Food Recommendation API

NOTE: please don't change everything in main branch, if you want to change you have to create new branch with your desired name.

You can also check how the main branch is developed by checking the other branches, the branches are as follow....

• sample-api: testing fastapi, just pure
• api-with-mongodb: testing with mongodb database

Run the docker container

docker-compose up -d

1. Clone the repo

git clone https://github.com/Hein-HtetSan/api-for-sarrmal-app.git

Create env dir and credentials.env

mkdir env
cd env
touch credentials.env

2. Create a Virtual Environment

python -m venv venv

3. Activate the Virtual Environment

# for windows
venv\Scripts\activate

# for mac
source venv/bin/activate

4. Install Dependencies

pip install -r requirements.txt

5. After installing new packages, don't forget to update (optional)

pip freeze > requirements.txt

6. Project Structures

app/
├── venv/                     # Virtual environment directory
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── api/
│   │   ├── __init__.py
│   │   ├── endpoints/
│   │   │   ├── __init__.py
│   │   │   ├── recommendations.py
│   │   │   ├── users.py
│   │   │   ├── foods.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── config.py
│   │   ├── security.py
│   ├── db/
│   │   ├── __init__.py
│   │   ├── mongodb.py
│   │   ├── models/
│   │   │   ├── __init__.py
│   │   │   ├── user.py
│   │   │   ├── food.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── recommendation_model.py
│   ├── schemas/
│   │   ├── __init__.py
│   │   ├── user.py
│   │   ├── food.py
│   │   ├── recommendation.py
│   ├── services/
│   │   ├── __init__.py
│   │   ├── recommendation_service.py
│   │   ├── user_service.py
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── common.py
│   └── tests/
│       ├── __init__.py
│       ├── test_recommendations.py
│       ├── test_users.py
│       ├── test_foods.py
├── data/
│   ├── raw/
│   ├── processed/
│   ├── trained_model/
│   │   ├── model.joblib
├── notebooks/
│   ├── data_preprocessing.ipynb
│   ├── model_training.ipynb
├── requirements.txt
├── .env
└── README.md

Libs included

• fastapi: The main framework for building your API.
• uvicorn: ASGI server to run your FastAPI application.
• pydantic: For data validation and settings management.
• motor: Asynchronous MongoDB driver.
• pandas: Data manipulation and analysis library.
• scikit-learn: For machine learning algorithms and model handling.
• joblib: For model serialization and deserialization.
• python-dotenv: To load environment variables from a .env file.
• pytest: Framework for writing and running tests.
• httpx: For making HTTP requests in your tests.
• flake8: For checking the style and quality of your code.
• black: Code formatter.

Project Structure Breakdown

your_app/:

The root directory of your FastAPI project.

venv/:

Virtual environment directory containing all the dependencies and packages installed for this project.

app/:

Main application directory containing the core components of your FastAPI project.

init.py:

Marks the app directory as a Python package. main.py:

The entry point of the FastAPI application where the app is initialized and routes are included.

api/:

Contains the API-related modules and routing.

init.py:

Marks the api directory as a Python package.

endpoints/:

Contains individual API endpoints or routes for different functionalities.

init.py:

Marks the endpoints directory as a Python package. recommendations.py:

API endpoints related to generating and managing food recommendations. users.py:

API endpoints related to user management, such as registration, login, and profile updates. foods.py:

API endpoints related to managing food items in the database.

core/:

Core functionalities and configurations of the application.

init.py:

Marks the core directory as a Python package. config.py:

Application configuration settings, including environment variables and database connections. security.py:

Security-related functions, such as password hashing, JWT token creation, and authentication.

db/:

Database-related modules and models.

init.py:

Marks the db directory as a Python package. mongodb.py:

MongoDB connection and database handling logic.

models/:

Contains database models that define the structure of data in MongoDB.

init.py:

Marks the models directory as a Python package. user.py:

Database model representing a user in the system. food.py:

Database model representing a food item in the system.

models/:

Contains machine learning models or any other business-related models.

init.py:

Marks the models directory as a Python package. recommendation_model.py:

Handles loading and using the machine learning model for food recommendations.

schemas/:

Pydantic models (schemas) used for data validation and serialization.

init.py:

Marks the schemas directory as a Python package. user.py:

Schemas for user-related data validation (e.g., registration, login). food.py:

Schemas for food-related data validation. recommendation.py:

Schemas for recommendation-related data validation.

services/:

Business logic and services that interact with the database and models.

init.py:

Marks the services directory as a Python package. recommendation_service.py:

Business logic for generating and managing food recommendations. user_service.py:

Business logic for managing users.

utils/:

Utility functions and common helpers used across the application.

init.py:

Marks the utils directory as a Python package. common.py:

Common utility functions used throughout the application.

tests/:

Test cases for the application, ensuring all features work as expected.

init.py:

Marks the tests directory as a Python package. test_recommendations.py:

Test cases for the recommendation functionality. test_users.py:

Test cases for user-related functionality. test_foods.py:

Test cases for food-related functionality.

data/:

Directory for storing datasets and trained models.

raw/:

Raw, unprocessed data files.

processed/:

Processed data files ready for use in model training.

trained_model/:

Contains trained machine learning models, like model.joblib.

notebooks/:

Jupyter notebooks for data analysis, preprocessing, and model training.

data_preprocessing.ipynb:

Notebook for data preprocessing. model_training.ipynb:

Notebook for training the machine learning model.

requirements.txt:

Lists all the Python packages required for the project.

.env:

Environment variables, such as database connection strings and secret keys.

README.md:

Project documentation providing an overview of the application, how to set it up, and how to use it.