skills/firebase-ai-logic-basics/references/usage_patterns_android.md

# Firebase AI Logic on Android (Kotlin)

First, ensure you have initialized the Firebase App (see `firebase-basics`
skill). Then, initialize the AI Logic service as below

### 0. Enable Firebase AI Logic via CLI

Before adding dependencies in your app, make sure you enable the AI Logic
service in your Firebase Project using the Firebase CLI:

```bash
npx -y firebase-tools@latest init
# When prompted, select 'AI logic' to enable the Gemini API in your project.
```

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### 1. Add Dependencies

In your module-level `build.gradle.kts` (usually `app/build.gradle.kts`), add
the dependency for Firebase AI:

```kotlin
dependencies {
    // [AGENT] Fetch the latest available BoM version from https://firebase.google.com/support/release-notes/android before adding this
    implementation(platform("com.google.firebase:firebase-bom:<latest_bom_version>"))

    // Add the dependency for the Firebase AI library
    implementation("com.google.firebase:firebase-ai")
}
```

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### 2. Initialize and Generate Content

In your Activity or Fragment, initialize the `FirebaseAI` service and generate
content using a Gemini model:

```kotlin
import com.google.firebase.ai.FirebaseAI
import com.google.firebase.ai.ktx.ai
import com.google.firebase.ktx.Firebase

class MainActivity : AppCompatActivity() {

    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        setContentView(R.layout.activity_main)

        // Initialize Firebase AI
        val ai = Firebase.ai

        // [AGENT] Replace "<latest_supported_model>" with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
        val model = ai.generativeModel("<latest_supported_model>")

        // Generate content
        lifecycleScope.launch {
            try {
                val response = model.generateContent("Write a story about a magic backpack.")
                Log.d(TAG, "Response: ${response.text}")
            } catch (e: Exception) {
                Log.e(TAG, "Error generating content", e)
            }
        }
    }
}
```

#### Jetpack Compose (Modern)

Initialize inside a `ComponentActivity` and use `setContent`:

```kotlin
import android.os.Bundle
import androidx.activity.ComponentActivity
import androidx.activity.compose.setContent
import androidx.compose.material3.MaterialTheme
import androidx.compose.material3.Text
import androidx.lifecycle.lifecycleScope
import com.google.firebase.Firebase
import com.google.firebase.ai.ai
import kotlinx.coroutines.launch

class MainActivity : ComponentActivity() {
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)
        val ai = Firebase.ai
        // [AGENT] Replace with the latest model from https://firebase.google.com/docs/ai-logic/models.md.txt
        val model = ai.generativeModel("<latest_supported_model>")
        
        lifecycleScope.launch {
            val response = model.generateContent("Hello Gemini!")
            setContent {
                MaterialTheme {
                    Text("AI Response: ${response.text}")
                }
            }
        }
    }
}
```

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### 3. Multimodal Input (Text and Images)

Pass bitmap data along with text prompts:

```kotlin
val image1: Bitmap = ... // Load your bitmap
val image2: Bitmap = ...

val response = model.generateContent(
    content("Analyze these images for me") {
        image(image1)
        image(image2)
        text("Compare these two items.")
    }
)
Log.d(TAG, response.text)
```

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### 4. Chat Session (Multi-turn)

Maintain chat history automatically:

```kotlin
val chat = model.startChat(
    history = listOf(
        content("user") { text("Hello, I am a software engineer.") },
        content("model") { text("Hello! How can I help you today?") }
    )
)

lifecycleScope.launch {
    val response = chat.sendMessage("What should I learn next?")
    Log.d(TAG, response.text)
}
```

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### 5. Streaming Responses

For faster display, stream the response:

```kotlin
lifecycleScope.launch {
    model.generateContentStream("Tell me a long story.")
        .collect { chunk ->
            print(chunk.text) // Update UI incrementally
        }
}
```