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docs/index.md

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+# TEMPS documentation!
+
+Welcome to the documentation for TEMPS! 
+This repository contains a neural network to predict photometric redshifts. The neural network incorporates domain adaptation, a methodology to mitigate the impact of sample bias in the spectroscopic training samples. 
+
+The training and validation data are not available in the repository, but the model can be trained with new data. The model is also deployed and available [here](https://huggingface.co/spaces/lauracabayol/TEMPS). The model in production enables making predictions for new galaxies with the pretrained models. 
+
+## Table of Contents
+
+- [Prerequisites](#prerequisites)
+- [Installation](#installation)
+- [Usage](#deployed-model)
+- [Notebooks](#notebooks)
+- [Training the Model](#training-the-model)
+
+## Prerequisites
+
+Before proceeding, ensure that the following software is installed on your system:
+
+- Python 3.10
+- [pip](https://pip.pypa.io/en/stable/installation/)
+- [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+
+You will also need to clone the repository to your local environment by executing the following commands:
+
+```bash
+git clone https://github.com/lauracabayol/TEMPS
+cd TEMPS
+```
+## Installation
+
+We recommend using a conda environment with Python 3.10 by executing the following commands:
+```
+conda create -n temps -c conda-forge python=3.10
+conda activate temps
+```
+Once your environment is ready, proceed with the installation of the package:
+
+```
+pip install -e .
+```
+This will already install the dependencies. 
+
+
+## Deployed model
+Alternatively, one can access the deployed models at [HuggigFace](https://huggingface.co/spaces/lauracabayol/TEMPS). This enbles making predictions from a file with photometric measurements. The format should be a csv file with the following band photometries in this order: G,R,I,Z,Y,H,J. 
+
+
+## Notebooks
+The repository contains notebooks to reproduce the figures in the paper (to be updated with the link)
+The notebooks are loaded on GitHub as .py files. To convert them to .ipynb use <jupytext>
+
+```bash
+jupytext --to ipynb notebooks/*.py
+```
+
+## Training the Model
+The model can be trained using the train.py script at the repo main directory. 
+
+```
+python train.py --config-file data/config.yml
+```
+One only needs to modify the config file to point at the input files. Make sure to also specify the photometric bands naming, and the spectroscopic and photometric redshift columns. 
+Input catalogs must be in .fits or .csv formats and these should already contain clean photometry. 
+
+If extinction_corr is set to True, one must specify the column namings of the E_VB corrections in the config file. 
+
+## License
+
+This project is licensed under the MIT License. You are free to use, modify, and distribute this project as long as you adhere to the license terms.