How to run

1. Clone this repository

To get started, clone this repository onto your local machine. Follow the instructions below:

1. Open a terminal or Command Prompt.
2. Change to the directory where you want to clone the repository.
3. Enter the following command to clone the repository:

   git clone https://github.com/MuhFaridanSutariya/jakarta-pollution-classification.git

4. Once the cloning process is complete, navigate into the cloned directory using the cd command:

   cd jakarta-pollution-classification

2. System Requirements

Make sure your system meets the following requirements before proceeding:

• Python 3.10+ is installed on your computer.
• Pip (Python package installer) is installed.

3. Create a Virtual Environment

A virtual environment will allow you to separate this project from the global Python installation. Follow these steps to create a virtual environment:

On Windows: Open Command Prompt and enter the following command:

python -m venv virtualenv_name

Replace virtualenv_name with the desired name for your virtual environment.

On macOS and Linux: Open the terminal and enter the following command:

python3 -m venv virtualenv_name

Replace virtualenv_name with the desired name for your virtual environment.

4. Activate the Virtual Environment

After creating the virtual environment, you need to activate it before installing the requirements. Use the following steps:

On Windows: In Command Prompt, enter the following command:

virtualenv_name\Scripts\activate

Replace virtualenv_name with the name you provided in the previous step.

On macOS and Linux: In the terminal, enter the following command:

source virtualenv_name/bin/activate.bat

Replace virtualenv_name with the name you provided in the previous step.

5. Install Requirements

Once the virtual environment is activated, you can install the project requirements from the requirements.txt file. Follow these steps:

On Windows, macOS, and Linux: In the activated virtual environment, navigate to the directory where the requirements.txt file is located. Then, enter the following command:

pip install -r requirements.txt

This command will install all the required packages specified in the requirements.txt file

6. Run Streamlit and FastAPI:

Please run this command at the same time

How to run FAST API:

uvicorn <replace-with-namefile>:app --reload

How to run Streamlit:

streamlit run <replace-with-namefile>

Open your port as mentioned in terminal prompt.

7. API Endpoint:

POST /predict Make a POST request to this endpoint to get pollution predictions.

Request Body

The request body should be a JSON object with the following fields:

• stasiun: str (DKI1 (Bunderan HI), DKI2 (Kelapa Gading), DKI3 (Jagakarsa), DKI4 (Lubang Buaya), DKI5 (Kebon Jeruk) Jakarta Barat)
• pm10: float (-1 - 800)
• pm25: float (-1 - 400)
• so2: float (-1 - 500)
• co: float (-1 - 100)
• o3: float (-1 - 160)
• no2: float (-1 - 100)

Example request body: { "stasiun": DKI1 (Bunderan HI), "pm10": 10.5, "pm25": 5.2, "so2": 0.8, "co": 1.2, "o3": 0.4, "no2": 0.6 }

Response

The API will respond with a JSON object containing the prediction result:

{ "status": 200, "input": [1.0, 0.0, 0.0, 0.0, 0.0, 10.5, 5.2, 0.8, 1.2, 0.4, 0.6], "message": "TIDAK SEHAT" }

The status field indicates the status of the prediction. A value of 200 indicates a successful prediction. The input field shows the input values provided in the request. The message field contains the predicted pollution level.

In case of an error or failure, the API will respond with a status code of 204 and an error message in the message field.

[OPTIONAL]

Docker Setup and Usage

This repository provides instructions on how to build and run a Docker image. Before proceeding, ensure that Docker is installed on your Ubuntu system.

Installation

Follow the official Docker documentation to install Docker on Ubuntu by following the step-by-step instructions provided at https://docs.docker.com/engine/install/ubuntu/.

Building the Docker Image

To build the Docker image, navigate to the directory containing the Dockerfile and run the following command:

docker build -t example_cicd .

Replace example_cicd with the desired tag name for your Docker image.

The build process will start, and Docker will execute the instructions specified in the Dockerfile to create the image.

Running Fastapi on Docker Image

docker run -p 8009:8000 example_cicd uvicorn src.api.main:app --host 0.0.0.0

This command will start a container and bind port 8009 of the host machine to port 8000 of the container. The uvicorn command is used to run the specified Python application within the container.

You can now access the running application by opening a web browser and navigating to http://localhost:8009.

Note: If you want to run the container in the background (detached mode), you can add the -d flag to the docker run command.

Illustration of architecture