Meal Recommendation API

This is a FastAPI-based application that recommends meals based on user preferences such as height, weight, age, allergies, lifestyle, and previous meals. The application uses a machine learning model to retrieve the best meal suggestions from a dataset of meal ingredients.

Prerequisites

Before you can run the app, you need to have the following installed:

• Python 3.8+ (preferably Python 3.10 or higher) (Ex: Python 3.10.7)
• pip (Python package installer)

Setup Instructions

1. Clone the Repository

Clone this repository to your local machine:

    git clone https://github.com/vinhnguyenhoang/generate_meal_api
    cd generate_meal_api

2. Create a Virtual Environment

Create a virtual environment to manage dependencies:

python -m venv venv

Activate the virtual environment:

On Windows:

.\venv\Scripts\activate

On macOS/Linux:

source venv/bin/activate

3. Install Dependencies

Once the virtual environment is activated, install the required dependencies:

pip install -r requirements.txt

4. Prepare the Data

Make sure the meal_ingredients.csv file is present in the project directory. This file should contain meal data, including meal_id, meal_name, ingredient_name, and ingredient_qty.

5. Run the Application

Start the FastAPI server with Uvicorn:

uvicorn main:app --reload

By default, the app will be accessible at http://127.0.0.1:8000.

6. Run the Application using Docker

To run the application using Docker, follow these steps:

Step 1: Install Docker and .env Make sure Docker is installed on your system and create file .env (include GROQ_API_KEY)

Step 2: Build the Docker Image Navigate to the project directory and build the Docker image. Run the following command:

    docker build -t meal-recommendation-api .

This command will build the Docker image and tag it as meal-recommendation-api.

Step 3: Run the Docker Container If you are currently in the project directory and want to run the application with Docker, you can use the following command to start the container and map the code folder on your machine to the container:

    docker run -p 8000:8000 -v $(pwd):/app meal-recommendation-api

-p 8000:8000: This will map port 8000 on your local machine to port 8000 on the container, so you can access the app at http://localhost:8000. -v $(pwd):/app: This will map the current directory (where your code is located) on your local machine to the /app directory inside the container. This allows you to change the code without rebuilding the Docker image. (or -v ./:/app) meal-recommendation-api: This is the name of the Docker image you built in the previous step.

Step 4: Access the Application Once the container is running, you can access the application by opening your browser and navigating to: http://localhost:8000

Step 5: Stopping the Docker Container To stop the running Docker container, you can use the following command:

    docker ps  # List running containers
    docker stop <container_id>  # Stop the container

Replace <container_id> with the actual container ID you want to stop.

Notes: Development Mode: Since you're mapping the code from your local machine to the container, you don't need to rebuild the Docker image every time you make changes to the code. Docker will reflect the changes automatically. --reload flag for uvicorn: If you're using FastAPI with uvicorn, ensure that the --reload flag is set (either in your Dockerfile or manually), so the app reloads when code changes.

7. Test the API Using Postman

You can use Postman to test the API. Below are the details for testing the /recommend endpoint:

Endpoint: POST /recommend This endpoint recommends meals based on the user's preferences. It expects a JSON payload in the body of the request.

Request body format:

{
    "height": 170,
    "weight": 70,
    "age": 25,
    "preferences": "Vegetarian",
    "lifestyle": "Active",
    "diet": "Low-carb",
    "allergy": "None",
    "previous_meals": ["Salad", "Grilled chicken"]
}

Example Request in Postman: URL: http://127.0.0.1:8000/recommend Method: POST Body: Select the raw option and set the body type to JSON, then paste the sample JSON above. Expected Response:

{
    "recommended_meal_ids": [123, 456, 789]
}

This will return an array of meal_ids that match the user's preferences.

8. Accessing the Documentation

FastAPI provides automatic API documentation. You can access the interactive API docs at the following URLs:

Swagger UI: http://127.0.0.1:8000/docs ReDoc: http://127.0.0.1:8000/redoc These pages will help you explore and test the available API endpoints.

Troubleshooting Error: ModuleNotFoundError

Ensure that all dependencies are installed by running pip install -r requirements.txt. Error: File not found

Make sure the meal_ingredients.csv file exists in the same directory as the app.

Summary:

This README.md file provides step-by-step instructions for setting up the FastAPI app, running it local