Building Mobile AI Apps with Google Nano Banana Pro

Introduction

This Nano Banana Pro tutorial walks you through building production-ready mobile AI applications from scratch. Building intelligent mobile applications has traditionally required either compromising on privacy through cloud APIs or sacrificing capability with limited on-device models. Google Nano Banana Pro changes this equation entirely, offering complete offline functionality and privacy preservation for mobile AI development.

Whether you're creating a healthcare app, financial tool, productivity application, or accessibility feature, this guide provides the knowledge and code examples needed to successfully integrate Nano Banana Pro. We'll cover setup, implementation on both Android and iOS, optimization techniques, and deployment strategies that work in real-world applications.

Development Environment Setup

Prerequisites

Before starting, ensure your development environment meets these requirements:

For Android Development:

• Android Studio Flamingo or later
• Android SDK 24 (API level 24, Android 7.0) or higher
• Minimum 8GB RAM (16GB recommended)
• 50GB free disk space
• Kotlin 1.8+ or Java 11+

For iOS Development:

• Xcode 15.0 or later
• iOS 14.0 or higher deployment target
• Swift 5.7+
• 10GB free disk space
• M1/M2 Mac recommended (Intel Macs supported with slower compilation)

For Both:

• Google Cloud project with appropriate APIs enabled
• Device or emulator for testing
• Basic understanding of mobile development
• Familiarity with asynchronous programming patterns

Initial Configuration

Create a Google Cloud Project:

• Visit the Google Cloud Console
• Create a new project named "MobileAIApp"
• Enable the following APIs:

- Google AI Platform

- Mobile Edge AI

- Firebase (optional, for analytics)

Download the Model:

The Nano Banana Pro model is approximately 2GB. Plan storage accordingly:

Android SDK Integration

Adding Dependencies

Start with your build.gradle (Project level):

Then in your app's build.gradle:

Model Loading and Initialization

Create a ModelManager class to handle model lifecycle:

Building Your First Feature - AI Writing Assistant

Create an activity that uses the model:

Handling Device Constraints

iOS SDK Integration

CocoaPods Setup

Create a Podfile in your iOS project:

Install pods:

Model Integration in Swift

Building an iOS AI Assistant View

Advanced Features: Text Generation

Implementing Custom Prompts

{language}

{code}

Offline Capabilities and Storage

Implementing Persistent Storage

Performance Optimization

Battery and Memory Management

Memory-Efficient Batch Processing

Real-World App Examples

Example 1: Healthcare Symptom Checker

Example 2: Personal Finance Assistant

Deployment Best Practices

Version Management

Testing Strategies

Conclusion

Building production-ready mobile AI applications with Google Nano Banana Pro involves careful attention to initialization, streaming, optimization, and testing. The frameworks and code examples provided throughout this tutorial establish patterns that scale from simple prototypes to complex, feature-rich applications.

The combination of offline functionality, privacy preservation, and sophisticated AI reasoning creates opportunities for entirely new categories of mobile applications. Your users benefit from responsive, intelligent features that respect their privacy and work seamlessly, even without internet connectivity.

Ready to build your first mobile AI app? Start with the examples provided, adapt them to your use case, and leverage the comprehensive SDKs Google provides for both Android and iOS. The future of mobile development is intelligent, private, and on-device.

Related Resources

For deeper exploration, check out these complementary guides:

• Google Nano Banana Pro: Complete Overview - Understand the model's capabilities and use cases
• Gemini 3.1 Pro: Complete Feature Guide - Learn about cloud-based AI alternatives
• State of AI Tools 2026 - Overview of the current AI landscape and emerging technologies