Divya - Building Apps, Connecting People

Earlier, I explored how on-device AI works on iPhone using Core ML + SwiftUI.

Now, I wanted to bring the same idea to Android - using TensorFlow Lite.

Same concept: pick an image → run the model directly on the phone → get an instant prediction with a confidence score.

No APIs. No servers. Just pure local inference.

⚡ What Surprised Me

⚡
Speed - still incredibly fast. Inference averages ~40–60 ms on a mid-range Pixel.
🔒
Privacy - identical to iOS: everything happens locally, nothing touches the network.
🔧
Setup - TensorFlow Lite needs a few extra steps (model conversion + interpreter setup) but feels flexible once configured.
🔋
Battery trade-off - quantized models make a huge difference in efficiency.

🔧 How It Works

The architecture mirrors the iOS version, just with Android building blocks:

TensorFlow Lite model: mobilenet_v1_224.tflite (≈ 5 MB)
Jetpack Compose for the UI
ImageClassifier API from TensorFlow Lite Task Library
Output → label + confidence score

Everything runs completely offline.

💻 Core Code Snippet

val classifier = ImageClassifier.createFromFileAndOptions(
    context,
    "mobilenet_v1_224.tflite",
    ImageClassifier.ImageClassifierOptions.builder()
        .setMaxResults(1)
        .build()
)

val bitmap = ... // image from gallery or camera
val result = classifier.classify(TensorImage.fromBitmap(bitmap))
val top = result.firstOrNull()?.categories?.firstOrNull()

textResult.text = "${top?.label} - ${(top?.score ?: 0f) * 100}%"

✅ No API keys
✅ No network calls
✅ Fully on-device inference

☁️ On-Device vs Cloud (Android Edition)

Cloud AI	On-Device AI
Requires internet	Works offline
~600 ms latency	~50 ms latency
Privacy concerns	100% local
Unlimited compute	Battery / RAM constrained

🎥 Demo in Action

Android app classifying images with TensorFlow Lite - showing instant predictions with confidence scores

Tap “Choose Image” → select any photo → instant label appears with a confidence score

Seeing it run in real time on an Android phone feels the same way the Core ML demo did — shockingly instant.

🔋 Takeaways

Running AI locally isn't just possible on Android, it's becoming the expected direction.

With TensorFlow Lite, Gemini Nano, and new on-device APIs, Android is closing the gap between mobile UX and AI intelligence.

This experiment reaffirmed something I felt on iOS:

AI doesn't need to live in massive cloud servers anymore.

It can live right inside your phone - fast, private, and personal.

🧩 TL;DR

✅ Built the same image-classification demo on Android
✅ Used TensorFlow Lite + Jetpack Compose
✅ Runs entirely offline (~50 ms inference)
✅ Maintains privacy and performance
✅ Mirrors Core ML workflow - different tools, same magic

🔗 View the Code

The complete working code for the iOS and Android demo is available in my GitHub repository:

View on GitHub →

Repository: Mobile-AI-Experiments

🧠 On-Device AI, Part 2 - Running Image Classification on Android with TensorFlow Lite

On-Device AI Series