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🧠 Full Guide: How to Run an ONNX Model on Android (Java)

📌 What is this?

This guide walks you through creating and deploying a fully offline ONNX-based text classification app on Android using Java.

✅ 1. Project Setup

✅ 2. Prepare Assets

app/src/main/assets/

{
  "<OOV>": 1,
  "hello": 2,
  "world": 3
}

{
  "0": "Not_Hate",
  "1": "Hate"
}

✅ 3. Add ONNX Runtime

dependencies {
    implementation 'ai.onnxruntime:onnxruntime-android:1.16.3'
}

✅ 4. Java Implementation

Replace the contents of MainActivity.java with this code:

package com.example.hateclassifier;

import android.os.Bundle;
import android.util.Log;
import android.widget.Button;
import android.widget.EditText;
import android.widget.TextView;

import androidx.appcompat.app.AppCompatActivity;

import ai.onnxruntime.*;

import org.json.JSONObject;

import java.io.*;
import java.util.*;

public class MainActivity extends AppCompatActivity {
    private OrtEnvironment env;
    private OrtSession session;
    private Map<Integer, String> labelMap;
    private Map<String, Integer> vocab;

    private static final int MAX_LEN = 30;

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        EditText inputText = findViewById(R.id.inputText);
        Button sendButton = findViewById(R.id.sendButton);
        TextView chatOutput = findViewById(R.id.chatOutput);

        try {
            LabelVocabLoader loader = new LabelVocabLoader("labelMap.json", "vocab.json");
            labelMap = loader.getLabelMap();
            vocab = loader.getVocab();

            Log.d("ONNX", "Loading model...");
            session = loadModel("model.onnx");
            env = OrtEnvironment.getEnvironment();
        } catch (Exception e) {
            e.printStackTrace();
            chatOutput.setText("❌ Error loading model or data.");
            return;
        }

        sendButton.setOnClickListener(v -> {
            String text = inputText.getText().toString().trim();
            if (text.isEmpty()) return;

            new Thread(() -> {
                try {
                    Tokenizer tokenizer = new Tokenizer(vocab);
                    int[] tokenizedInput = tokenizer.tokenize(text);
                    int[] paddedInput = new int[MAX_LEN];
                    for (int i = 0; i < MAX_LEN; i++) {
                        if (i < tokenizedInput.length) {
                            paddedInput[i] = tokenizedInput[i];
                        } else {
                            paddedInput[i] = 0;
                        }
                    }

                    int[][] inputData = new int[1][MAX_LEN];
                    inputData[0] = paddedInput;

                    String inputName = session.getInputNames().iterator().next();
                    OnnxTensor inputTensor = OnnxTensor.createTensor(env, inputData);

                    OrtSession.Result result = session.run(Collections.singletonMap(inputName, inputTensor));
                    float[][] outputArray = (float[][]) result.get(0).getValue();

                    StringBuilder builder = new StringBuilder();
                    for (int i = 0; i < outputArray[0].length; i++) {
                        builder.append("Class: ")
                               .append(labelMap.get(i))
                               .append(", Probability: ")
                               .append(outputArray[0][i])
                               .append("\n");
                    }

                    runOnUiThread(() -> {
                        chatOutput.append("👤: " + text + "\n🤖:\n" + builder + "\n");
                        inputText.setText("");
                    });

                } catch (Exception ex) {
                    ex.printStackTrace();
                    runOnUiThread(() -> chatOutput.append("❌ Error during inference\n"));
                }
            }).start();
        });
    }

    private OrtSession loadModel(String filename) throws IOException, OrtException {
        InputStream is = getAssets().open(filename);
        byte[] modelBytes = new byte[is.available()];
        is.read(modelBytes);
        is.close();
        return OrtEnvironment.getEnvironment().createSession(modelBytes, new OrtSession.SessionOptions());
    }

    static class Tokenizer {
        private final Map<String, Integer> vocab;

        public Tokenizer(Map<String, Integer> vocab) {
            this.vocab = vocab;
        }

        public int[] tokenize(String text) {
            String[] words = text.toLowerCase().split("\\s+");
            int[] tokenized = new int[words.length];
            for (int i = 0; i < words.length; i++) {
                tokenized[i] = vocab.getOrDefault(words[i], vocab.get("<OOV>"));
            }
            return tokenized;
        }
    }

    class LabelVocabLoader {
        private final Map<Integer, String> labelMap;
        private final Map<String, Integer> vocab;

        public LabelVocabLoader(String labelMapFile, String vocabFile) throws Exception {
            labelMap = new HashMap<>();
            vocab = new HashMap<>();

            String labelJson = readJsonFromAssets(labelMapFile);
            JSONObject labelObj = new JSONObject(labelJson);
            for (Iterator<String> it = labelObj.keys(); it.hasNext(); ) {
                String key = it.next();
                labelMap.put(Integer.parseInt(key), labelObj.getString(key));
            }

            String vocabJson = readJsonFromAssets(vocabFile);
            JSONObject vocabObj = new JSONObject(vocabJson);
            for (Iterator<String> it = vocabObj.keys(); it.hasNext(); ) {
                String key = it.next();
                vocab.put(key, vocabObj.getInt(key));
            }
        }

        public Map<Integer, String> getLabelMap() {
            return labelMap;
        }

        public Map<String, Integer> getVocab() {
            return vocab;
        }

        private String readJsonFromAssets(String filename) throws IOException {
            InputStream is = getAssets().open(filename);
            BufferedReader reader = new BufferedReader(new InputStreamReader(is));
            StringBuilder sb = new StringBuilder();
            String line;
            while ((line = reader.readLine()) != null) {
                sb.append(line);
            }
            reader.close();
            return sb.toString();
        }
    }
}

✅ 5. Create the UI

<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
    android:layout_width="match_parent"
    android:layout_height="match_parent"
    android:orientation="vertical"
    android:padding="16dp">

    <EditText
        android:id="@+id/inputText"
        android:layout_width="match_parent"
        android:layout_height="wrap_content"
        android:hint="Enter a sentence..." />

    <Button
        android:id="@+id/sendButton"
        android:layout_width="match_parent"
        android:layout_height="wrap_content"
        android:text="Run Model" />

    <TextView
        android:id="@+id/chatOutput"
        android:layout_width="match_parent"
        android:layout_height="wrap_content"
        android:textAppearance="?android:textAppearanceMedium"
        android:paddingTop="16dp" />
</LinearLayout>

✅ 6. Build the APK

app/build/outputs/apk/debug/app-debug.apk

✅ 7. Install on Your Android Device

✅ 8. Android Demo

🤖

Android Demo

🎬 ONNX inference running on Android device

▶️ Watch Full Video on YouTube

✅ 9. Features