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-# 🚀 Example Chute for Turbovision 🪂
-
-This repository demonstrates how to deploy a **Chute** via the **Turbovision CLI**, hosted on **Hugging Face Hub**.
-It serves as a minimal example showcasing the required structure and workflow for integrating machine learning models, preprocessing, and orchestration into a reproducible Chute environment.
-
-## Repository Structure 
-The following two files **must be present** (in their current locations) for a successful deployment — their content can be modified as needed:
-
-| File | Purpose |
-|------|----------|
-| `miner.py` | Defines the ML model type(s), orchestration, and all pre/postprocessing logic. |
-| `config.yml` | Specifies machine configuration (e.g., GPU type, memory, environment variables). |
-
-Other files — e.g., model weights, utility scripts, or dependencies — are **optional** and can be included as needed for your model. Note: Any required assets must be defined or contained **within this repo**, which is fully open-source, since all network-related operations (downloading challenge data, weights, etc.) are disabled **inside the Chute** 
-
-## Overview
-
-Below is a high-level diagram showing the interaction between Huggingface, Chutes and Turbovision:
-
-![](../../../../images/miner.png)
-
-## Local Testing
-After editing the `config.yml` and `miner.py` and saving it into your Huggingface Repo, you will want to test it works locally. 
-
-1. Copy the file `scorevision/miner/open_source/chute_template/turbovision_chute.py.j2` as a python file called `my_chute.py` and fill in the missing variables:
-```python
-HF_REPO_NAME = "{{ huggingface_repository_name }}"
-HF_REPO_REVISION = "{{ huggingface_repository_revision }}"
-CHUTES_USERNAME = "{{ chute_username }}"
-CHUTE_NAME = "{{ chute_name }}"
-```
-
-2. Run the following command to build the chute locally (Caution: there are known issues with the docker location when running this on a mac) 
-```bash
-chutes build my_chute:chute --local --public
-```
-
-3. Run the name of the docker image just built (i.e. `CHUTE_NAME`) and enter it
-```bash
-docker run -p 8000:8000 -e CHUTES_EXECUTION_CONTEXT=REMOTE -it <image-name> /bin/bash
-```
-
-4. Run the file from within the container
-```bash
-chutes run my_chute:chute --dev --debug
-```
-
-5. In another terminal, test the local endpoints to ensure there are no bugs
-```bash
-curl -X POST http://localhost:8000/health -d '{}'
-curl -X POST http://localhost:8000/predict -d '{"url": "https://scoredata.me/2025_03_14/35ae7a/h1_0f2ca0.mp4","meta": {}}'
-```
-
-## Live Testing
-1. If you have any chute with the same name (ie from a previous deployment), ensure you delete that first (or you will get an error when trying to build).
-```bash
-chutes chutes list
-```
-Take note of the chute id that you wish to delete (if any)
-```bash
-chutes chutes delete <chute-id>
-```
-
-You should also delete its associated image 
-```bash
-chutes images list
-```
-Take note of the chute image id
-```bash
-chutes images delete <chute-image-id>
-```
-
-2. Use Turbovision's CLI to build, deploy and commit on-chain (Note: you can skip the on-chain commit using `--no-commit`.  You can also specify a past huggingface revision to point to using `--revision` and/or the local files you want to upload to your huggingface repo using `--model-path`)
-```bash
-sv -vv deploy-os-miner --element-id <element_id>
-```
-
-3. When completed, warm up the chute (if its cold 🧊). (You can confirm its status using `chutes chutes list` or `chutes chutes get <chute-id>` if you already know its id). Note: Warming up can sometimes take a while but if the chute runs without errors (should be if you've tested locally first) and there are sufficient nodes (i.e. machines) available matching the `config.yml` you specified, the chute should become hot 🔥!
-```bash
-chutes warmup <chute-id>
-```
-
-4. Test the chute's endpoints
-```bash
-curl -X POST https://<YOUR-CHUTE-SLUG>.chutes.ai/health -d '{}' -H "Authorization: Bearer $CHUTES_API_KEY"
-curl -X POST https://<YOUR-CHUTE-SLUG>.chutes.ai/predict -d '{"url": "https://scoredata.me/2025_03_14/35ae7a/h1_0f2ca0.mp4","meta": {}}' -H "Authorization: Bearer $CHUTES_API_KEY"
-```
-
-5. Test what your chute would get on a validator (this also applies any validation/integrity checks which may fail if you did not use the Turbovision CLI above to deploy the chute)
-```bash
-sv -vv run-once
-```