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Is it possible to use matplotlib and seaborn output plots in a GUI app (e.g. Flutter)?

I am building a tool in Python, which loads data, then does all sorts of preprocessing depending on the input parameters and in the end it generates all sorts of graphs, scatterplots, histograms, and in some cases ML algorithms.
I want to take this code and expand upon it by making it into a user friendly app for future usage: e.g. load custom data, change the input parameters, and allow friendly interaction within the plots themselves (e.g. allow user to change things in the plot) etc.

Will I be able to use the matplotlib and searborn outputs and put them directly into the flutter GUI, and then also allow all sorts of interactions with the plots that might be available in Python, but not in Flutter? (e.g. will I be able to run a matplotlib instance inside flutter so I get some of the features of that, e.g. in some cases I allow the user to add points to the plot, so I can’t just output an image in the Flutter app for example)

Is it possible to do this with Flutter, or perhaps a different tool? It doesn’t have to be Flutter. I want the nice backend implemented in Python to still be available (e.g. uses pandas, matplotlib, etc) as I expand upon these tools.

A related unanswered question: How to show seaborn plots in an Flutter App.

I might be missing some core component of understanding, but I would value being pointed in the right direction.

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2

Answers


  1. 0

    Yes, it’s possible to use matplotlib and seaborn plots in a GUI application, such as one built with Flutter. The process involves generating the plots within your Python environment, saving them as images (e.g., PNG or JPEG files), and then displaying these images within the GUI application. Here’s a general step-by-step approach to achieve this:

    Generate Plots: Use matplotlib and seaborn to create plots in Python. You can customize these plots with various settings like labels, titles, and styles.

    Save Plots as Images: Once your plot is ready, you can save it as an image file using the .savefig() method in matplotlib. This allows you to specify the filename and format of the image (e.g., PNG, JPEG).

    Transfer Images to Your GUI Application: You need to transfer these image files from your Python environment to the environment where your GUI application is running. This can be done through various methods:

    Local Storage: If both the Python script and the Flutter app are running on the same device, you can save the images to a local directory that your Flutter app can access.
    Network Request: If they are running on different devices, you might consider setting up a simple API (using frameworks like Flask or Django) that your Flutter app can call to fetch images.
    Static Hosting: Alternatively, you can upload the images to a static file server or a cloud storage solution (like AWS S3, Google Cloud Storage), and then load them in the Flutter app via URL.
    Display Images in Flutter: In your Flutter application, you can use an Image widget to display the image. If you’re fetching the image from the network or an API, you might want to use widgets like FutureBuilder or Image.network to handle the asynchronous loading.

    Handle Updates: If your data updates and you need to refresh the plots, ensure your backend script can regenerate the images and your Flutter app can fetch the new versions, either by polling, using websockets for a push mechanism, or triggering a refresh based on user interaction.

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  2. 0

    Creating a user-friendly app that integrates with your existing Python codebase for data processing and visualization while also allowing interactive features like those available in matplotlib within a Flutter GUI can be achieved, but it requires a multi-step approach and potentially utilizing different technologies for different parts of the application. Here’s a high-level overview of how you might approach this:

    1. Backend in Python: Keep your existing Python codebase for data processing, preprocessing, and generating visualizations using libraries like pandas, matplotlib, and seaborn. This will serve as the backend for your application.

    2. Expose APIs: Expose endpoints or APIs from your Python backend using a framework like Flask or FastAPI. These APIs will allow your Flutter frontend to communicate with the Python backend, sending data and receiving processed results.

    3. Flutter Frontend: Build a Flutter application to serve as the frontend of your tool. You can use Flutter’s rich set of widgets to create a user-friendly interface for loading data, adjusting parameters, and displaying visualizations.

    4. Integration of Visualizations: There isn’t a direct way to embed matplotlib or seaborn plots into a Flutter app since they are Python libraries. However, you can convert the plots generated by these libraries into images (PNG or SVG) on the Python backend and send them to the Flutter frontend as byte data. In Flutter, you can then display these images using the Image widget.

    5. Interactive Features: For interactive features like allowing users to add points to a plot, you’ll need to implement custom logic in your Flutter app. Instead of relying on matplotlib’s interactivity, you’ll have to handle user input events in Flutter and update the plot accordingly. This may involve redrawing the plot with new data based on user interactions.

    6. Alternative Visualization Libraries: Consider exploring alternative visualization libraries for Flutter that offer more interactivity out of the box, such as FlChart or charts_flutter. While these may not provide all the features of matplotlib or seaborn, they may offer better integration with Flutter’s UI and interaction capabilities.

    7. Research and Experimentation: The integration between Python and Flutter is not straightforward, so be prepared for some trial and error as you explore different approaches and technologies. It’s also worth checking if there are any existing packages or libraries that facilitate this integration, although as of my last update, such solutions might be limited.

    By following these steps and carefully integrating the backend Python code with the Flutter frontend, you should be able to create a user-friendly app that leverages your existing data processing capabilities while providing a visually appealing and interactive experience for the users.

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