This web app is developed to visualize the forecasted precipitation and temperature of various meteorological stations using ML models trained on the station based different weather parameters.
This project was bootstrapped with Create React App.
In the project directory, you can run:
Runs the app in the development mode.
Open http://localhost:3000 to view it in your browser.
The page will reload when you make changes.
You may also see any lint errors in the console.
For backend, you need to create a virtual environment. Open a Git Bash or Bash terminal and navigate to the flask-server directory:
Create a virtual environment named "venv" using the following command:
Activate the virtual environment using the following command:
Install flask:
Virtual environment for your backend server will be created and to run the server use following command: If you are inside the flask-server directory:
If you are in react directory:
Open http://localhost:5000 to view it in your browser.
You also need to install multiple python libraries in order to run the ML model inside the server. Install tensorflow, keras, seaborn, pandas, numpy, scikit-learn using pip install <libraries_names>. The virtual environment along with libraries were not uploaded because of large file sizes.
MapboxGL was used for an interactive map in the web page. Since the account is premium, we can't provide an access token. If you have an account over there, you can enter your own access token inside a config.js file present inside config folder on src.