Compare commits

2 Commits

Author SHA1 Message Date
Awen Lelu
57714071da remove crap, erd and architecure diagram
- a bit of cleaning of useless files
- finished ERD diagram
- added architecture diagram
    - added to dossier as well
2026-03-10 10:32:47 +01:00
Awen Lelu
7340e61a30 add models and temps UML
- add new models
- temp change to UML diagrams
2026-03-08 12:43:23 +01:00
32 changed files with 1363 additions and 82 deletions

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# HealthAI Coach
An ai coach that helps you for a better shape.
## Développement
### Backend
> [!note] Pré requis
>
> Vous devez avoir nix et docker/podman d'installer
Téléchargement des dépendences et lancement d'un environnement de
développement.
```bash
nix develop # télécharge les dépendences du projet
```
Lancement de la base de donnée et de grafana
```bash
docker compose up -d
```
Lancement du backend
```bash
cd backend
# créer les tables néscéssaires dans la base de donnée
python manage.py migrate
python manage.py runserver # lance le serveur de développement
```
l'api sera disponible sur l'url [](http://localhost:8080/api)
l'interface d'administration sur l'url [](http://localhost:8080/admin) (admin/admin)
la documentation swagger sur l'url [](http://localhost:8080/api/schema/swagger-ui/)
**Grafana** sera disponible sur l'url [](http://localhost:3000) (admin/admin par défaut)
### Configuration Grafana
Après le démarrage, configurez la source de données PostgreSQL dans Grafana :
**Option 1 : Script automatique (recommandé)**
```powershell
.\grafana\setup_datasource.ps1
```
**Option 2 : Configuration manuelle**
Voir le guide dans `grafana/SETUP.md`
## Déploiement
```bash
# clone le repository
git clone https://gitlab.com/la-team-du-sang-de-la-veine/healthai-coach.git
cd healthai-coach
# copiez la configuration d'example et remplissez les champs de mot
# de passe pour la base de donnée.
cp docker-compose.production.example.yml docker-compose.production.yml
# la commande de deploy vas builder les composants néscéssaires et lancer les
# services via docker
docker compose up -d -f docker-compose.production.yml
```

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# yaml-language-server: $schema=https://raw.githubusercontent.com/mfontanini/presenterm/master/config-file-schema.json
options:
implicit_slide_ends: true
strict_front_matter_parsing: false
defaults:
theme: light
export:
dimensions:
columns: 106
rows: 75

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---
title: "MSPR EPSI - HealthAI"
sub_title: "Rapport technique et guide de déploiement"
authors:
- Abdellah Ait Abdelkarim
- Awenn lelu
- Noah Lejolivet
- Thibau Lasblei
- https://gitlab.com/la-team-du-sang-de-la-veine/healthai-coach
---
Table de matières
===
<!-- column_layout: [1,4,1] -->
<!-- column: 1 -->
# Mise en contexte
# Choix technologiques
## Backend
### Framework
### Bases de donnée
## Visualisation
## Environnement de développement
## Environnement de déploiement
# résultat obtenus
# difficultés
# évolutions
Mise en contexte
===
L'entreprise healthAI coach cherche à mettre en place une application
de conseil d'hygiène de vie orienté autour d'une aproche holistique (
considérer la santé comme un ensemble de système interagissant entre eux
plutôt qu'un seul point précis) de la santé.
Dans ce cadre, le but de ce projet est de concevoire une solution logicelle
satisfsaisant les points suivants:
# Business model
La monétisation de l'application doit être assurée par 2 paliers d'abonnements.
Les utilisateurs non abonnés ont accès aux fonctionnalités suivantes:
- journal alimenaire, suivis d'activité, IMC, tableaux de progressions simples
Les 2 paliers suivants donnes accèes aux fonctions suivantes:
- Premium **(9.99€ / mois)**
- accès à des recommendations spécialisés fournis par IA, des plans
nutritionels et sportifs plus détailés, et un suivis plus fin des
objectifs.
- Premium +
- Gestion des données lié au sommeil / poid / fréquence cardiaque,
consultation en ligne avec nutritionistes partenaires.
En plus de ces paliers d'abonnements, l'application dois pouvoir être vendus
en **B2B** à des entreprises partenaires avec la possibilités pour ces
dernières d'apposer leur branding.
Mise en contexte
===
# Cibles
Les cibles de l'application sont:
- Millenials / GenZ
- urbains actifs ne disposant que d'un temps limité pour leur activité sportive
- débutant en nutrition / sport
- personne avec des objectifs spécifiques
- perte de poid
- renforcement musculaire
- amélioration de sommeil
- ...
# Différentation concurentielle
Le marché étant fortement concurenciel, l'application se différencie par les
points suivants:
- approche hollistique de la santé
- intégration de l'IA générative
- stratégie B2B permettant une source de revenu / visibilité suplémentaire
# Scalabilité technologique
Afin de soutenir sa croissance future et son modèle B2B, l'application
dois pouvoir être scalable au niveau de ses différents composants.
Ainsi, chaque module de l'application dois être construit sur de technologies
modernes, documentées et permettant d'assurer la stabilité et la
scalabilité de chaque module séparément, ainsi que leur déploiement et
maintenance pour des clients B2B.
Mise en contexte
===
# Objectif
Dans le cadre de ce bloc de développement, l'objectif est de fournis un
prototype de la partie **Ingestion de données** de l'application finale.
Il s'agis de définir le socle technique et structurel sur lequel les
développements ultérieurs vont se baser.
Le rendus final doit être capable d'ingérer des set de données provenant
de multiples source de données et de les convertir dans un format standardisé
afin d'être exploité par les parties ultérieurs de l'application.
Les objectifs concrêts sont les suivants:
- Mettre en place un système de collecte et de traitement autmatiser des
données de manière fiable et sécuriser.
- Développer un processus de transformation de ces données afin de garantir
l'exploitation de ces dernières dans des outils ultérieurs
- Proposer une **API REST** permettant aux clients mobiles et web d'accéder
à ses données.
- livrer une interface de visualisation permettant de suivre les indicateurs
clés de l'application (revenu, nb d'utilisateurs, abonnements, ect...)
Choix technologique
===
# Backend
## Framework
Le choix à été fait d'utiliser [Django](https://www.djangoproject.com/)
et [Django Rest Framework](https://www.django-rest-framework.org/) afin de
gérer la partie **API** de l'application.
Ce choix est motivé par le fait que
*Django* est un framework reconnu comme étant particulièrement maintenable sur
le long terme, il est conçus avec le language *Python*, qui, bien que simple
à apprendre, ce qui en fait un plus pour l'intégration de nouveaux arrivant,
est un language extrêmement facile à déployé sur de multiples plateformes,
et disposant d'un écosystème riche autant pour le web que pour le traitement
de données et l'IA.
De plus, Django propose nativement une interface d'administration extensible
permettant d'interagir avec les données stockée dans la base de données,
ainsi qu'une gestion des rôles et des utilisateurs, permettant de répondre
à ces besoins pour un coût de développement minime.
Django propose de plus un ORM pouvant s'intégrer dans des scripts externes
permettant de standardiser la manière dont la base de données est accéder,
fiabilisant les opérations de lectures et d'écritures.
## Bases de donnée
### [Postgres](https://www.postgresql.org/)
Base de donnée principale servant au stockage "*froid*" des données,
postgres dispose d'un large écosystème d'utilisateur et de libairies associés et
est reconnu comme une solution performante et open source.
### [Valkey](https://valkey.io/)
Base de données de cache pour les requêtes api et les données d'entrainement
du futur modèle d'IA. **Valkey** est une base clé/valeur stockée en RAM qui implémente
la spécification de *Redis*, ce qui la rend compatible avec le parc de librairie
déjà en place pour cette dernière.
Choix technologique
===
# Visualisation
## [Grafana](https://grafana.com/oss/grafana/)
Grafana est une solution open source simple d'utilisation et extrêmement
versatile concernant les types et sources de données qu'elle peut aggréer.
Elle permet de concevoir des dashboard contenant une variété non négligeable
de graphiques et, de son côté open source, peut être étendre pour satisfaire
les besoin spécifiques du projet.
# Environnement de développement
## [nix](https://nixos.org/)
Nix est un manage de paquet permettant de créer des environnement de
développements et des paquets de manière déclarrative et reproductible.
Ce mécanisme permet d'assurer la reproductibilité de l'environnement de
développement d'un pc à un autre, et de manière isolée, permettant de
réduire les erreurs lié à l'environnement du développeur.
De plus, cela peut être utilisé comme outil de build avec les
mêmes avantages, permettant de réduire les outils de builds différents et
de simplifié drastiquement la maintenance de scripts de builds et de CI/CD.
## [git](https://git-scm.com/)
Le projet est versionné avec git afin d'assurer un historique des
modifications du code et que chaque développeur puisse intervenir sur
des branches spécifiques du code et les fusionner avec celles des autres.
Git est la référence moderne de ce type d'outil et est un choix par défaut
dans la plupart des projets.
## [gitlab](gitlab.com)
Gitlab est la forge logicielle concurente à github, contrairement à ce dernier,
il n'est pas détenu par Microsoft et est self hostable, ce qui fait que le
projet pourra être facilement porté si l'entreprise décide de self hoster son
infrastructure dans le futur. Gitlab intègre de même des outils de gestion de
projet via les issues boards, ainsi qu'une CI/CD solide.
Choix Technologique
===
# Environnement de déploiement
## Virtualisation
### Podman
Dans un soucis d'isolation des différents modules du projet, chaque
composant sera fournis sous la forme d'une image **podman** qui est une
implémentation open source du standard de **Docker**, ce dernier permet de
plus de fonctionner *rootless* par défaut, ce qui permet d'éviter les
escalations de privilèges si la daemon est compromis. De plus, les images
serons builder par **nix** afin d'assurer la reproductibilité des builds.
Le choix de conteurs comme environnement de déploiement permet de pouvoir
isoler les dépendences de chaque module, ainsi que de pouvoir les déployé,
stopper et redémarrer séparément. De plus, dans une optique de scalabilité,
il est possible de lancer plusieurs instances d'un module afin de distribuer
la charge sur toutes les instance disponibles.
Difficultés
===
La plus grosse difficulté du projet fut de déterminer l'approche que l'on
allait avoir concernant le traitement des données par l'IA, quel données était
pertinantes, comment utilisé l'IA dans des fonctions où elle aurais plus
de valeur ajouté qu'une approche déterministe.
Au final, l'approche retenue
est d'utilisé l'IA comme un moteur de suggestions et de concevoir l'interface
de l'application de manière "AI-first", c'est à dire de présenter l'application
comme une interface de conversation avec l'agent, ce dernier pouvant répondre
aux questions de l'utilisateur basée sur ses données biométriques et le
dataset auquel il a accès. L'utilisateur converse ainsi avec l'agent afin
de lui énoncé ses objectifs, lui demandé des exemples de repas et des exercices
sur le moment de son activité, conseils qu'il peut retrouver dans son historique.
Évolutions
===
# CI/CD
La mise en place d'un processus de développement continue permettant d'analyser
de façon automatique la qualité du code serais un plus non négligeable afin
d'assurer la stabilité long terme du produit.
De plus, un système de déploiement continue permettrais de réduire les marges
d'erreurs et les coûts lié à des déploiement manuels, ainsi que de mettre en place
différents environnements (production / développement) de manière automatisé.
# Tests
De la même façon que la mise en place d'une CI, la mise en place de Test unitaires
pour les pipelines d'ingestions et l'API permettrais de s'assurer de l'intégrité
du code au cours des développements successif. Ces points pourrais être
directement intégrer au seins du processus de développement via des méthodologies
de *TDD*
<!-- include: uml.md -->
Dossier de déploiement
===
<!-- include: ../README.md -->

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UML
===
# cas d'utilisations
## Admin
<!-- include: uml/usecases/admin.md -->
## User
<!-- include: uml/usecases/user.md -->
UML
===
# Séquence
## Consultation des données via API
<!-- include: uml/sequences/api.md -->
## Grafana
<!-- include: uml/sequences/grafana.md -->
UML
===
# Entity Relations
<!-- include: uml/ERD.md -->
UML
===
# Architecture
<!-- include: uml/architecture.md -->

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```mermaid +render
---
config:
layout: elk
---
erDiagram
UserProfile ||--o{ Objective: objective
UserProfile |o--o{ Subscription: subscription
UserProfile |o--o{ Diet: diet
UserProfile }o--o{ Ingredient: allergies
UserProfile ||--|| User: user
Ingredient |o--o{ Diet: diet
Recipe }|--o{ Ingredient: ingredients
ExercisePlan }|--o{ Exercise: exercises
ExercisePlan ||--o{ User: user
MealPlan }|--o{ Recipe: meals
MealPlan ||--o{ User: user
Exercise |o--o{ Equipement: equipements
Exercise |o--o{ BodyPart: bodyparts
Exercise |o--o{ Muscle: muscles
User{
int id
bool is_superuser
string password
datetime last_login
datetime date_joined
string username
string email
bool is_active
string first_name
string last_name
bool is_staff
}
UserProfile{
int id
date birthdate
bool sexe
string gender
integer weight
integer height
string picture
}
Objective{
int id
string name
string description
string image_url
}
Subscription {
int id
string name
int price
string description
string image_url
}
Ingredient{
int id
string name
string description
string type
int calories
int proteins
int lipids
int fibers
int sugars
int salt
}
Recipe{
int id
string name
int minute_preparation
int minute_preparation
string summary
string steps
string picture
}
MealPlan {
int id
datetime created_date
}
Diet {
int id
string name
string description
}
Exercise {
int id
string name
string image
string instructions
}
Equipement {
int id
string name
}
BodyPart {
int id
string name
}
Muscle {
int id
string name
}
ExercisePlan {
int id
datetime created_date
}
```

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```mermaid +render
architecture-beta
group back(Server)[HealhAI Back]
group api(cloud)[API] in back
group etl(cloud)[ETL] in back
group datavis(cloud)[Data visualisation] in back
service db(database)[Database] in api
service gunicorn(server)[gunicorn] in api
service grafana(cloud)[Grafana] in datavis
service django-admin(cloud)[Django Admin] in datavis
service redis(database)[Redis] in etl
service parser(server)[Parser] in etl
junction parserJunction in etl
junction redisJunction in etl
gunicorn:L -- R:db
parser:B -- T:redis
parser:R --> L:parserJunction
redis:R <-- L:redisJunction
parserJunction:B -- T:redisJunction
grafana:T <-- B:db
django-admin:T -- B:gunicorn
gunicorn:T -- B:redisJunction
```

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```mermaid +render
sequenceDiagram
actor Utilisateur
participant Client
participant API
participant Auth
participant Service
participant DB
Utilisateur->>Client: Demande consultation données
Client->>API: GET /api/paramètre
API->>Auth: Vérifier token JWT
API->>Service: récupérerLesDonnées
Service->>DB: SELECT données
```

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```mermaid +render
sequenceDiagram
participant Grafana
participant DB
Grafana->>DB: requêtes SQL statistiques
Grafana->>Grafana: affichage graphiques
```

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```mermaid +render
mindmap
root((Admin))
Utilisateur
Ajouter Utilisateur
Modifier Utilisateur
Supprimer Utilisateur
Aliment
Ajouter
Modifier
Supprimer
Excercice
Ajouter
Modifier
Supprimer
Abonnement
Ajouter
Modifier
Supprimer
Dataset
Exporter les données
Consulter les rapports d'erreur
```

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```mermaid +render
mindmap
root((User))
Créer un compte
Se Déconnecter
Se Connecter
Consultation
Données biométriques
Objectif
Journal d'activité
Catalogue nutritionel
Catalogue d'excercice
Abonnement
Consulter
Souscrire
Annuler
```

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@@ -72,11 +72,23 @@
virtualenv = pythonSet.mkVirtualEnv "healthai-dev-env" workspace.deps.all;
in
{
default = pkgs.mkShell {
dossier = pkgs.mkShellNoCC {
PRESENTERM_CONFIG_FILE = ./dossier/config.yaml;
packages = with pkgs; [
presenterm
yaml-language-server
mermaid-cli
pythonSet.python.pkgs.weasyprint
];
};
default = pkgs.mkShellNoCC {
packages = [
virtualenv
pkgs.uv
pythonSet.python.pkgs.setuptools
pkgs.ruff
pkgs.taplo
];
env = {
UV_NO_SYNC = "1";

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@@ -2,12 +2,13 @@
name = "healthai"
version = "0.1.0"
description = "Add your description here"
requires-python = ">=3.12"
requires-python = ">=3.13"
dependencies = [
"django>=6.0.2",
"django-cors-headers>=4.9.0",
"djangorestframework>=3.16.1",
"drf-spectacular>=0.29.0",
"pillow>=12.1.1",
"psycopg2-binary>=2.9.11",
"redis>=7.2.1",
]
@@ -19,3 +20,10 @@ dependencies = [
requires = ["uv_build>=0.10.0,<0.11.0"]
build-backend = "uv_build"
[dependency-groups]
dev = [
"black>=26.3.0",
"python-lsp-server>=1.14.0",
"jedi-language-server>=0.46.0",
]

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@@ -2,4 +2,4 @@ from django.apps import AppConfig
class ApiConfig(AppConfig):
name = 'api'
name = "api"

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# Generated by Django 6.0.2 on 2026-03-08 11:25
import datetime
import django.db.models.deletion
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name="Bodypart",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=200)),
],
),
migrations.CreateModel(
name="Diet",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=200)),
("description", models.TextField()),
],
),
migrations.CreateModel(
name="Equipement",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=200)),
],
),
migrations.CreateModel(
name="Muscle",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=200)),
],
),
migrations.CreateModel(
name="Objectif",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=200)),
("description", models.TextField()),
(
"image_url",
models.ImageField(upload_to="", verbose_name="objectives"),
),
],
),
migrations.CreateModel(
name="Recipe",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("nom", models.CharField(max_length=300)),
("minutes_preparation", models.IntegerField()),
("minutes_cuisson", models.IntegerField()),
("summary", models.TextField()),
("steps", models.TextField()),
("picture", models.ImageField(upload_to="", verbose_name="recipes")),
],
),
migrations.CreateModel(
name="Subscription",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=200)),
("price", models.FloatField()),
("description", models.TextField()),
("image_url", models.ImageField(upload_to="subscriptions")),
],
),
migrations.CreateModel(
name="Exercise",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=200)),
("instuctions", models.TextField()),
("image", models.ImageField(upload_to="exercise")),
(
"bodyparts",
models.ManyToManyField(related_name="exercises", to="api.bodypart"),
),
(
"equipements",
models.ManyToManyField(
related_name="exercises", to="api.equipement"
),
),
(
"muscles",
models.ManyToManyField(related_name="exercises", to="api.muscle"),
),
],
),
migrations.CreateModel(
name="Ingredient",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=200)),
("desription", models.TextField()),
("type", models.CharField(max_length=50)),
("calories", models.IntegerField()),
("proteins", models.IntegerField()),
("lipids", models.IntegerField()),
("fibers", models.IntegerField()),
("sugars", models.IntegerField()),
("salts", models.IntegerField()),
("is_allergen", models.BooleanField(default=False)),
(
"diet",
models.ForeignKey(
blank=True,
null=True,
on_delete=django.db.models.deletion.SET_NULL,
related_name="ingredients",
to="api.diet",
),
),
],
),
migrations.CreateModel(
name="UserProfile",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("birthdate", models.DateField()),
("sexe", models.CharField()),
("weight", models.IntegerField()),
("height", models.IntegerField()),
("picture", models.ImageField(upload_to="profiles")),
(
"allergies",
models.ManyToManyField(related_name="users", to="api.ingredient"),
),
(
"diet",
models.ForeignKey(
blank=True,
null=True,
on_delete=django.db.models.deletion.SET_NULL,
related_name="users",
to="api.diet",
),
),
(
"objective",
models.ForeignKey(
null=True,
on_delete=django.db.models.deletion.SET_NULL,
related_name="users",
to="api.objectif",
),
),
(
"subscription",
models.ForeignKey(
null=True,
on_delete=django.db.models.deletion.SET_NULL,
related_name="users",
to="api.subscription",
),
),
(
"user",
models.OneToOneField(
on_delete=django.db.models.deletion.CASCADE,
to=settings.AUTH_USER_MODEL,
),
),
],
),
migrations.CreateModel(
name="ExercisePlan",
fields=[
(
"id",
models.BigAutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
(
"created_date",
models.DateTimeField(default=datetime.datetime.now, editable=False),
),
(
"exercises",
models.ManyToManyField(related_name="plans", to="api.exercise"),
),
(
"user",
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name="plans",
to="api.userprofile",
),
),
],
),
]

View File

@@ -1,3 +1,114 @@
from django.db import models
from django.contrib.auth.models import User
from datetime import datetime
# Create your models here.
class UserProfile(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE)
birthdate = models.DateField()
sexe = models.BooleanField() # bio sex
gender = models.CharField(max_length=200) # gender identity
weight = models.IntegerField() # in g
height = models.IntegerField() # in cm
picture = models.ImageField(upload_to="profiles")
allergies = models.ManyToManyField("Ingredient", related_name="users")
diet = models.ForeignKey(
"Diet", on_delete=models.SET_NULL, null=True, blank=True, related_name="users"
)
objective = models.ForeignKey(
"Objectif", on_delete=models.SET_NULL, null=True, related_name="users"
)
subscription = models.ForeignKey(
"Subscription", related_name="users", on_delete=models.SET_NULL, null=True
)
# exercise_plans / exercises [old]
# will be better to have an object "exercise plan"
# wich store the current plan for the user along
# with the date of plan generation.
class Objective(models.Model):
name = models.CharField(max_length=200)
description = models.TextField()
image_url = models.ImageField("objectives")
class Subscription(models.Model):
# Plan
name = models.CharField(max_length=200)
price = models.FloatField() # in USD
description = models.TextField()
image_url = models.ImageField(upload_to="subscriptions")
class Diet(models.Model):
name = models.CharField(max_length=200)
description = models.TextField()
class Ingredient(models.Model):
# Aliment
name = models.CharField(max_length=200)
description = models.TextField()
type = models.CharField(max_length=50)
calories = models.IntegerField()
proteins = models.IntegerField() # in mg/100g
lipids = models.IntegerField() # in mg/100g
fibers = models.IntegerField() # in mg/100g
sugars = models.IntegerField() # in mg/100g
salts = models.IntegerField() # in mg/100g
is_allergen = models.BooleanField(default=False)
diet = models.ForeignKey(
Diet,
related_name="ingredients",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
class Recipe(models.Model):
name = models.CharField(max_length=300)
minutes_preparation = models.IntegerField()
minutes_cuisson = models.IntegerField()
summary = models.TextField()
steps = models.TextField()
picture = models.ImageField("recipes")
ingredients = models.ManyToManyField(Ingredient, related_name="ingredients")
class ExercisePlan(models.Model):
created_date = models.DateTimeField(default=datetime.now, editable=False)
exercises = models.ManyToManyField("Exercise", related_name="plans")
user = models.ForeignKey(
UserProfile, related_name="exercise_plans", on_delete=models.CASCADE
)
class MealPlan(models.Model):
created_date = models.DateTimeField(default=datetime.now, editable=False)
meals = models.ManyToManyField("Recipe", related_name="plans")
user = models.ForeignKey(
UserProfile, related_name="meal_plans", on_delete=models.CASCADE
)
class Exercise(models.Model):
name = models.CharField(max_length=200)
instuctions = models.TextField()
image = models.ImageField(upload_to="exercise")
equipements = models.ManyToManyField("Equipement", related_name="exercises")
bodyparts = models.ManyToManyField("Bodypart", related_name="exercises")
muscles = models.ManyToManyField("Muscle", related_name="exercises")
class Equipement(models.Model):
name = models.CharField(max_length=200)
class Bodypart(models.Model):
name = models.CharField(max_length=200)
class Muscle(models.Model):
name = models.CharField(max_length=200)

View File

@@ -2,4 +2,4 @@ from django.apps import AppConfig
class EtlConfig(AppConfig):
name = 'etl'
name = "etl"

Binary file not shown.

View File

@@ -11,6 +11,6 @@ import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'healthai.settings')
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "healthai.settings")
application = get_asgi_application()

View File

@@ -20,7 +20,7 @@ BASE_DIR = Path(__file__).resolve().parent.parent
# See https://docs.djangoproject.com/en/6.0/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-*sbfn+r!9%p$u@ugm=5877*#ee$uukoeh*q@=%%ow1o$#4ad#x'
SECRET_KEY = "django-insecure-*sbfn+r!9%p$u@ugm=5877*#ee$uukoeh*q@=%%ow1o$#4ad#x"
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
@@ -31,51 +31,53 @@ ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
"django.contrib.admin",
"django.contrib.auth",
"django.contrib.contenttypes",
"django.contrib.sessions",
"django.contrib.messages",
"django.contrib.staticfiles",
"api",
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
"django.middleware.security.SecurityMiddleware",
"django.contrib.sessions.middleware.SessionMiddleware",
"django.middleware.common.CommonMiddleware",
"django.middleware.csrf.CsrfViewMiddleware",
"django.contrib.auth.middleware.AuthenticationMiddleware",
"django.contrib.messages.middleware.MessageMiddleware",
"django.middleware.clickjacking.XFrameOptionsMiddleware",
]
ROOT_URLCONF = 'healthai.urls'
ROOT_URLCONF = "healthai.urls"
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
"BACKEND": "django.template.backends.django.DjangoTemplates",
"DIRS": [],
"APP_DIRS": True,
"OPTIONS": {
"context_processors": [
"django.template.context_processors.request",
"django.contrib.auth.context_processors.auth",
"django.contrib.messages.context_processors.messages",
],
},
},
]
WSGI_APPLICATION = 'healthai.wsgi.application'
WSGI_APPLICATION = "healthai.wsgi.application"
# Database
# https://docs.djangoproject.com/en/6.0/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
"default": {
"ENGINE": "django.db.backends.sqlite3",
"NAME": BASE_DIR / "db.sqlite3",
}
}
@@ -85,16 +87,16 @@ DATABASES = {
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
"NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator",
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",
},
]
@@ -102,9 +104,9 @@ AUTH_PASSWORD_VALIDATORS = [
# Internationalization
# https://docs.djangoproject.com/en/6.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
LANGUAGE_CODE = "en-us"
TIME_ZONE = 'UTC'
TIME_ZONE = "UTC"
USE_I18N = True
@@ -114,4 +116,4 @@ USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/6.0/howto/static-files/
STATIC_URL = 'static/'
STATIC_URL = "static/"

View File

@@ -14,9 +14,10 @@ Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path
urlpatterns = [
path('admin/', admin.site.urls),
path("admin/", admin.site.urls),
]

View File

@@ -11,6 +11,6 @@ import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'healthai.settings')
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "healthai.settings")
application = get_wsgi_application()

View File

@@ -1,12 +1,13 @@
#!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
"""Run administrative tasks."""
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'healthai.settings')
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "healthai.settings")
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
@@ -18,5 +19,5 @@ def main():
execute_from_command_line(sys.argv)
if __name__ == '__main__':
if __name__ == "__main__":
main()

411
uv.lock generated
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revision = 3
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[[package]]
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version = "0.29.0"
@@ -85,20 +159,48 @@ dependencies = [
{ name = "django-cors-headers" },
{ name = "djangorestframework" },
{ name = "drf-spectacular" },
{ name = "pillow" },
{ name = "psycopg2-binary" },
{ name = "redis" },
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{ name = "djangorestframework", specifier = ">=3.16.1" },
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{ name = "psycopg2-binary", specifier = ">=2.9.11" },
{ name = "redis", specifier = ">=7.2.1" },
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[package.metadata.requires-dev]
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{ name = "jedi-language-server", specifier = ">=0.46.0" },
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