upload the project

README.md

@@ -1,2 +1,124 @@
-# Hairloss-report
-Uses multiple ML models to . This project only generates the data, the graphs and a galery view of the reports. The report must be written by the user.
+# Hairloss report
+
+Initially intended as a joke, but I decided to publish it in case someone wants to reuse it later.
+
+Note : It will only produce the graphs of the receding hairline and a gallery view of each pictures with its relevent score. You will need to write the report yourself.
+
+This project was written in a rush (less than two days before leaving to vacation) and, as such, uses extensively AI (ChatGPT 5.5 free tier). I might (or might not) come back later to polish it a bit.
+
+# Preparation
+
+### Models
+
+You will need to have pulled the models (this will take multiple gigas of storage) :
+```
+ollama pull llava:7b
+ollama pull ministral-3:8b
+ollama pull gemma4:12b
+ollama pull qwen3-vl:8b
+```
+
+This report's strategy is to use multiple models and to repeat multiple time averaging the results. The models used are :
+* llava:7b (10x repetitions)
+* ministral-3:8b (10x repetitions)
+* gemma4:12b (3x repetitions)
+* qwen3-vl:8b (1x repetitions)
+
+If you want to change the models or the number of repetitions, a quick run of `rg -C3 {your model here}` should show you where are the changes needed.
+
+### Images
+
+For privacy reason, this repo does not contain the initial dataset of images.
+
+Your dataset needs to :
+* focus on thethe person's head. Crop pictures if needed.
+* follow this naming logic : `yyyy-mm-dd-nna` where `yyyy-mm-dd` follows [iso 8601](https://en.wikipedia.org/wiki/ISO_8601), `nn` is a number between 00 and 99 (used in cases there are multiple images for the same day and `a` is the angle of the photo (`t` for top, `l` for lateral and `f` for face). If you don't know the exact date, estimate it. There are two helper script in this repo (just fix the shebang and directory paths). `rename.sh` renames all the files (this could break stuff, be careful) into their last modified date. You just need to crop them and add the number and angles. `checkNaming.sh` checks if the files are correctly named (works on `.png` for other format adapt the regex).
+* You should try to use the best quality pictures.
+
+
+### Graphs
+
+You can browse the repo for an example of the graphs and the tex file for the gallery.
+
+One feature of this project is that you can focus on the period after one stres--inducing event (difficult work, sickness, annoying person, ...) that you hypothesize it will impact the rate of hairloss.
+You can change this date by running `sed -i "s|2023-09-11|{Put here your date}|g" ./hairloss/main.py`
+
+If you have multiple such events, you will need to manually modify the code in `./hairloss/main.py`.
+
+# Usage
+
+## On [NixOS](https://nixos.org/)
+
+Clear the examples graphs : `rm *.pdf`
+
+You must set the path to the images directory as the nix store doesn't work with relative paths.
+```
+sed -i "s|PathToYourLocalGitClone/Images|$(pwd)/Images|g" flake.nix
+```
+
+Generate the values. This might take some time as it uses multiple ML models and repeats the prompt for less noise. For comparison, this took a few hours on my RTX3070ti.
+
+Note : if you want to run on a GPU you must replace the `ollama` package by a [gpu-specific one](https://search.nixos.org/packages?type=packages&query=ollama) by running `sed -i "s|ollama|{Put here your ollama variant}|g" flake.nix`
+
+```
+nix run . -- --generate_with_llava
+nix run . -- --generate_with_gemma
+nix run . -- --generate_with_qwen
+nix run . -- --generate_with_ministral
+```
+
+Note : you can stop at every point. It will resume its calculations at the last image.
+
+Compute the averages :
+```
+nix run . -- --generate_averages
+```
+
+Create the gallery view :
+```
+nix run . -- --generate_gallery
+nix develop .
+pdflatex ./gallery.tex -interaction=nonstopmode
+```
+
+You now have all the files in pdf. You can create your own report !
+
+## On other systems
+
+This might or might not work as I did everything on NixOS.
+
+Clear the examples graphs : `rm *.pdf`
+
+Install dependencies :
+* ollama
+* Python and pip
+* your favorite LaTeX distribution
+
+Install the project :
+```
+pip install .
+```
+
+Generate the values. This might take some time as it uses multiple ML models and repeats the prompt for a more accurate count. For compairon, this took a few hours on my RTX3070ti.
+
+```
+hairloss --generate_with_llava
+hairloss --generate_with_gemma
+hairloss --generate_with_qwen
+hairloss --generate_with_ministral
+```
+
+Note : you can stop at every point. It will resume it's calculations at the last image.
+
+Run the averages :
+```
+hairloss --generate_averages
+```
+
+Create the gallery view :
+```
+hairloss --generate_gallery
+pdflatex ./gallery.tex -interaction=nonstopmode
+```
+
+You now have all the files in pdf. You can create your own report !