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.github/README.md

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+# Précis
+
+<!-- This version of the README is created just for HuggingFace to work -->
+
+A system for compressing long-form content into clear, structured summaries. Précis is designed for videos, articles, and papers. Paste a YouTube link, drop in an article, or upload a text file. Précis will pulls the key facts into a single sentence using a local LLM via [Ollama](https://ollama.com).
+
+## Features
+
+- **YouTube summarization**: paste a URL, transcript is fetched automatically via `youtube-transcript-api`
+- **Article / transcript**: paste any text directly
+- **File upload**: drag-and-drop `.txt` files
+- **Streaming**: summaries stream token-by-token from Ollama via NDJSON
+- **Model switching**: choose between available Ollama models from the UI
+
+## API Endpoints
+
+| Method  |       Path              |     Description       |
+|---------|-------------------------|-----------------------|
+| `GET`   | `/health`               | Health check          |
+| `GET`   | `/status`               | Ollama statuses, etc. |
+| `GET`   | `/models`               | List available models |
+| `POST`  | `/summarize/transcript` | Raw text summary      |
+| `POST`  | `/summarize/youtube`    | YouTube video by URL  |
+| `POST`  | `/summarize/file`       | `.txt` file summary   |
+
+All `/summarize/*` endpoints accept an optional `model` field to override the default.
+
+## Local Setup
+
+### Prerequisites
+
+- Python 3.11+,
+- Node.js 18+ (or an alternative like [Bun](https://bun.sh)),
+- [Ollama](https://ollama.com) installed and running (`ollama serve` is the command, although it may be on auto-start).
+- At least one model pulled: `ollama pull phi4-mini:latest` (for example)
+
+### Run the Fine-Tuning
+
+Follow the scripts in `scripts/`, using any model you prefer. This project has been primarily tested with phi4-mini (from Microsoft) and Qwen 3-4b (from Alibaba) (`ollama pull qwen3:4b` to pull it).
+
+### Start the Backend
+
+```bash
+# Create a venv or conda environment or whatever else you may want
+pip install -r ../requirements.txt
+cd backend
+uvicorn app:app --reload
+```
+
+Runs on `http://localhost:8000`. Interactive docs at `/docs`.
+
+### Run the Frontend
+
+```bash
+cd frontend
+npm install   # or whatever replacement for npm you may be using
+npm run dev
+```
+
+Runs on `http://localhost:5173`.
+
+<!-- ## Data -->
+
+<!-- Later, for fine-tuning data details -->
+
+<!-- Interview Dataset -->
+<!-- 
+
+@article{zhu2021mediasum,
+  title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
+  author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
+  journal={arXiv preprint arXiv:2103.06410},
+  year={2021}
+} 
+
+-->
+
+<!--------------------------------------------------------------------------------------------------->
+
+<!-- 
+
+@inproceedings{chen-etal-2021-dialogsum,
+    title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset",
+    author = "Chen, Yulong  and
+      Liu, Yang  and
+      Chen, Liang  and
+      Zhang, Yue",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.449",
+    doi = "10.18653/v1/2021.findings-acl.449",
+    pages = "5062--5074",
+}
+
+-->
+
+<!------------------------------------------------------------------------------------------------->
+
+<!-- "Single question followed by an answer" dataset -->
+
+<!--
+
+@article{wang2022squality,
+  title        = {SQuALITY: Building a Long-Document Summarization Dataset the Hard Way},
+  author       = {Wang, Alex and Pang, Richard Yuanzhe and Chen, Angelica and Phang, Jason and Bowman, Samuel R.},
+  journal      = {arXiv preprint arXiv:2205.11465},
+  year         = {2022},
+  archivePrefix = {arXiv},
+  eprint       = {2205.11465},
+  primaryClass = {cs.CL},
+  doi          = {10.48550/arXiv.2205.11465},
+  url          = {https://doi.org/10.48550/arXiv.2205.11465}
+}
+
+-->
+
+<!------------------------------------------------------------------------------------------------->
+
+<!-- High Quality Query-Answer (concise) examples -->
+
+<!--
+
+@inproceedings{nguyen2016msmarco,
+  title     = {MS MARCO: A Human Generated Machine Reading Comprehension Dataset},
+  author    = {Nguyen, Tri and Rosenberg, Mir and Song, Xia and Gao, Jianfeng and Tiwary, Saurabh and Majumder, Rangan and Deng, Li},
+  booktitle = {Proceedings of the Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches 2016},
+  year      = {2016},
+  publisher = {CEUR-WS.org}
+}
+
+-->
+
+## License
+
+[GPL-3.0](LICENSE.md)

README.md

@@ -1,3 +1,15 @@
+---
+title: Précis
+emoji: 📝
+colorFrom: blue
+colorTo: purple
+sdk: docker
+sdk_version: "1"
+python_version: "3.11"
+app_file: app.py
+pinned: false
+---
+
 # Précis
 
 A system for compressing long-form content into clear, structured summaries. Précis is designed for videos, articles, and papers. Paste a YouTube link, drop in an article, or upload a text file. Précis will pulls the key facts into a single sentence using a local LLM via [Ollama](https://ollama.com).