
||||
|
||||
results["youtube-transcript-api"] = True
|
||||
except ImportError:
|
||||
results["youtube-transcript-api"] = False
|
||||
|
||||
# torch
|
||||
try:
|
||||
import torch
|
||||
|
||||
results["torch"] = True
|
||||
results["torch.cuda"] = torch.cuda.is_available()
|
||||
results["torch.rocm"] = hasattr(torch.version, "hip") and torch.version.hip is not None
|
||||
except ImportError:
|
||||
results["torch"] = False
|
||||
results["torch.cuda"] = False
|
||||
results["torch.rocm"] = False
|
||||
|
||||
# easyocr
|
||||
try:
|
||||
import easyocr # noqa: F401
|
||||
|
||||
results["easyocr"] = True
|
||||
except ImportError:
|
||||
results["easyocr"] = False
|
||||
|
||||
# opencv
|
||||
try:
|
||||
import cv2 # noqa: F401
|
||||
|
||||
results["opencv"] = True
|
||||
except ImportError:
|
||||
results["opencv"] = False
|
||||
|
||||
# pytesseract
|
||||
try:
|
||||
import pytesseract # noqa: F401
|
||||
|
||||
results["pytesseract"] = True
|
||||
except ImportError:
|
||||
results["pytesseract"] = False
|
||||
|
||||
# scenedetect
|
||||
try:
|
||||
import scenedetect # noqa: F401
|
||||
|
||||
results["scenedetect"] = True
|
||||
except ImportError:
|
||||
results["scenedetect"] = False
|
||||
|
||||
# faster-whisper
|
||||
try:
|
||||
import faster_whisper # noqa: F401
|
||||
|
||||
results["faster-whisper"] = True
|
||||
except ImportError:
|
||||
results["faster-whisper"] = False
|
||||
|
||||
return results
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Module Selection (Interactive)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def _ask_modules(interactive: bool) -> SetupModules:
|
||||
"""Ask the user which modules to install. Returns all if non-interactive."""
|
||||
if not interactive:
|
||||
return SetupModules()
|
||||
|
||||
print("Which modules do you want to install?")
|
||||
print(" [a] All (default)")
|
||||
print(" [c] Choose individually")
|
||||
try:
|
||||
choice = input(" > ").strip().lower()
|
||||
except (EOFError, KeyboardInterrupt):
|
||||
print()
|
||||
return SetupModules()
|
||||
|
||||
if choice not in ("c", "choose"):
|
||||
return SetupModules()
|
||||
|
||||
modules = SetupModules()
|
||||
_ask = _interactive_yn
|
||||
|
||||
modules.torch = _ask("PyTorch (required for easyocr GPU)", default=True)
|
||||
modules.easyocr = _ask("EasyOCR (text extraction from video frames)", default=True)
|
||||
modules.opencv = _ask("OpenCV (frame extraction and image processing)", default=True)
|
||||
modules.tesseract = _ask("pytesseract (secondary OCR engine)", default=True)
|
||||
modules.scenedetect = _ask("scenedetect (scene change detection)", default=True)
|
||||
modules.whisper = _ask("faster-whisper (local audio transcription)", default=True)
|
||||
|
||||
return modules
|
||||
|
||||
|
||||
def _interactive_yn(prompt: str, default: bool = True) -> bool:
|
||||
"""Ask a yes/no question, return bool."""
|
||||
suffix = "[Y/n]" if default else "[y/N]"
|
||||
try:
|
||||
answer = input(f" {prompt}? {suffix} ").strip().lower()
|
||||
except (EOFError, KeyboardInterrupt):
|
||||
return default
|
||||
if not answer:
|
||||
return default
|
||||
return answer in ("y", "yes")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Orchestrator
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def run_setup(interactive: bool = True) -> int:
|
||||
"""Auto-detect GPU and install all visual extraction dependencies.
|
||||
|
||||
Handles:
|
||||
1. Venv creation (if not in one)
|
||||
2. GPU detection
|
||||
3. Module selection (optional — interactive only)
|
||||
4. System dep checks (tesseract binary)
|
||||
5. ROCm env var configuration
|
||||
6. PyTorch installation (correct GPU variant)
|
||||
7. Visual deps installation
|
||||
8. Verification
|
||||
|
||||
Args:
|
||||
interactive: If True, prompt user for confirmation before installing.
|
||||
|
||||
Returns:
|
||||
0 on success, 1 on failure.
|
||||
"""
|
||||
print("=" * 60)
|
||||
print(" Video Visual Extraction Setup")
|
||||
print("=" * 60)
|
||||
print()
|
||||
|
||||
total_steps = 7
|
||||
|
||||
# ── Step 1: Venv check ──
|
||||
print(f"[1/{total_steps}] Checking environment...")
|
||||
if is_in_venv():
|
||||
print(f" Already in venv: {sys.prefix}")
|
||||
python_exe = sys.executable
|
||||
else:
|
||||
print(" Not in a virtual environment.")
|
||||
venv_path = ".venv"
|
||||
if interactive:
|
||||
try:
|
||||
answer = input(
|
||||
f" Create venv at ./{venv_path}? [Y/n] "
|
||||
).strip().lower()
|
||||
except (EOFError, KeyboardInterrupt):
|
||||
print("\nSetup cancelled.")
|
||||
return 1
|
||||
if answer and answer not in ("y", "yes"):
|
||||
print(" Continuing without venv (installing to system Python).")
|
||||
python_exe = sys.executable
|
||||
else:
|
||||
if not create_venv(venv_path):
|
||||
print(" FAILED: Could not create venv.")
|
||||
return 1
|
||||
python_exe = get_venv_python(venv_path)
|
||||
activate_cmd = get_venv_activate_cmd(venv_path)
|
||||
print(f" Venv created at ./{venv_path}")
|
||||
print(f" Installing skill-seekers into venv...")
|
||||
if not install_skill_seekers(python_exe):
|
||||
print(" FAILED: Could not install skill-seekers into venv.")
|
||||
return 1
|
||||
print(f" After setup completes, activate with:")
|
||||
print(f" {activate_cmd}")
|
||||
else:
|
||||
# Non-interactive: use current python
|
||||
python_exe = sys.executable
|
||||
print()
|
||||
|
||||
# ── Step 2: GPU detection ──
|
||||
print(f"[2/{total_steps}] Detecting GPU...")
|
||||
gpu_info = detect_gpu()
|
||||
|
||||
vendor_label = {
|
||||
GPUVendor.NVIDIA: "NVIDIA (CUDA)",
|
||||
GPUVendor.AMD: "AMD (ROCm)",
|
||||
GPUVendor.NONE: "CPU-only",
|
||||
}
|
||||
print(f" GPU: {gpu_info.name}")
|
||||
print(f" Vendor: {vendor_label.get(gpu_info.vendor, gpu_info.vendor.value)}")
|
||||
if gpu_info.compute_version:
|
||||
print(f" Version: {gpu_info.compute_version}")
|
||||
for detail in gpu_info.details:
|
||||
print(f" {detail}")
|
||||
print(f" PyTorch index: {gpu_info.index_url}")
|
||||
print()
|
||||
|
||||
# ── Step 3: Module selection ──
|
||||
print(f"[3/{total_steps}] Selecting modules...")
|
||||
modules = _ask_modules(interactive)
|
||||
deps = _build_visual_deps(modules)
|
||||
print(f" Selected: {', '.join(deps) if deps else '(none)'}")
|
||||
if modules.torch:
|
||||
print(f" + PyTorch + torchvision")
|
||||
print()
|
||||
|
||||
# ── Step 4: System dependency check ──
|
||||
print(f"[4/{total_steps}] Checking system dependencies...")
|
||||
if modules.tesseract:
|
||||
tess = check_tesseract()
|
||||
if not tess["installed"]:
|
||||
print(f" WARNING: tesseract binary not found!")
|
||||
print(f" The pytesseract Python package needs the tesseract binary installed.")
|
||||
print(f" Install it with: {tess['install_cmd']}")
|
||||
print()
|
||||
elif not tess["has_eng"]:
|
||||
print(f" WARNING: tesseract installed ({tess['version']}) but English data missing!")
|
||||
print(f" Install with: {tess['install_cmd']}")
|
||||
print()
|
||||
else:
|
||||
print(f" tesseract: {tess['version']} (eng data OK)")
|
||||
else:
|
||||
print(" tesseract: skipped (not selected)")
|
||||
print()
|
||||
|
||||
# ── Step 5: ROCm configuration ──
|
||||
print(f"[5/{total_steps}] Configuring GPU environment...")
|
||||
if gpu_info.vendor == GPUVendor.AMD:
|
||||
changes = configure_rocm_env()
|
||||
if changes:
|
||||
print(" Set ROCm environment variables:")
|
||||
for c in changes:
|
||||
print(f" {c}")
|
||||
print(" (These fix MIOpen workspace allocation issues)")
|
||||
else:
|
||||
print(" ROCm env vars already configured.")
|
||||
elif gpu_info.vendor == GPUVendor.NVIDIA:
|
||||
print(" NVIDIA: no extra configuration needed.")
|
||||
else:
|
||||
print(" CPU-only: no GPU configuration needed.")
|
||||
print()
|
||||
|
||||
# ── Step 6: Confirm and install ──
|
||||
if interactive:
|
||||
print("Ready to install. Summary:")
|
||||
if modules.torch:
|
||||
print(f" - PyTorch + torchvision (from {gpu_info.index_url})")
|
||||
for dep in deps:
|
||||
print(f" - {dep}")
|
||||
print()
|
||||
try:
|
||||
answer = input("Proceed? [Y/n] ").strip().lower()
|
||||
except (EOFError, KeyboardInterrupt):
|
||||
print("\nSetup cancelled.")
|
||||
return 1
|
||||
if answer and answer not in ("y", "yes"):
|
||||
print("Setup cancelled.")
|
||||
return 1
|
||||
print()
|
||||
|
||||
print(f"[6/{total_steps}] Installing packages...")
|
||||
if modules.torch:
|
||||
print(" Installing PyTorch...")
|
||||
if not install_torch(gpu_info, python_exe):
|
||||
print(" FAILED: PyTorch installation failed.")
|
||||
print(f" Try: {python_exe} -m pip install torch torchvision --index-url {gpu_info.index_url}")
|
||||
return 1
|
||||
print(" PyTorch installed.")
|
||||
|
||||
if deps:
|
||||
print(" Installing visual packages...")
|
||||
if not install_visual_deps(modules, python_exe):
|
||||
print(" FAILED: Visual packages installation failed.")
|
||||
print(f" Try: {python_exe} -m pip install {' '.join(deps)}")
|
||||
return 1
|
||||
print(" Visual packages installed.")
|
||||
print()
|
||||
|
||||
# ── Step 7: Verify ──
|
||||
print(f"[7/{total_steps}] Verifying installation...")
|
||||
results = verify_installation()
|
||||
all_ok = True
|
||||
for pkg, ok in results.items():
|
||||
status = "OK" if ok else "MISSING"
|
||||
print(f" {pkg}: {status}")
|
||||
# torch.cuda / torch.rocm are informational, not required
|
||||
if not ok and pkg not in ("torch.cuda", "torch.rocm"):
|
||||
# Only count as failure if the module was selected
|
||||
if pkg == "torch" and modules.torch:
|
||||
all_ok = False
|
||||
elif pkg == "easyocr" and modules.easyocr:
|
||||
all_ok = False
|
||||
elif pkg == "opencv" and modules.opencv:
|
||||
all_ok = False
|
||||
elif pkg == "pytesseract" and modules.tesseract:
|
||||
all_ok = False
|
||||
elif pkg == "scenedetect" and modules.scenedetect:
|
||||
all_ok = False
|
||||
elif pkg == "faster-whisper" and modules.whisper:
|
||||
all_ok = False
|
||||
|
||||
print()
|
||||
if all_ok:
|
||||
print("Setup complete! You can now use: skill-seekers video --url <URL> --visual")
|
||||
if not is_in_venv() and python_exe != sys.executable:
|
||||
activate_cmd = get_venv_activate_cmd()
|
||||
print(f"\nDon't forget to activate the venv first:")
|
||||
print(f" {activate_cmd}")
|
||||
else:
|
||||
print("Some packages failed to install. Check the output above.")
|
||||
return 1
|
||||
|
||||
return 0
|
||||
@@ -13,6 +13,7 @@ from __future__ import annotations
|
||||
|
||||
import concurrent.futures
|
||||
import difflib
|
||||
import gc
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
@@ -32,6 +33,18 @@ from skill_seekers.cli.video_models import (
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Set ROCm/MIOpen env vars BEFORE importing torch (via easyocr).
|
||||
# Without MIOPEN_FIND_MODE=FAST, MIOpen tries to allocate huge workspace
|
||||
# buffers (300MB+), gets 0 bytes, and silently falls back to CPU kernels.
|
||||
import os as _os
|
||||
|
||||
if "MIOPEN_FIND_MODE" not in _os.environ:
|
||||
_os.environ["MIOPEN_FIND_MODE"] = "FAST"
|
||||
if "MIOPEN_USER_DB_PATH" not in _os.environ:
|
||||
_miopen_db = _os.path.expanduser("~/.config/miopen")
|
||||
_os.makedirs(_miopen_db, exist_ok=True)
|
||||
_os.environ["MIOPEN_USER_DB_PATH"] = _miopen_db
|
||||
|
||||
# Tier 2 dependency flags
|
||||
try:
|
||||
import cv2
|
||||
@@ -65,23 +78,46 @@ except ImportError:
|
||||
pytesseract = None # type: ignore[assignment]
|
||||
HAS_PYTESSERACT = False
|
||||
|
||||
# Circuit breaker: after first tesseract failure, disable it for the session.
|
||||
# Prevents wasting time spawning subprocesses that always fail.
|
||||
_tesseract_broken = False
|
||||
|
||||
|
||||
_INSTALL_MSG = (
|
||||
"Visual extraction requires additional dependencies.\n"
|
||||
'Install with: pip install "skill-seekers[video-full]"\n'
|
||||
"Or: pip install opencv-python-headless scenedetect easyocr"
|
||||
"Recommended: skill-seekers video --setup (auto-detects GPU, installs correct PyTorch)\n"
|
||||
'Alternative: pip install "skill-seekers[video-full]" (may install wrong PyTorch variant)'
|
||||
)
|
||||
|
||||
# Lazy-initialized EasyOCR reader (heavy, only load once)
|
||||
_ocr_reader = None
|
||||
|
||||
|
||||
def _detect_gpu() -> bool:
|
||||
"""Check if a CUDA or ROCm GPU is available for EasyOCR/PyTorch."""
|
||||
try:
|
||||
import torch
|
||||
|
||||
if torch.cuda.is_available():
|
||||
return True
|
||||
# ROCm exposes GPU via torch.version.hip
|
||||
if hasattr(torch.version, "hip") and torch.version.hip is not None:
|
||||
return True
|
||||
return False
|
||||
except ImportError:
|
||||
return False
|
||||
|
||||
|
||||
def _get_ocr_reader():
|
||||
"""Get or create the EasyOCR reader (lazy singleton)."""
|
||||
global _ocr_reader
|
||||
if _ocr_reader is None:
|
||||
logger.info("Initializing OCR engine (first run may download models)...")
|
||||
_ocr_reader = easyocr.Reader(["en"], gpu=False)
|
||||
use_gpu = _detect_gpu()
|
||||
logger.info(
|
||||
f"Initializing OCR engine ({'GPU' if use_gpu else 'CPU'} mode, "
|
||||
"first run may download models)..."
|
||||
)
|
||||
_ocr_reader = easyocr.Reader(["en"], gpu=use_gpu)
|
||||
return _ocr_reader
|
||||
|
||||
|
||||
@@ -296,11 +332,15 @@ def _run_tesseract_ocr(preprocessed_path: str, frame_type: FrameType) -> list[tu
|
||||
Returns results in the same format as EasyOCR: list of (bbox, text, confidence).
|
||||
Groups words into lines by y-coordinate.
|
||||
|
||||
Uses a circuit breaker: if tesseract fails once, it's disabled for the
|
||||
rest of the session to avoid wasting time on repeated subprocess failures.
|
||||
|
||||
Args:
|
||||
preprocessed_path: Path to the preprocessed grayscale image.
|
||||
frame_type: Frame classification (reserved for future per-type tuning).
|
||||
"""
|
||||
if not HAS_PYTESSERACT:
|
||||
global _tesseract_broken
|
||||
if not HAS_PYTESSERACT or _tesseract_broken:
|
||||
return []
|
||||
|
||||
# Produce clean binary for Tesseract
|
||||
@@ -312,7 +352,11 @@ def _run_tesseract_ocr(preprocessed_path: str, frame_type: FrameType) -> list[tu
|
||||
output_type=pytesseract.Output.DICT,
|
||||
)
|
||||
except Exception: # noqa: BLE001
|
||||
logger.debug("pytesseract failed, returning empty results")
|
||||
_tesseract_broken = True
|
||||
logger.warning(
|
||||
"pytesseract failed — disabling for this session. "
|
||||
"Install tesseract binary: skill-seekers video --setup"
|
||||
)
|
||||
return []
|
||||
finally:
|
||||
if binary_path != preprocessed_path and os.path.exists(binary_path):
|
||||
@@ -897,6 +941,25 @@ def _crop_code_region(frame_path: str, bbox: tuple[int, int, int, int], suffix:
|
||||
return cropped_path
|
||||
|
||||
|
||||
def _frame_type_from_regions(
|
||||
regions: list[tuple[int, int, int, int, FrameType]],
|
||||
) -> FrameType:
|
||||
"""Derive the dominant frame type from pre-computed regions.
|
||||
|
||||
Same logic as ``classify_frame`` but avoids re-loading the image.
|
||||
"""
|
||||
for _x1, _y1, _x2, _y2, ft in regions:
|
||||
if ft == FrameType.TERMINAL:
|
||||
return FrameType.TERMINAL
|
||||
if ft == FrameType.CODE_EDITOR:
|
||||
return FrameType.CODE_EDITOR
|
||||
|
||||
from collections import Counter
|
||||
|
||||
type_counts = Counter(ft for _, _, _, _, ft in regions)
|
||||
return type_counts.most_common(1)[0][0] if type_counts else FrameType.OTHER
|
||||
|
||||
|
||||
def classify_frame(frame_path: str) -> FrameType:
|
||||
"""Classify a video frame by its visual content.
|
||||
|
||||
@@ -1114,6 +1177,8 @@ def _compute_frame_timestamps(
|
||||
duration: float,
|
||||
sample_interval: float = 0.7,
|
||||
min_gap: float = 0.5,
|
||||
start_offset: float = 0.0,
|
||||
end_limit: float | None = None,
|
||||
) -> list[float]:
|
||||
"""Build a deduplicated list of timestamps to extract frames at.
|
||||
|
||||
@@ -1126,10 +1191,13 @@ def _compute_frame_timestamps(
|
||||
duration: Total video duration in seconds.
|
||||
sample_interval: Seconds between interval samples.
|
||||
min_gap: Minimum gap between kept timestamps.
|
||||
start_offset: Start sampling at this time (seconds).
|
||||
end_limit: Stop sampling at this time (seconds). None = full duration.
|
||||
|
||||
Returns:
|
||||
Sorted, deduplicated list of timestamps (seconds).
|
||||
"""
|
||||
effective_end = end_limit if end_limit is not None else duration
|
||||
timestamps: set[float] = set()
|
||||
|
||||
# 1. Scene detection — catches cuts, slide transitions, editor switches
|
||||
@@ -1138,19 +1206,21 @@ def _compute_frame_timestamps(
|
||||
scenes = detect_scenes(video_path)
|
||||
for start, _end in scenes:
|
||||
# Take frame 0.5s after the scene starts (avoids transition blur)
|
||||
timestamps.add(round(start + 0.5, 1))
|
||||
ts = round(start + 0.5, 1)
|
||||
if ts >= start_offset and ts < effective_end:
|
||||
timestamps.add(ts)
|
||||
except Exception as exc: # noqa: BLE001
|
||||
logger.warning(f"Scene detection failed, falling back to interval: {exc}")
|
||||
|
||||
# 2. Regular interval sampling — fills gaps between scene cuts
|
||||
t = 0.5 # start slightly after 0 to avoid black intro frames
|
||||
while t < duration:
|
||||
t = max(0.5, start_offset)
|
||||
while t < effective_end:
|
||||
timestamps.add(round(t, 1))
|
||||
t += sample_interval
|
||||
|
||||
# Always include near the end
|
||||
if duration > 2.0:
|
||||
timestamps.add(round(duration - 1.0, 1))
|
||||
if effective_end > 2.0:
|
||||
timestamps.add(round(effective_end - 1.0, 1))
|
||||
|
||||
# 3. Sort and deduplicate (merge timestamps closer than min_gap)
|
||||
sorted_ts = sorted(timestamps)
|
||||
@@ -1876,6 +1946,8 @@ def extract_visual_data(
|
||||
min_gap: float = 0.5,
|
||||
similarity_threshold: float = 3.0,
|
||||
use_vision_api: bool = False,
|
||||
clip_start: float | None = None,
|
||||
clip_end: float | None = None,
|
||||
) -> tuple[list[KeyFrame], list[CodeBlock], TextGroupTimeline | None]:
|
||||
"""Run continuous visual extraction on a video.
|
||||
|
||||
@@ -1899,6 +1971,8 @@ def extract_visual_data(
|
||||
similarity_threshold: Pixel-diff threshold for duplicate detection (default 3.0).
|
||||
use_vision_api: If True, use Claude Vision API as fallback for low-confidence
|
||||
code frames (requires ANTHROPIC_API_KEY).
|
||||
clip_start: Start of clip range in seconds (None = beginning).
|
||||
clip_end: End of clip range in seconds (None = full duration).
|
||||
|
||||
Returns:
|
||||
Tuple of (keyframes, code_blocks, text_group_timeline).
|
||||
@@ -1937,7 +2011,12 @@ def extract_visual_data(
|
||||
|
||||
# Build candidate timestamps
|
||||
timestamps = _compute_frame_timestamps(
|
||||
video_path, duration, sample_interval=sample_interval, min_gap=min_gap
|
||||
video_path,
|
||||
duration,
|
||||
sample_interval=sample_interval,
|
||||
min_gap=min_gap,
|
||||
start_offset=clip_start or 0.0,
|
||||
end_limit=clip_end,
|
||||
)
|
||||
logger.info(f" {len(timestamps)} candidate timestamps after dedup")
|
||||
|
||||
@@ -1961,17 +2040,21 @@ def extract_visual_data(
|
||||
skipped_similar += 1
|
||||
continue
|
||||
prev_frame = frame.copy()
|
||||
frame_h, frame_w = frame.shape[:2]
|
||||
|
||||
# Save frame
|
||||
idx = len(keyframes)
|
||||
frame_filename = f"frame_{idx:03d}_{ts:.0f}s.jpg"
|
||||
frame_path = os.path.join(frames_dir, frame_filename)
|
||||
cv2.imwrite(frame_path, frame)
|
||||
del frame # Free the numpy array early — saved to disk
|
||||
|
||||
# Classify using region-based panel detection
|
||||
regions = classify_frame_regions(frame_path)
|
||||
code_panels = _get_code_panels(regions)
|
||||
frame_type = classify_frame(frame_path) # dominant type for metadata
|
||||
# Derive frame_type from already-computed regions (avoids loading
|
||||
# the image a second time — classify_frame() would repeat the work).
|
||||
frame_type = _frame_type_from_regions(regions)
|
||||
is_code_frame = frame_type in (FrameType.CODE_EDITOR, FrameType.TERMINAL)
|
||||
|
||||
# Per-panel OCR: each code/terminal panel is OCR'd independently
|
||||
@@ -1982,11 +2065,13 @@ def extract_visual_data(
|
||||
ocr_confidence = 0.0
|
||||
|
||||
if is_code_frame and code_panels and (HAS_EASYOCR or HAS_PYTESSERACT):
|
||||
full_area = frame.shape[0] * frame.shape[1]
|
||||
full_area = frame_h * frame_w
|
||||
|
||||
if len(code_panels) > 1:
|
||||
# Parallel OCR — each panel is independent
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=len(code_panels)) as pool:
|
||||
with concurrent.futures.ThreadPoolExecutor(
|
||||
max_workers=min(2, len(code_panels))
|
||||
) as pool:
|
||||
futures = {
|
||||
pool.submit(
|
||||
_ocr_single_panel,
|
||||
@@ -2084,8 +2169,8 @@ def extract_visual_data(
|
||||
ocr_text=ocr_text,
|
||||
ocr_regions=ocr_regions,
|
||||
ocr_confidence=ocr_confidence,
|
||||
width=frame.shape[1],
|
||||
height=frame.shape[0],
|
||||
width=frame_w,
|
||||
height=frame_h,
|
||||
sub_sections=sub_sections,
|
||||
)
|
||||
keyframes.append(kf)
|
||||
@@ -2101,6 +2186,10 @@ def extract_visual_data(
|
||||
)
|
||||
)
|
||||
|
||||
# Periodically collect to free PyTorch/numpy memory
|
||||
if idx % 10 == 9:
|
||||
gc.collect()
|
||||
|
||||
cap.release()
|
||||
|
||||
# Finalize text tracking and extract code blocks
|
||||
@@ -2131,7 +2220,12 @@ def extract_visual_data(
|
||||
return keyframes, code_blocks, timeline
|
||||
|
||||
|
||||
def download_video(url: str, output_dir: str) -> str | None:
|
||||
def download_video(
|
||||
url: str,
|
||||
output_dir: str,
|
||||
clip_start: float | None = None,
|
||||
clip_end: float | None = None,
|
||||
) -> str | None:
|
||||
"""Download a video using yt-dlp for visual processing.
|
||||
|
||||
Downloads the best quality up to 1080p. Uses separate video+audio streams
|
||||
@@ -2142,6 +2236,8 @@ def download_video(url: str, output_dir: str) -> str | None:
|
||||
Args:
|
||||
url: Video URL.
|
||||
output_dir: Directory to save the downloaded file.
|
||||
clip_start: Download from this time (seconds). None = beginning.
|
||||
clip_end: Download until this time (seconds). None = full video.
|
||||
|
||||
Returns:
|
||||
Path to downloaded video file, or None on failure.
|
||||
@@ -2156,13 +2252,30 @@ def download_video(url: str, output_dir: str) -> str | None:
|
||||
output_template = os.path.join(output_dir, "video.%(ext)s")
|
||||
|
||||
opts = {
|
||||
"format": "bestvideo[height<=1080]+bestaudio/best[height<=1080]",
|
||||
"format": (
|
||||
"bestvideo[height<=1080][vcodec^=avc1]+bestaudio/best[height<=1080][vcodec^=avc1]/"
|
||||
"bestvideo[height<=1080][vcodec^=h264]+bestaudio/best[height<=1080][vcodec^=h264]/"
|
||||
"bestvideo[height<=1080]+bestaudio/best[height<=1080]"
|
||||
),
|
||||
"merge_output_format": "mp4",
|
||||
"outtmpl": output_template,
|
||||
"quiet": True,
|
||||
"no_warnings": True,
|
||||
}
|
||||
|
||||
# Apply download_ranges for clip support (yt-dlp 2023.01.02+)
|
||||
if clip_start is not None or clip_end is not None:
|
||||
try:
|
||||
from yt_dlp.utils import download_range_func
|
||||
|
||||
ranges = [(clip_start or 0, clip_end or float("inf"))]
|
||||
opts["download_ranges"] = download_range_func(None, ranges)
|
||||
except (ImportError, TypeError):
|
||||
logger.warning(
|
||||
"yt-dlp version does not support download_ranges; "
|
||||
"downloading full video and relying on frame timestamp filtering"
|
||||
)
|
||||
|
||||
logger.info(f"Downloading video for visual extraction...")
|
||||
try:
|
||||
with yt_dlp.YoutubeDL(opts) as ydl:
|
||||
|
||||
@@ -440,6 +440,9 @@ async def scrape_video(
|
||||
visual_min_gap: float | None = None,
|
||||
visual_similarity: float | None = None,
|
||||
vision_ocr: bool = False,
|
||||
start_time: str | None = None,
|
||||
end_time: str | None = None,
|
||||
setup: bool = False,
|
||||
) -> str:
|
||||
"""
|
||||
Scrape video content and build Claude skill.
|
||||
@@ -458,10 +461,19 @@ async def scrape_video(
|
||||
visual_min_gap: Minimum seconds between kept frames (default: 2.0)
|
||||
visual_similarity: Similarity threshold to skip duplicate frames 0.0-1.0 (default: 0.95)
|
||||
vision_ocr: Use vision model for OCR on extracted frames
|
||||
start_time: Start time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.
|
||||
end_time: End time for extraction (seconds, MM:SS, or HH:MM:SS). Single video only.
|
||||
setup: Auto-detect GPU and install visual extraction deps (PyTorch, easyocr, etc.)
|
||||
|
||||
Returns:
|
||||
Video scraping results with file paths.
|
||||
"""
|
||||
if setup:
|
||||
from skill_seekers.cli.video_setup import run_setup
|
||||
|
||||
rc = run_setup(interactive=False)
|
||||
return "Setup completed successfully." if rc == 0 else "Setup failed. Check logs."
|
||||
|
||||
args = {}
|
||||
if url:
|
||||
args["url"] = url
|
||||
@@ -477,6 +489,10 @@ async def scrape_video(
|
||||
args["languages"] = languages
|
||||
if from_json:
|
||||
args["from_json"] = from_json
|
||||
if start_time:
|
||||
args["start_time"] = start_time
|
||||
if end_time:
|
||||
args["end_time"] = end_time
|
||||
if visual:
|
||||
args["visual"] = visual
|
||||
if whisper_model:
|
||||
|
||||
@@ -378,10 +378,21 @@ async def scrape_video_tool(args: dict) -> list[TextContent]:
|
||||
- visual_min_gap (float, optional): Minimum seconds between kept frames (default: 2.0)
|
||||
- visual_similarity (float, optional): Similarity threshold to skip duplicate frames (default: 0.95)
|
||||
- vision_ocr (bool, optional): Use vision model for OCR on frames (default: False)
|
||||
- start_time (str, optional): Start time for extraction (seconds, MM:SS, or HH:MM:SS)
|
||||
- end_time (str, optional): End time for extraction (seconds, MM:SS, or HH:MM:SS)
|
||||
- setup (bool, optional): Auto-detect GPU and install visual extraction deps
|
||||
|
||||
Returns:
|
||||
List[TextContent]: Tool execution results
|
||||
"""
|
||||
# Handle --setup early exit
|
||||
if args.get("setup", False):
|
||||
from skill_seekers.cli.video_setup import run_setup
|
||||
|
||||
rc = run_setup(interactive=False)
|
||||
msg = "Setup completed successfully." if rc == 0 else "Setup failed. Check logs."
|
||||
return [TextContent(type="text", text=msg)]
|
||||
|
||||
url = args.get("url")
|
||||
video_file = args.get("video_file")
|
||||
playlist = args.get("playlist")
|
||||
@@ -395,6 +406,8 @@ async def scrape_video_tool(args: dict) -> list[TextContent]:
|
||||
visual_min_gap = args.get("visual_min_gap")
|
||||
visual_similarity = args.get("visual_similarity")
|
||||
vision_ocr = args.get("vision_ocr", False)
|
||||
start_time = args.get("start_time")
|
||||
end_time = args.get("end_time")
|
||||
|
||||
# Build command
|
||||
cmd = [sys.executable, str(CLI_DIR / "video_scraper.py")]
|
||||
@@ -440,6 +453,10 @@ async def scrape_video_tool(args: dict) -> list[TextContent]:
|
||||
cmd.extend(["--visual-similarity", str(visual_similarity)])
|
||||
if vision_ocr:
|
||||
cmd.append("--vision-ocr")
|
||||
if start_time:
|
||||
cmd.extend(["--start-time", str(start_time)])
|
||||
if end_time:
|
||||
cmd.extend(["--end-time", str(end_time)])
|
||||
|
||||
# Run video_scraper.py with streaming
|
||||
timeout = 600 # 10 minutes for video extraction
|
||||
|
||||
@@ -3115,5 +3115,286 @@ class TestVideoWorkflowAutoInjection(unittest.TestCase):
|
||||
self.assertTrue(yaml_path.exists(), "video-tutorial.yaml not found")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Test: Time Clipping (--start-time / --end-time)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestTimeClipping(unittest.TestCase):
|
||||
"""Test --start-time / --end-time clipping support."""
|
||||
|
||||
# ---- parse_time_to_seconds() ----
|
||||
|
||||
def test_parse_time_seconds_integer(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
self.assertEqual(parse_time_to_seconds("330"), 330.0)
|
||||
|
||||
def test_parse_time_seconds_float(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
self.assertAlmostEqual(parse_time_to_seconds("90.5"), 90.5)
|
||||
|
||||
def test_parse_time_mmss(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
self.assertEqual(parse_time_to_seconds("5:30"), 330.0)
|
||||
|
||||
def test_parse_time_hhmmss(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
self.assertEqual(parse_time_to_seconds("1:05:30"), 3930.0)
|
||||
|
||||
def test_parse_time_zero(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
self.assertEqual(parse_time_to_seconds("0"), 0.0)
|
||||
self.assertEqual(parse_time_to_seconds("0:00"), 0.0)
|
||||
self.assertEqual(parse_time_to_seconds("0:00:00"), 0.0)
|
||||
|
||||
def test_parse_time_decimal_mmss(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
self.assertAlmostEqual(parse_time_to_seconds("1:30.5"), 90.5)
|
||||
|
||||
def test_parse_time_invalid_raises(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
parse_time_to_seconds("abc")
|
||||
|
||||
def test_parse_time_empty_raises(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
parse_time_to_seconds("")
|
||||
|
||||
def test_parse_time_too_many_colons_raises(self):
|
||||
from skill_seekers.cli.video_scraper import parse_time_to_seconds
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
parse_time_to_seconds("1:2:3:4")
|
||||
|
||||
# ---- Argument registration ----
|
||||
|
||||
def test_video_arguments_include_start_end_time(self):
|
||||
from skill_seekers.cli.arguments.video import VIDEO_ARGUMENTS
|
||||
|
||||
self.assertIn("start_time", VIDEO_ARGUMENTS)
|
||||
self.assertIn("end_time", VIDEO_ARGUMENTS)
|
||||
|
||||
def test_create_arguments_include_start_end_time(self):
|
||||
from skill_seekers.cli.arguments.create import VIDEO_ARGUMENTS
|
||||
|
||||
self.assertIn("start_time", VIDEO_ARGUMENTS)
|
||||
self.assertIn("end_time", VIDEO_ARGUMENTS)
|
||||
|
||||
def test_argument_parsing_defaults_none(self):
|
||||
import argparse
|
||||
from skill_seekers.cli.arguments.video import add_video_arguments
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
add_video_arguments(parser)
|
||||
args = parser.parse_args(["--url", "https://example.com"])
|
||||
self.assertIsNone(args.start_time)
|
||||
self.assertIsNone(args.end_time)
|
||||
|
||||
def test_argument_parsing_with_values(self):
|
||||
import argparse
|
||||
from skill_seekers.cli.arguments.video import add_video_arguments
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
add_video_arguments(parser)
|
||||
args = parser.parse_args(
|
||||
["--url", "https://example.com", "--start-time", "2:00", "--end-time", "5:00"]
|
||||
)
|
||||
self.assertEqual(args.start_time, "2:00")
|
||||
self.assertEqual(args.end_time, "5:00")
|
||||
|
||||
# ---- Transcript filtering ----
|
||||
|
||||
def test_transcript_clip_filters_segments(self):
|
||||
"""Verify transcript segments are filtered to clip range."""
|
||||
from skill_seekers.cli.video_models import TranscriptSegment
|
||||
|
||||
segments = [
|
||||
TranscriptSegment(text="intro", start=0.0, end=30.0),
|
||||
TranscriptSegment(text="part1", start=30.0, end=90.0),
|
||||
TranscriptSegment(text="part2", start=90.0, end=150.0),
|
||||
TranscriptSegment(text="outro", start=150.0, end=200.0),
|
||||
]
|
||||
|
||||
clip_start, clip_end = 60.0, 120.0
|
||||
filtered = [s for s in segments if s.end > clip_start and s.start < clip_end]
|
||||
# part1 (30-90) overlaps with 60-120, part2 (90-150) overlaps with 60-120
|
||||
self.assertEqual(len(filtered), 2)
|
||||
self.assertEqual(filtered[0].text, "part1")
|
||||
self.assertEqual(filtered[1].text, "part2")
|
||||
|
||||
def test_transcript_clip_start_only(self):
|
||||
"""Verify only clip_start filters correctly."""
|
||||
from skill_seekers.cli.video_models import TranscriptSegment
|
||||
|
||||
segments = [
|
||||
TranscriptSegment(text="before", start=0.0, end=50.0),
|
||||
TranscriptSegment(text="after", start=50.0, end=100.0),
|
||||
]
|
||||
clip_start = 50.0
|
||||
clip_end = float("inf")
|
||||
filtered = [s for s in segments if s.end > clip_start and s.start < clip_end]
|
||||
self.assertEqual(len(filtered), 1)
|
||||
self.assertEqual(filtered[0].text, "after")
|
||||
|
||||
# ---- Validation ----
|
||||
|
||||
def test_playlist_plus_clip_rejected(self):
|
||||
from skill_seekers.cli.video_models import VideoSourceConfig
|
||||
|
||||
config = VideoSourceConfig(
|
||||
playlist="https://youtube.com/playlist?list=x",
|
||||
clip_start=60.0,
|
||||
)
|
||||
errors = config.validate()
|
||||
self.assertTrue(any("--start-time" in e for e in errors))
|
||||
|
||||
def test_start_gte_end_rejected(self):
|
||||
from skill_seekers.cli.video_models import VideoSourceConfig
|
||||
|
||||
config = VideoSourceConfig(
|
||||
url="https://youtube.com/watch?v=x", clip_start=300.0, clip_end=120.0
|
||||
)
|
||||
errors = config.validate()
|
||||
self.assertTrue(any("must be before" in e for e in errors))
|
||||
|
||||
def test_valid_clip_no_errors(self):
|
||||
from skill_seekers.cli.video_models import VideoSourceConfig
|
||||
|
||||
config = VideoSourceConfig(
|
||||
url="https://youtube.com/watch?v=x", clip_start=60.0, clip_end=300.0
|
||||
)
|
||||
errors = config.validate()
|
||||
self.assertEqual(errors, [])
|
||||
|
||||
# ---- VideoInfo clip metadata serialization ----
|
||||
|
||||
def test_video_info_clip_roundtrip(self):
|
||||
from skill_seekers.cli.video_models import VideoInfo, VideoSourceType
|
||||
|
||||
info = VideoInfo(
|
||||
video_id="test",
|
||||
source_type=VideoSourceType.YOUTUBE,
|
||||
duration=300.0,
|
||||
original_duration=600.0,
|
||||
clip_start=120.0,
|
||||
clip_end=420.0,
|
||||
)
|
||||
data = info.to_dict()
|
||||
self.assertEqual(data["original_duration"], 600.0)
|
||||
self.assertEqual(data["clip_start"], 120.0)
|
||||
self.assertEqual(data["clip_end"], 420.0)
|
||||
|
||||
restored = VideoInfo.from_dict(data)
|
||||
self.assertEqual(restored.original_duration, 600.0)
|
||||
self.assertEqual(restored.clip_start, 120.0)
|
||||
self.assertEqual(restored.clip_end, 420.0)
|
||||
|
||||
def test_video_info_no_clip_roundtrip(self):
|
||||
from skill_seekers.cli.video_models import VideoInfo, VideoSourceType
|
||||
|
||||
info = VideoInfo(video_id="test", source_type=VideoSourceType.YOUTUBE)
|
||||
data = info.to_dict()
|
||||
self.assertIsNone(data["original_duration"])
|
||||
self.assertIsNone(data["clip_start"])
|
||||
self.assertIsNone(data["clip_end"])
|
||||
|
||||
restored = VideoInfo.from_dict(data)
|
||||
self.assertIsNone(restored.original_duration)
|
||||
self.assertIsNone(restored.clip_start)
|
||||
|
||||
# ---- VideoSourceConfig clip fields ----
|
||||
|
||||
def test_source_config_clip_fields(self):
|
||||
from skill_seekers.cli.video_models import VideoSourceConfig
|
||||
|
||||
config = VideoSourceConfig.from_dict(
|
||||
{
|
||||
"url": "https://example.com",
|
||||
"clip_start": 10.0,
|
||||
"clip_end": 60.0,
|
||||
}
|
||||
)
|
||||
self.assertEqual(config.clip_start, 10.0)
|
||||
self.assertEqual(config.clip_end, 60.0)
|
||||
|
||||
def test_source_config_clip_defaults_none(self):
|
||||
from skill_seekers.cli.video_models import VideoSourceConfig
|
||||
|
||||
config = VideoSourceConfig.from_dict({"url": "https://example.com"})
|
||||
self.assertIsNone(config.clip_start)
|
||||
self.assertIsNone(config.clip_end)
|
||||
|
||||
# ---- Converter init ----
|
||||
|
||||
def test_converter_init_with_clip_times(self):
|
||||
from skill_seekers.cli.video_scraper import VideoToSkillConverter
|
||||
|
||||
config = {
|
||||
"name": "test",
|
||||
"url": "https://youtube.com/watch?v=x",
|
||||
"start_time": 120.0,
|
||||
"end_time": 300.0,
|
||||
}
|
||||
converter = VideoToSkillConverter(config)
|
||||
self.assertEqual(converter.start_time, 120.0)
|
||||
self.assertEqual(converter.end_time, 300.0)
|
||||
|
||||
def test_converter_init_without_clip_times(self):
|
||||
from skill_seekers.cli.video_scraper import VideoToSkillConverter
|
||||
|
||||
config = {"name": "test", "url": "https://youtube.com/watch?v=x"}
|
||||
converter = VideoToSkillConverter(config)
|
||||
self.assertIsNone(converter.start_time)
|
||||
self.assertIsNone(converter.end_time)
|
||||
|
||||
# ---- Segmenter start_offset / end_limit ----
|
||||
|
||||
def test_segmenter_time_window_with_offset(self):
|
||||
from skill_seekers.cli.video_segmenter import segment_by_time_window
|
||||
from skill_seekers.cli.video_models import VideoInfo, VideoSourceType
|
||||
|
||||
info = VideoInfo(video_id="test", source_type=VideoSourceType.YOUTUBE, duration=600.0)
|
||||
# Use 120s windows starting at 120s, ending at 360s
|
||||
segments = segment_by_time_window(
|
||||
info, [], window_seconds=120.0, start_offset=120.0, end_limit=360.0
|
||||
)
|
||||
# No transcript segments so no segments generated, but verify no crash
|
||||
self.assertEqual(len(segments), 0)
|
||||
|
||||
def test_segmenter_time_window_offset_with_transcript(self):
|
||||
from skill_seekers.cli.video_segmenter import segment_by_time_window
|
||||
from skill_seekers.cli.video_models import (
|
||||
VideoInfo,
|
||||
VideoSourceType,
|
||||
TranscriptSegment,
|
||||
)
|
||||
|
||||
info = VideoInfo(video_id="test", source_type=VideoSourceType.YOUTUBE, duration=600.0)
|
||||
transcript = [
|
||||
TranscriptSegment(text="before clip", start=0.0, end=60.0),
|
||||
TranscriptSegment(text="in clip part1", start=120.0, end=180.0),
|
||||
TranscriptSegment(text="in clip part2", start=200.0, end=300.0),
|
||||
TranscriptSegment(text="after clip", start=400.0, end=500.0),
|
||||
]
|
||||
segments = segment_by_time_window(
|
||||
info, transcript, window_seconds=120.0, start_offset=120.0, end_limit=360.0
|
||||
)
|
||||
# Should have segments starting at 120, 240
|
||||
self.assertTrue(len(segments) >= 1)
|
||||
# All segments should be within clip range
|
||||
for seg in segments:
|
||||
self.assertGreaterEqual(seg.start_time, 120.0)
|
||||
self.assertLessEqual(seg.end_time, 360.0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
679
tests/test_video_setup.py
Normal file
679
tests/test_video_setup.py
Normal file
@@ -0,0 +1,679 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Tests for Video Setup (cli/video_setup.py) and video_visual.py resilience.
|
||||
|
||||
Tests cover:
|
||||
- GPU detection (NVIDIA, AMD ROCm, AMD without ROCm, CPU fallback)
|
||||
- CUDA / ROCm version → index URL mapping
|
||||
- PyTorch installation (mocked subprocess)
|
||||
- Visual deps installation (mocked subprocess)
|
||||
- Installation verification
|
||||
- run_setup orchestrator
|
||||
- Venv detection and creation
|
||||
- System dep checks (tesseract binary)
|
||||
- ROCm env var configuration
|
||||
- Module selection (SetupModules)
|
||||
- Tesseract circuit breaker (video_visual.py)
|
||||
- --setup flag in VIDEO_ARGUMENTS and early-exit in video_scraper
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import unittest
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
from skill_seekers.cli.video_setup import (
|
||||
_BASE_VIDEO_DEPS,
|
||||
GPUInfo,
|
||||
GPUVendor,
|
||||
SetupModules,
|
||||
_build_visual_deps,
|
||||
_cuda_version_to_index_url,
|
||||
_detect_distro,
|
||||
_PYTORCH_BASE,
|
||||
_rocm_version_to_index_url,
|
||||
check_tesseract,
|
||||
configure_rocm_env,
|
||||
create_venv,
|
||||
detect_gpu,
|
||||
get_venv_activate_cmd,
|
||||
get_venv_python,
|
||||
install_torch,
|
||||
install_visual_deps,
|
||||
is_in_venv,
|
||||
run_setup,
|
||||
verify_installation,
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# GPU Detection Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestGPUDetection(unittest.TestCase):
|
||||
"""Tests for detect_gpu() and its helpers."""
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which")
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_nvidia_detected(self, mock_run, mock_which):
|
||||
"""nvidia-smi present → GPUVendor.NVIDIA."""
|
||||
mock_which.side_effect = lambda cmd: "/usr/bin/nvidia-smi" if cmd == "nvidia-smi" else None
|
||||
mock_run.return_value = MagicMock(
|
||||
returncode=0,
|
||||
stdout=(
|
||||
"+-------------------------+\n"
|
||||
"| NVIDIA GeForce RTX 4090 On |\n"
|
||||
"| CUDA Version: 12.4 |\n"
|
||||
"+-------------------------+\n"
|
||||
),
|
||||
)
|
||||
gpu = detect_gpu()
|
||||
assert gpu.vendor == GPUVendor.NVIDIA
|
||||
assert "12.4" in gpu.compute_version
|
||||
assert "cu124" in gpu.index_url
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which")
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
@patch("skill_seekers.cli.video_setup._read_rocm_version", return_value="6.3.1")
|
||||
def test_amd_rocm_detected(self, mock_rocm_ver, mock_run, mock_which):
|
||||
"""rocminfo present → GPUVendor.AMD."""
|
||||
|
||||
def which_side(cmd):
|
||||
if cmd == "nvidia-smi":
|
||||
return None
|
||||
if cmd == "rocminfo":
|
||||
return "/usr/bin/rocminfo"
|
||||
return None
|
||||
|
||||
mock_which.side_effect = which_side
|
||||
mock_run.return_value = MagicMock(
|
||||
returncode=0,
|
||||
stdout="Marketing Name: AMD Radeon RX 7900 XTX\n",
|
||||
)
|
||||
gpu = detect_gpu()
|
||||
assert gpu.vendor == GPUVendor.AMD
|
||||
assert "rocm6.3" in gpu.index_url
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which")
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_amd_no_rocm_fallback(self, mock_run, mock_which):
|
||||
"""AMD GPU in lspci but no ROCm → AMD vendor, CPU index URL."""
|
||||
|
||||
def which_side(cmd):
|
||||
if cmd == "lspci":
|
||||
return "/usr/bin/lspci"
|
||||
return None
|
||||
|
||||
mock_which.side_effect = which_side
|
||||
|
||||
mock_run.return_value = MagicMock(
|
||||
returncode=0,
|
||||
stdout="06:00.0 VGA compatible controller: AMD/ATI Navi 31 [Radeon RX 7900 XTX]\n",
|
||||
)
|
||||
gpu = detect_gpu()
|
||||
assert gpu.vendor == GPUVendor.AMD
|
||||
assert "cpu" in gpu.index_url
|
||||
assert any("ROCm is not installed" in d for d in gpu.details)
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which", return_value=None)
|
||||
def test_cpu_fallback(self, mock_which):
|
||||
"""No GPU tools found → GPUVendor.NONE."""
|
||||
gpu = detect_gpu()
|
||||
assert gpu.vendor == GPUVendor.NONE
|
||||
assert "cpu" in gpu.index_url
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which")
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_nvidia_smi_error(self, mock_run, mock_which):
|
||||
"""nvidia-smi returns non-zero → skip to next check."""
|
||||
mock_which.side_effect = lambda cmd: (
|
||||
"/usr/bin/nvidia-smi" if cmd == "nvidia-smi" else None
|
||||
)
|
||||
mock_run.return_value = MagicMock(returncode=1, stdout="")
|
||||
gpu = detect_gpu()
|
||||
assert gpu.vendor == GPUVendor.NONE
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which")
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_nvidia_smi_timeout(self, mock_run, mock_which):
|
||||
"""nvidia-smi times out → skip to next check."""
|
||||
mock_which.side_effect = lambda cmd: (
|
||||
"/usr/bin/nvidia-smi" if cmd == "nvidia-smi" else None
|
||||
)
|
||||
mock_run.side_effect = subprocess.TimeoutExpired(cmd="nvidia-smi", timeout=10)
|
||||
gpu = detect_gpu()
|
||||
assert gpu.vendor == GPUVendor.NONE
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which")
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_rocminfo_error(self, mock_run, mock_which):
|
||||
"""rocminfo returns non-zero → skip to next check."""
|
||||
|
||||
def which_side(cmd):
|
||||
if cmd == "nvidia-smi":
|
||||
return None
|
||||
if cmd == "rocminfo":
|
||||
return "/usr/bin/rocminfo"
|
||||
return None
|
||||
|
||||
mock_which.side_effect = which_side
|
||||
mock_run.return_value = MagicMock(returncode=1, stdout="")
|
||||
gpu = detect_gpu()
|
||||
assert gpu.vendor == GPUVendor.NONE
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Version Mapping Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestVersionMapping(unittest.TestCase):
|
||||
"""Tests for CUDA/ROCm version → index URL mapping."""
|
||||
|
||||
def test_cuda_124(self):
|
||||
assert _cuda_version_to_index_url("12.4") == f"{_PYTORCH_BASE}/cu124"
|
||||
|
||||
def test_cuda_126(self):
|
||||
assert _cuda_version_to_index_url("12.6") == f"{_PYTORCH_BASE}/cu124"
|
||||
|
||||
def test_cuda_121(self):
|
||||
assert _cuda_version_to_index_url("12.1") == f"{_PYTORCH_BASE}/cu121"
|
||||
|
||||
def test_cuda_118(self):
|
||||
assert _cuda_version_to_index_url("11.8") == f"{_PYTORCH_BASE}/cu118"
|
||||
|
||||
def test_cuda_old_falls_to_cpu(self):
|
||||
assert _cuda_version_to_index_url("10.2") == f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
def test_cuda_invalid_string(self):
|
||||
assert _cuda_version_to_index_url("garbage") == f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
def test_rocm_63(self):
|
||||
assert _rocm_version_to_index_url("6.3.1") == f"{_PYTORCH_BASE}/rocm6.3"
|
||||
|
||||
def test_rocm_60(self):
|
||||
assert _rocm_version_to_index_url("6.0") == f"{_PYTORCH_BASE}/rocm6.2.4"
|
||||
|
||||
def test_rocm_old_falls_to_cpu(self):
|
||||
assert _rocm_version_to_index_url("5.4") == f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
def test_rocm_invalid(self):
|
||||
assert _rocm_version_to_index_url("bad") == f"{_PYTORCH_BASE}/cpu"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Venv Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestVenv(unittest.TestCase):
|
||||
"""Tests for venv detection and creation."""
|
||||
|
||||
def test_is_in_venv_returns_bool(self):
|
||||
result = is_in_venv()
|
||||
assert isinstance(result, bool)
|
||||
|
||||
def test_is_in_venv_detects_prefix_mismatch(self):
|
||||
# If sys.prefix != sys.base_prefix, we're in a venv
|
||||
with patch.object(sys, "prefix", "/some/venv"), \
|
||||
patch.object(sys, "base_prefix", "/usr"):
|
||||
assert is_in_venv() is True
|
||||
|
||||
def test_is_in_venv_detects_no_venv(self):
|
||||
with patch.object(sys, "prefix", "/usr"), \
|
||||
patch.object(sys, "base_prefix", "/usr"):
|
||||
assert is_in_venv() is False
|
||||
|
||||
def test_create_venv_in_tempdir(self):
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
venv_path = os.path.join(tmpdir, "test_venv")
|
||||
result = create_venv(venv_path)
|
||||
assert result is True
|
||||
assert os.path.isdir(venv_path)
|
||||
|
||||
def test_create_venv_already_exists(self):
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
# Create it once
|
||||
create_venv(tmpdir)
|
||||
# Creating again should succeed (already exists)
|
||||
assert create_venv(tmpdir) is True
|
||||
|
||||
def test_get_venv_python_linux(self):
|
||||
with patch("skill_seekers.cli.video_setup.platform.system", return_value="Linux"):
|
||||
path = get_venv_python("/path/.venv")
|
||||
assert path.endswith("bin/python")
|
||||
|
||||
def test_get_venv_activate_cmd_linux(self):
|
||||
with patch("skill_seekers.cli.video_setup.platform.system", return_value="Linux"):
|
||||
cmd = get_venv_activate_cmd("/path/.venv")
|
||||
assert "source" in cmd
|
||||
assert "bin/activate" in cmd
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# System Dep Check Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestSystemDeps(unittest.TestCase):
|
||||
"""Tests for system dependency checks."""
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which", return_value=None)
|
||||
def test_tesseract_not_installed(self, mock_which):
|
||||
result = check_tesseract()
|
||||
assert result["installed"] is False
|
||||
assert result["has_eng"] is False
|
||||
assert isinstance(result["install_cmd"], str)
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which", return_value="/usr/bin/tesseract")
|
||||
def test_tesseract_installed_with_eng(self, mock_which, mock_run):
|
||||
mock_run.side_effect = [
|
||||
# --version call
|
||||
MagicMock(returncode=0, stdout="tesseract 5.3.0\n", stderr=""),
|
||||
# --list-langs call
|
||||
MagicMock(returncode=0, stdout="List of available languages:\neng\nosd\n", stderr=""),
|
||||
]
|
||||
result = check_tesseract()
|
||||
assert result["installed"] is True
|
||||
assert result["has_eng"] is True
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
@patch("skill_seekers.cli.video_setup.shutil.which", return_value="/usr/bin/tesseract")
|
||||
def test_tesseract_installed_no_eng(self, mock_which, mock_run):
|
||||
mock_run.side_effect = [
|
||||
MagicMock(returncode=0, stdout="tesseract 5.3.0\n", stderr=""),
|
||||
MagicMock(returncode=0, stdout="List of available languages:\nosd\n", stderr=""),
|
||||
]
|
||||
result = check_tesseract()
|
||||
assert result["installed"] is True
|
||||
assert result["has_eng"] is False
|
||||
|
||||
def test_detect_distro_returns_string(self):
|
||||
result = _detect_distro()
|
||||
assert isinstance(result, str)
|
||||
|
||||
@patch("builtins.open", side_effect=OSError)
|
||||
def test_detect_distro_no_os_release(self, mock_open):
|
||||
assert _detect_distro() == "unknown"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# ROCm Configuration Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestROCmConfig(unittest.TestCase):
|
||||
"""Tests for configure_rocm_env()."""
|
||||
|
||||
def test_sets_miopen_find_mode(self):
|
||||
env_backup = os.environ.get("MIOPEN_FIND_MODE")
|
||||
try:
|
||||
os.environ.pop("MIOPEN_FIND_MODE", None)
|
||||
changes = configure_rocm_env()
|
||||
assert "MIOPEN_FIND_MODE=FAST" in changes
|
||||
assert os.environ["MIOPEN_FIND_MODE"] == "FAST"
|
||||
finally:
|
||||
if env_backup is not None:
|
||||
os.environ["MIOPEN_FIND_MODE"] = env_backup
|
||||
|
||||
def test_does_not_override_existing(self):
|
||||
env_backup = os.environ.get("MIOPEN_FIND_MODE")
|
||||
try:
|
||||
os.environ["MIOPEN_FIND_MODE"] = "NORMAL"
|
||||
changes = configure_rocm_env()
|
||||
miopen_changes = [c for c in changes if "MIOPEN_FIND_MODE" in c]
|
||||
assert len(miopen_changes) == 0
|
||||
assert os.environ["MIOPEN_FIND_MODE"] == "NORMAL"
|
||||
finally:
|
||||
if env_backup is not None:
|
||||
os.environ["MIOPEN_FIND_MODE"] = env_backup
|
||||
else:
|
||||
os.environ.pop("MIOPEN_FIND_MODE", None)
|
||||
|
||||
def test_sets_miopen_user_db_path(self):
|
||||
env_backup = os.environ.get("MIOPEN_USER_DB_PATH")
|
||||
try:
|
||||
os.environ.pop("MIOPEN_USER_DB_PATH", None)
|
||||
changes = configure_rocm_env()
|
||||
db_changes = [c for c in changes if "MIOPEN_USER_DB_PATH" in c]
|
||||
assert len(db_changes) == 1
|
||||
finally:
|
||||
if env_backup is not None:
|
||||
os.environ["MIOPEN_USER_DB_PATH"] = env_backup
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Module Selection Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestModuleSelection(unittest.TestCase):
|
||||
"""Tests for SetupModules and _build_visual_deps."""
|
||||
|
||||
def test_default_modules_all_true(self):
|
||||
m = SetupModules()
|
||||
assert m.torch is True
|
||||
assert m.easyocr is True
|
||||
assert m.opencv is True
|
||||
assert m.tesseract is True
|
||||
assert m.scenedetect is True
|
||||
assert m.whisper is True
|
||||
|
||||
def test_build_all_deps(self):
|
||||
deps = _build_visual_deps(SetupModules())
|
||||
assert "yt-dlp" in deps
|
||||
assert "youtube-transcript-api" in deps
|
||||
assert "easyocr" in deps
|
||||
assert "opencv-python-headless" in deps
|
||||
assert "pytesseract" in deps
|
||||
assert "scenedetect[opencv]" in deps
|
||||
assert "faster-whisper" in deps
|
||||
|
||||
def test_build_no_optional_deps(self):
|
||||
"""Even with all optional modules off, base video deps are included."""
|
||||
m = SetupModules(
|
||||
torch=False, easyocr=False, opencv=False,
|
||||
tesseract=False, scenedetect=False, whisper=False,
|
||||
)
|
||||
deps = _build_visual_deps(m)
|
||||
assert deps == list(_BASE_VIDEO_DEPS)
|
||||
|
||||
def test_build_partial_deps(self):
|
||||
m = SetupModules(easyocr=True, opencv=True, tesseract=False, scenedetect=False, whisper=False)
|
||||
deps = _build_visual_deps(m)
|
||||
assert "yt-dlp" in deps
|
||||
assert "youtube-transcript-api" in deps
|
||||
assert "easyocr" in deps
|
||||
assert "opencv-python-headless" in deps
|
||||
assert "pytesseract" not in deps
|
||||
assert "faster-whisper" not in deps
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Installation Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestInstallation(unittest.TestCase):
|
||||
"""Tests for install_torch() and install_visual_deps()."""
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_torch_success(self, mock_run):
|
||||
mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
|
||||
gpu = GPUInfo(vendor=GPUVendor.NVIDIA, index_url=f"{_PYTORCH_BASE}/cu124")
|
||||
assert install_torch(gpu) is True
|
||||
call_args = mock_run.call_args[0][0]
|
||||
assert "torch" in call_args
|
||||
assert "--index-url" in call_args
|
||||
assert f"{_PYTORCH_BASE}/cu124" in call_args
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_torch_cpu(self, mock_run):
|
||||
mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
|
||||
gpu = GPUInfo(vendor=GPUVendor.NONE, index_url=f"{_PYTORCH_BASE}/cpu")
|
||||
assert install_torch(gpu) is True
|
||||
call_args = mock_run.call_args[0][0]
|
||||
assert f"{_PYTORCH_BASE}/cpu" in call_args
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_torch_failure(self, mock_run):
|
||||
mock_run.return_value = MagicMock(returncode=1, stdout="", stderr="error msg")
|
||||
gpu = GPUInfo(vendor=GPUVendor.NVIDIA, index_url=f"{_PYTORCH_BASE}/cu124")
|
||||
assert install_torch(gpu) is False
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_torch_timeout(self, mock_run):
|
||||
mock_run.side_effect = subprocess.TimeoutExpired(cmd="pip", timeout=600)
|
||||
gpu = GPUInfo(vendor=GPUVendor.NVIDIA, index_url=f"{_PYTORCH_BASE}/cu124")
|
||||
assert install_torch(gpu) is False
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_torch_custom_python(self, mock_run):
|
||||
mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
|
||||
gpu = GPUInfo(vendor=GPUVendor.NONE, index_url=f"{_PYTORCH_BASE}/cpu")
|
||||
install_torch(gpu, python_exe="/custom/python")
|
||||
call_args = mock_run.call_args[0][0]
|
||||
assert call_args[0] == "/custom/python"
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_visual_deps_success(self, mock_run):
|
||||
mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
|
||||
assert install_visual_deps() is True
|
||||
call_args = mock_run.call_args[0][0]
|
||||
assert "easyocr" in call_args
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_visual_deps_failure(self, mock_run):
|
||||
mock_run.return_value = MagicMock(returncode=1, stdout="", stderr="error")
|
||||
assert install_visual_deps() is False
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_visual_deps_partial_modules(self, mock_run):
|
||||
mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
|
||||
modules = SetupModules(easyocr=True, opencv=False, tesseract=False, scenedetect=False, whisper=False)
|
||||
install_visual_deps(modules)
|
||||
call_args = mock_run.call_args[0][0]
|
||||
assert "easyocr" in call_args
|
||||
assert "opencv-python-headless" not in call_args
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.subprocess.run")
|
||||
def test_install_visual_deps_base_only(self, mock_run):
|
||||
"""Even with all optional modules off, base video deps get installed."""
|
||||
mock_run.return_value = MagicMock(returncode=0, stdout="", stderr="")
|
||||
modules = SetupModules(easyocr=False, opencv=False, tesseract=False, scenedetect=False, whisper=False)
|
||||
result = install_visual_deps(modules)
|
||||
assert result is True
|
||||
call_args = mock_run.call_args[0][0]
|
||||
assert "yt-dlp" in call_args
|
||||
assert "youtube-transcript-api" in call_args
|
||||
assert "easyocr" not in call_args
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Verification Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestVerification(unittest.TestCase):
|
||||
"""Tests for verify_installation()."""
|
||||
|
||||
@patch.dict("sys.modules", {"torch": None, "easyocr": None, "cv2": None})
|
||||
def test_returns_dict(self):
|
||||
results = verify_installation()
|
||||
assert isinstance(results, dict)
|
||||
|
||||
def test_expected_keys(self):
|
||||
results = verify_installation()
|
||||
for key in ("yt-dlp", "youtube-transcript-api", "torch", "torch.cuda", "torch.rocm", "easyocr", "opencv"):
|
||||
assert key in results, f"Missing key: {key}"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Orchestrator Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestRunSetup(unittest.TestCase):
|
||||
"""Tests for run_setup() orchestrator."""
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.verify_installation")
|
||||
@patch("skill_seekers.cli.video_setup.install_visual_deps", return_value=True)
|
||||
@patch("skill_seekers.cli.video_setup.install_torch", return_value=True)
|
||||
@patch("skill_seekers.cli.video_setup.check_tesseract")
|
||||
@patch("skill_seekers.cli.video_setup.detect_gpu")
|
||||
def test_non_interactive_success(self, mock_detect, mock_tess, mock_torch, mock_deps, mock_verify):
|
||||
mock_detect.return_value = GPUInfo(
|
||||
vendor=GPUVendor.NONE, name="CPU-only", index_url=f"{_PYTORCH_BASE}/cpu",
|
||||
)
|
||||
mock_tess.return_value = {"installed": True, "has_eng": True, "install_cmd": "", "version": "5.3.0"}
|
||||
mock_verify.return_value = {
|
||||
"torch": True, "torch.cuda": False, "torch.rocm": False,
|
||||
"easyocr": True, "opencv": True, "pytesseract": True,
|
||||
"scenedetect": True, "faster-whisper": True,
|
||||
}
|
||||
rc = run_setup(interactive=False)
|
||||
assert rc == 0
|
||||
mock_torch.assert_called_once()
|
||||
mock_deps.assert_called_once()
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.install_torch", return_value=False)
|
||||
@patch("skill_seekers.cli.video_setup.check_tesseract")
|
||||
@patch("skill_seekers.cli.video_setup.detect_gpu")
|
||||
def test_failure_returns_nonzero(self, mock_detect, mock_tess, mock_torch):
|
||||
mock_detect.return_value = GPUInfo(
|
||||
vendor=GPUVendor.NONE, name="CPU-only", index_url=f"{_PYTORCH_BASE}/cpu",
|
||||
)
|
||||
mock_tess.return_value = {"installed": True, "has_eng": True, "install_cmd": "", "version": "5.3.0"}
|
||||
rc = run_setup(interactive=False)
|
||||
assert rc == 1
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.install_torch", return_value=True)
|
||||
@patch("skill_seekers.cli.video_setup.install_visual_deps", return_value=False)
|
||||
@patch("skill_seekers.cli.video_setup.check_tesseract")
|
||||
@patch("skill_seekers.cli.video_setup.detect_gpu")
|
||||
def test_visual_deps_failure(self, mock_detect, mock_tess, mock_deps, mock_torch):
|
||||
mock_detect.return_value = GPUInfo(
|
||||
vendor=GPUVendor.NONE, name="CPU-only", index_url=f"{_PYTORCH_BASE}/cpu",
|
||||
)
|
||||
mock_tess.return_value = {"installed": True, "has_eng": True, "install_cmd": "", "version": "5.3.0"}
|
||||
rc = run_setup(interactive=False)
|
||||
assert rc == 1
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.verify_installation")
|
||||
@patch("skill_seekers.cli.video_setup.install_visual_deps", return_value=True)
|
||||
@patch("skill_seekers.cli.video_setup.install_torch", return_value=True)
|
||||
@patch("skill_seekers.cli.video_setup.check_tesseract")
|
||||
@patch("skill_seekers.cli.video_setup.detect_gpu")
|
||||
def test_rocm_configures_env(self, mock_detect, mock_tess, mock_torch, mock_deps, mock_verify):
|
||||
"""AMD GPU → configure_rocm_env called and env vars set."""
|
||||
mock_detect.return_value = GPUInfo(
|
||||
vendor=GPUVendor.AMD, name="RX 7900", index_url=f"{_PYTORCH_BASE}/rocm6.3",
|
||||
)
|
||||
mock_tess.return_value = {"installed": True, "has_eng": True, "install_cmd": "", "version": "5.3.0"}
|
||||
mock_verify.return_value = {
|
||||
"torch": True, "torch.cuda": False, "torch.rocm": True,
|
||||
"easyocr": True, "opencv": True, "pytesseract": True,
|
||||
"scenedetect": True, "faster-whisper": True,
|
||||
}
|
||||
rc = run_setup(interactive=False)
|
||||
assert rc == 0
|
||||
assert os.environ.get("MIOPEN_FIND_MODE") is not None
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Tesseract Circuit Breaker Tests (video_visual.py)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestTesseractCircuitBreaker(unittest.TestCase):
|
||||
"""Tests for _tesseract_broken flag in video_visual.py."""
|
||||
|
||||
def test_circuit_breaker_flag_exists(self):
|
||||
import skill_seekers.cli.video_visual as vv
|
||||
assert hasattr(vv, "_tesseract_broken")
|
||||
|
||||
def test_circuit_breaker_skips_after_failure(self):
|
||||
import skill_seekers.cli.video_visual as vv
|
||||
from skill_seekers.cli.video_models import FrameType
|
||||
|
||||
# Save and set broken state
|
||||
original = vv._tesseract_broken
|
||||
try:
|
||||
vv._tesseract_broken = True
|
||||
result = vv._run_tesseract_ocr("/nonexistent/path.png", FrameType.CODE_EDITOR)
|
||||
assert result == []
|
||||
finally:
|
||||
vv._tesseract_broken = original
|
||||
|
||||
def test_circuit_breaker_allows_when_not_broken(self):
|
||||
import skill_seekers.cli.video_visual as vv
|
||||
from skill_seekers.cli.video_models import FrameType
|
||||
|
||||
original = vv._tesseract_broken
|
||||
try:
|
||||
vv._tesseract_broken = False
|
||||
if not vv.HAS_PYTESSERACT:
|
||||
# pytesseract not installed → returns [] immediately
|
||||
result = vv._run_tesseract_ocr("/nonexistent/path.png", FrameType.CODE_EDITOR)
|
||||
assert result == []
|
||||
# If pytesseract IS installed, it would try to run and potentially fail
|
||||
# on our fake path — that's fine, the circuit breaker would trigger
|
||||
finally:
|
||||
vv._tesseract_broken = original
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MIOPEN Env Var Tests (video_visual.py)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestMIOPENEnvVars(unittest.TestCase):
|
||||
"""Tests that video_visual.py sets MIOPEN env vars at import time."""
|
||||
|
||||
def test_miopen_find_mode_set(self):
|
||||
# video_visual.py sets this at module level before torch import
|
||||
assert "MIOPEN_FIND_MODE" in os.environ
|
||||
|
||||
def test_miopen_user_db_path_set(self):
|
||||
assert "MIOPEN_USER_DB_PATH" in os.environ
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Argument & Early-Exit Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestVideoArgumentSetup(unittest.TestCase):
|
||||
"""Tests for --setup flag in VIDEO_ARGUMENTS."""
|
||||
|
||||
def test_setup_in_video_arguments(self):
|
||||
from skill_seekers.cli.arguments.video import VIDEO_ARGUMENTS
|
||||
|
||||
assert "setup" in VIDEO_ARGUMENTS
|
||||
assert VIDEO_ARGUMENTS["setup"]["kwargs"]["action"] == "store_true"
|
||||
|
||||
def test_parser_accepts_setup(self):
|
||||
import argparse
|
||||
|
||||
from skill_seekers.cli.arguments.video import add_video_arguments
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
add_video_arguments(parser)
|
||||
args = parser.parse_args(["--setup"])
|
||||
assert args.setup is True
|
||||
|
||||
def test_parser_default_false(self):
|
||||
import argparse
|
||||
|
||||
from skill_seekers.cli.arguments.video import add_video_arguments
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
add_video_arguments(parser)
|
||||
args = parser.parse_args(["--url", "https://example.com"])
|
||||
assert args.setup is False
|
||||
|
||||
|
||||
class TestVideoScraperSetupEarlyExit(unittest.TestCase):
|
||||
"""Test that --setup exits before source validation."""
|
||||
|
||||
@patch("skill_seekers.cli.video_setup.run_setup", return_value=0)
|
||||
def test_setup_skips_source_validation(self, mock_setup):
|
||||
"""--setup without --url should NOT error about missing source."""
|
||||
from skill_seekers.cli.video_scraper import main
|
||||
|
||||
old_argv = sys.argv
|
||||
try:
|
||||
sys.argv = ["video_scraper", "--setup"]
|
||||
rc = main()
|
||||
assert rc == 0
|
||||
mock_setup.assert_called_once_with(interactive=True)
|
||||
finally:
|
||||
sys.argv = old_argv
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
462
uv.lock
generated
462
uv.lock
generated
@@ -1078,31 +1078,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/b0/0d/9feae160378a3553fa9a339b0e9c1a048e147a4127210e286ef18b730f03/durationpy-0.10-py3-none-any.whl", hash = "sha256:3b41e1b601234296b4fb368338fdcd3e13e0b4fb5b67345948f4f2bf9868b286", size = 3922, upload-time = "2025-05-17T13:52:36.463Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "easyocr"
|
||||
version = "1.7.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "ninja" },
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "opencv-python-headless" },
|
||||
{ name = "pillow" },
|
||||
{ name = "pyclipper" },
|
||||
{ name = "python-bidi" },
|
||||
{ name = "pyyaml" },
|
||||
{ name = "scikit-image", version = "0.25.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "scikit-image", version = "0.26.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "scipy", version = "1.17.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "shapely" },
|
||||
{ name = "torch" },
|
||||
{ name = "torchvision" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/bb/84/4a2cab0e6adde6a85e7ba543862e5fc0250c51f3ac721a078a55cdcff250/easyocr-1.7.2-py3-none-any.whl", hash = "sha256:5be12f9b0e595d443c9c3d10b0542074b50f0ec2d98b141a109cd961fd1c177c", size = 2870178, upload-time = "2024-09-24T11:34:43.554Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.3.1"
|
||||
@@ -1898,20 +1873,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008, upload-time = "2025-10-12T14:55:18.883Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "imageio"
|
||||
version = "2.37.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||
{ name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||
{ name = "pillow" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a3/6f/606be632e37bf8d05b253e8626c2291d74c691ddc7bcdf7d6aaf33b32f6a/imageio-2.37.2.tar.gz", hash = "sha256:0212ef2727ac9caa5ca4b2c75ae89454312f440a756fcfc8ef1993e718f50f8a", size = 389600, upload-time = "2025-11-04T14:29:39.898Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fb/fe/301e0936b79bcab4cacc7548bf2853fc28dced0a578bab1f7ef53c9aa75b/imageio-2.37.2-py3-none-any.whl", hash = "sha256:ad9adfb20335d718c03de457358ed69f141021a333c40a53e57273d8a5bd0b9b", size = 317646, upload-time = "2025-11-04T14:29:37.948Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "8.7.1"
|
||||
@@ -2267,18 +2228,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/e6/8e/063e09c5e8a3dcd77e2a8f0bff3f71c1c52a9d238da1bcafd2df3281da17/langsmith-0.6.9-py3-none-any.whl", hash = "sha256:86ba521e042397f6fbb79d63991df9d5f7b6a6dd6a6323d4f92131291478dcff", size = 319228, upload-time = "2026-02-05T20:10:54.248Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "lazy-loader"
|
||||
version = "0.4"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "packaging" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/6f/6b/c875b30a1ba490860c93da4cabf479e03f584eba06fe5963f6f6644653d8/lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1", size = 15431, upload-time = "2024-04-05T13:03:12.261Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc", size = 12097, upload-time = "2024-04-05T13:03:10.514Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "librt"
|
||||
version = "0.7.8"
|
||||
@@ -3196,32 +3145,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ninja"
|
||||
version = "1.13.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/43/73/79a0b22fc731989c708068427579e840a6cf4e937fe7ae5c5d0b7356ac22/ninja-1.13.0.tar.gz", hash = "sha256:4a40ce995ded54d9dc24f8ea37ff3bf62ad192b547f6c7126e7e25045e76f978", size = 242558, upload-time = "2025-08-11T15:10:19.421Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/3c/74/d02409ed2aa865e051b7edda22ad416a39d81a84980f544f8de717cab133/ninja-1.13.0-py3-none-macosx_10_9_universal2.whl", hash = "sha256:fa2a8bfc62e31b08f83127d1613d10821775a0eb334197154c4d6067b7068ff1", size = 310125, upload-time = "2025-08-11T15:09:50.971Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8e/de/6e1cd6b84b412ac1ef327b76f0641aeb5dcc01e9d3f9eee0286d0c34fd93/ninja-1.13.0-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3d00c692fb717fd511abeb44b8c5d00340c36938c12d6538ba989fe764e79630", size = 177467, upload-time = "2025-08-11T15:09:52.767Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c8/83/49320fb6e58ae3c079381e333575fdbcf1cca3506ee160a2dcce775046fa/ninja-1.13.0-py3-none-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:be7f478ff9f96a128b599a964fc60a6a87b9fa332ee1bd44fa243ac88d50291c", size = 187834, upload-time = "2025-08-11T15:09:54.115Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/56/c7/ba22748fb59f7f896b609cd3e568d28a0a367a6d953c24c461fe04fc4433/ninja-1.13.0-py3-none-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:60056592cf495e9a6a4bea3cd178903056ecb0943e4de45a2ea825edb6dc8d3e", size = 202736, upload-time = "2025-08-11T15:09:55.745Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/79/22/d1de07632b78ac8e6b785f41fa9aad7a978ec8c0a1bf15772def36d77aac/ninja-1.13.0-py3-none-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:1c97223cdda0417f414bf864cfb73b72d8777e57ebb279c5f6de368de0062988", size = 179034, upload-time = "2025-08-11T15:09:57.394Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ed/de/0e6edf44d6a04dabd0318a519125ed0415ce437ad5a1ec9b9be03d9048cf/ninja-1.13.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fb46acf6b93b8dd0322adc3a4945452a4e774b75b91293bafcc7b7f8e6517dfa", size = 180716, upload-time = "2025-08-11T15:09:58.696Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/54/28/938b562f9057aaa4d6bfbeaa05e81899a47aebb3ba6751e36c027a7f5ff7/ninja-1.13.0-py3-none-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:4be9c1b082d244b1ad7ef41eb8ab088aae8c109a9f3f0b3e56a252d3e00f42c1", size = 146843, upload-time = "2025-08-11T15:10:00.046Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/2a/fb/d06a3838de4f8ab866e44ee52a797b5491df823901c54943b2adb0389fbb/ninja-1.13.0-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:6739d3352073341ad284246f81339a384eec091d9851a886dfa5b00a6d48b3e2", size = 154402, upload-time = "2025-08-11T15:10:01.657Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/31/bf/0d7808af695ceddc763cf251b84a9892cd7f51622dc8b4c89d5012779f06/ninja-1.13.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:11be2d22027bde06f14c343f01d31446747dbb51e72d00decca2eb99be911e2f", size = 552388, upload-time = "2025-08-11T15:10:03.349Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/9d/70/c99d0c2c809f992752453cce312848abb3b1607e56d4cd1b6cded317351a/ninja-1.13.0-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:aa45b4037b313c2f698bc13306239b8b93b4680eb47e287773156ac9e9304714", size = 472501, upload-time = "2025-08-11T15:10:04.735Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/9f/43/c217b1153f0e499652f5e0766da8523ce3480f0a951039c7af115e224d55/ninja-1.13.0-py3-none-musllinux_1_2_i686.whl", hash = "sha256:5f8e1e8a1a30835eeb51db05cf5a67151ad37542f5a4af2a438e9490915e5b72", size = 638280, upload-time = "2025-08-11T15:10:06.512Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/8c/45/9151bba2c8d0ae2b6260f71696330590de5850e5574b7b5694dce6023e20/ninja-1.13.0-py3-none-musllinux_1_2_ppc64le.whl", hash = "sha256:3d7d7779d12cb20c6d054c61b702139fd23a7a964ec8f2c823f1ab1b084150db", size = 642420, upload-time = "2025-08-11T15:10:08.35Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3c/fb/95752eb635bb8ad27d101d71bef15bc63049de23f299e312878fc21cb2da/ninja-1.13.0-py3-none-musllinux_1_2_riscv64.whl", hash = "sha256:d741a5e6754e0bda767e3274a0f0deeef4807f1fec6c0d7921a0244018926ae5", size = 585106, upload-time = "2025-08-11T15:10:09.818Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/c1/31/aa56a1a286703800c0cbe39fb4e82811c277772dc8cd084f442dd8e2938a/ninja-1.13.0-py3-none-musllinux_1_2_s390x.whl", hash = "sha256:e8bad11f8a00b64137e9b315b137d8bb6cbf3086fbdc43bf1f90fd33324d2e96", size = 707138, upload-time = "2025-08-11T15:10:11.366Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/34/6f/5f5a54a1041af945130abdb2b8529cbef0cdcbbf9bcf3f4195378319d29a/ninja-1.13.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:b4f2a072db3c0f944c32793e91532d8948d20d9ab83da9c0c7c15b5768072200", size = 581758, upload-time = "2025-08-11T15:10:13.295Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/95/97/51359c77527d45943fe7a94d00a3843b81162e6c4244b3579fe8fc54cb9c/ninja-1.13.0-py3-none-win32.whl", hash = "sha256:8cfbb80b4a53456ae8a39f90ae3d7a2129f45ea164f43fadfa15dc38c4aef1c9", size = 267201, upload-time = "2025-08-11T15:10:15.158Z" },
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[[package]]
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name = "tqdm"
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version = "4.67.1"
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Reference in New Issue
Block a user