ML Kit for On-Device AI Intermediate

import com.google.mlkit.vision.text.TextRecognition
import com.google.mlkit.vision.text.latin.TextRecognizerOptions
import com.google.mlkit.vision.common.InputImage

// Create a text recognizer
val recognizer = TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS)

// Process an image
val image = InputImage.fromBitmap(bitmap, 0)
recognizer.process(image)
    .addOnSuccessListener { visionText ->
        // Extract text from the result
        for (block in visionText.textBlocks) {
            for (line in block.lines) {
                Log.d("OCR", line.text)
            }
        }
    }
    .addOnFailureListener { e ->
        Log.e("OCR", "Text recognition failed", e)
    }

// For web, use the Vertex AI Gemini model for vision tasks
import { getVertexAI, getGenerativeModel } from 'firebase/vertexai';

const model = getGenerativeModel(vertexAI, { model: 'gemini-2.0-flash' });

// Label an image using Gemini's vision capabilities
const result = await model.generateContent([
  'List all objects you can identify in this image as a JSON array.',
  { inlineData: { mimeType: 'image/jpeg', data: base64Image } },
]);

const labels = JSON.parse(result.response.text());

import com.google.firebase.ml.modeldownloader.FirebaseModelDownloader
import com.google.firebase.ml.modeldownloader.CustomModelDownloadConditions

// Download a custom model from Firebase
val conditions = CustomModelDownloadConditions.Builder()
    .requireWifi()
    .build()

FirebaseModelDownloader.getInstance()
    .getModel("my-custom-model", DownloadType.LOCAL_MODEL, conditions)
    .addOnSuccessListener { model ->
        val modelFile = model.file
        // Use with TFLite Interpreter
    }