Android

Android#

Deploy to Android Studio#

MLange supports both for Kotlin and Java implementations.

Deploy your own On-device AI Android application easily.

There are only two steps are needed.

  1. Add module dependency in your library

  2. Initialize ZeticMLangeModel with your Model Key / Personal Key and run

Prerequisite#

  • Model Key

    • Prepare Model Key in with SaaS or CLI method

  • Personal Key

    • Prepare Personal Key in with SaaS method

Step-by-step Guideline#

1. Add ZeitcMLange dependency#

  • You can choose Groovy build or Kotlin DSL build

    • build.gradle (Groovy)

      android {
    ...
    packagingOptions {
        jniLibs {
            useLegacyPackaging true
        }
    }
}

dependencies {
    implementation 'com.zeticai.mlange:mlange:+'
}

    • build.gradle.kts (Kotlin DSL)

      android {
    ...
    packaging {
        jniLibs {
            useLegacyPackaging = true
        }
    }
}

dependencies {
    implementation("com.zeticai.mlange:mlange:+")
}

2. Initialize and run ZeticMLangeModel model with Model Key#

  • MLange supports both Kotlin and Java implementation

    1. Zetic MLange model running (Java)

    // 1. Zetic MLange model running

// (1) Load Zetic MLange model
ZeticMLangeModel model = new ZeticMLangeModel(CONTEXT, $PERSONAL_KEY, $MODEL_NAME);

// (2) Prepare model inputs
Tensor[] inputs = // Prepare your inputs;

// (3) Run and get output buffers of the model
Tensor[] outputs = model.run(inputs);

    1. Zetic MLange model running (Kotlin)

    // 1. Zetic MLange Model Running

// (1) Load Zetic MLange model
val model = ZeticMLangeModel(CONTEXT, $PERSONAL_KEY, $MODEL_NAME)

// (2) Prepare model inputs
val inputs: Array<Tensor> = // Prepare your inputs

// (3) Run and get output tensors of the model
val outputs = model.run(inputs)

ZETIC.MLange Android Sample App#

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