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ClassAtlas\n\nStreamlit + LangChain toolkit for turning lecture videos into searchable, multilingual study companions.\n\n## Highlights\n\n- End-to-end lecture pipeline from video transcription to outline generation\n- PDF slide alignment for lecture playback and study review\n- Multilingual workflow with a single Streamlit interface\n- Built-in Q\u0026A over processed lecture materials\n\n## Features\n\n- Transcribe lecture videos with Faster-Whisper\n- Build structured lecture outlines with an LLM\n- Align lecture slides from an uploaded PDF\n- Explore artifacts in a Streamlit UI\n- Ask questions over processed lecture content\n- Store outputs per lecture under `data/lectures/`\n\n## Repository Layout\n\n```text\nclass-atlas/\n  app/\n    pipeline/\n  scripts/\n  data/\n  process_lecture.py\n  streamlit_app.py\n  requirements.txt\n```\n\n## Requirements\n\n- Python 3.10+\n- FFmpeg available in `PATH`\n- A valid LLM API endpoint compatible with the configured settings\n\n## Install\n\n```bash\npython -m venv .venv\n.venv\\Scripts\\activate\npip install -r requirements.txt\n```\n\n## Configure\n\nCopy `settings.example.json` to `settings.json`, or let the app create it on first run.\n\nExample settings:\n\n```json\n{\n  \"llm_model\": \"Qwen/Qwen3-8B\",\n  \"llm_base_url\": \"https://api-inference.modelscope.cn/v1/\",\n  \"llm_api_key\": \"\",\n  \"whisper_model\": \"medium\",\n  \"lecture_root\": \"data/lectures\"\n}\n```\n\nYou can also point to a custom settings file with `CLASS_EXTRACT_SETTINGS`.\n\n## Quick Start\n\nProcess a lecture from the command line:\n\n```bash\npython process_lecture.py ^\n  --video path\\to\\lecture.mp4 ^\n  --output data\\lectures\\lecture-a ^\n  --lecture-id lecture-a ^\n  --title \"Lecture A\"\n```\n\nLaunch the UI:\n\n```bash\nstreamlit run streamlit_app.py\n```\n\nOpen the app, upload a lecture video, and browse the generated transcript, outline, slides, and Q\u0026A views from one workspace.\n\n## Typical Workflow\n\n1. Configure `settings.json`\n2. Process a lecture video from CLI or through the UI\n3. Inspect transcript, outline, and aligned slide artifacts\n4. Run Q\u0026A over the processed lecture materials\n\n## Notes\n\n- `settings.json`, logs, caches, and generated lecture artifacts are intentionally not committed.\n- `scripts/test_pipeline_cache.py` is preserved as a lightweight regression helper.\n- The Chinese project readme is preserved in [README_ZH.md](README_ZH.md).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhuggon1%2Fclass-atlas","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhuggon1%2Fclass-atlas","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhuggon1%2Fclass-atlas/lists"}