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This project connects three tasks: learning from Kaggle’s Digit Recognizer data, preparing a digit classifier for Android inference, and using Antigravity CLI from a supported computer to help with the coding work. The workflow does not run Antigravity CLI on the phone: official CLI documentation covers macOS, Linux, and Windows, and does not establish Android or Termux support.
What this project does—and what it does not
Kaggle’s Digit Recognizer is a classification exercise: train a model to map an image of a handwritten digit to a label from 0 through 9, then predict labels for the competition’s unlabeled test rows. An Android drawing app has a related but different job: take a new image drawn by a person, preprocess it into the model’s expected input, and display a prediction. A Kaggle submission file alone does not create that interactive app.
Antigravity CLI, invoked as agy, can assist with project files and terminal tasks while you work on a documented desktop or server platform. It is a coding assistant in this workflow, not the Android inference runtime.
The Tool Desk
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Kaggle describes each digit image as 28×28 grayscale pixels, represented in the CSV as 784 pixel values. The training CSV includes a label column; the test CSV contains image pixels without labels. The test set is intended for prediction, not for measuring how well a model generalizes, because its correct labels are not provided to the competitor. See Kaggle’s Digit Recognizer competition overview and competition data page for the dataset description and access details.
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Before sharing the CSVs, putting them in a public repository, or redistributing derived files, check Kaggle’s current competition rules and data-use conditions. Access and permitted use are governed by Kaggle’s terms, not by the fact that the files are downloadable.
Reshape each row into an image
The 784 pixel columns are a flattened 28×28 image. A training pipeline commonly separates the label from those columns, reshapes the pixels into image tensors, and converts the values to the numeric range expected by the model. Keep the same pixel ordering, scaling, and channel convention throughout training and Android inference; a mismatch can produce poor predictions even when the model loads successfully.
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Train and validate a classifier
Use the labeled training examples to fit a model that predicts one of ten digit classes. Before adapting it for a phone, hold out part of the labeled data for validation. That gives you a check on examples the model did not train on, whereas evaluating against Kaggle’s unlabeled test CSV is not possible locally without the labels.
- Record the preprocessing steps, model architecture, and class-label order alongside the saved model.
- Evaluate on a held-out validation split and report the split method and measured result if you actually run the experiment.
- Do not treat a Kaggle leaderboard metric definition as the result of this project. No accuracy score or device benchmark is established here.
Once the model is trained, the Kaggle test workflow and the mobile workflow diverge. For a competition submission, predict one label for each test row and format the output with the image identifiers and predicted labels required by Kaggle. For an app, the model must instead receive the appropriately preprocessed image created from the user’s drawing.
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Prepare the model for Android inference
TensorFlow Lite is one supported route for running a machine-learning model in an Android app. TensorFlow’s documentation notes that TensorFlow Lite and TensorFlow models use different formats; they are not interchangeable. Follow the applicable conversion and compatibility guidance for the model you trained, then verify that the converted model accepts the exact input tensor your app supplies. See TensorFlow Lite for Android.
Make input preprocessing match training
A drawing canvas produces an image, not a row of Kaggle CSV values. The app therefore needs a preprocessing step that matches the training pipeline: image dimensions, grayscale conversion, pixel scaling, orientation, and any centering or padding choices must be consistent. Confirm the model’s required input shape and data type rather than assuming that any 28×28 bitmap can be passed directly.
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Test the conversion separately
Before wiring the model into the drawing interface, run representative validation examples through both the original model and the converted model. Compare their outputs to catch conversion or preprocessing differences. This is a recommended verification step, not a reported test result for a specific implementation.
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Build the Android app using a reference example
TensorFlow’s official Digit Classification Demo Application is a useful reference for an Android digit classifier with a drawing interface. Its README says to use Android Studio and a physical Android device, with developer mode enabled; it lists SDK 23, equivalent to Android 6.0, as the minimum for that sample. The README’s publication date is not stated, so treat that minimum as a fact about the example documentation, not as a current recommendation for a new app or device.
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The sample demonstrates an end-to-end MNIST-trained classifier, but that does not establish that a separately trained Kaggle model has been converted, integrated, or tested. Adapt the model and preprocessing deliberately, then build and exercise the app on the device you intend to support.
Use Antigravity CLI from a supported computer
The official Antigravity CLI installation guide documents macOS, Linux, and Windows. It does not establish local execution on Android or Termux. Install and run agy on one of those documented platforms, with the project accessible there, and use the phone as the Android test target. See the Antigravity CLI documentation and Antigravity project workflow documentation.
A productive agent-assisted loop is to ask the CLI to inspect the project and explain its structure, propose a focused change, and then review the diff yourself. You can ask it to help trace the data-preprocessing path, adjust Android integration code, or interpret a build error. Treat generated changes as proposals: inspect them, run the relevant tests and build commands in your environment, and verify the resulting app behavior on a physical device.
What to report if you publish your results
If you complete the implementation, make the result reproducible and keep competition results distinct from app behavior. Report the model and preprocessing version, the validation method and measured score, and—in a device test—the phone model and Android version or API level, along with what you measured. If you have not run the build or tested the app, describe commands and expected workflow as instructions rather than claiming observed results.
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