AI in Mobile Apps is No Longer Optional
Users expect smart, predictive, personalized experiences. Here's how I've added AI capabilities to Flutter apps without a PhD in machine learning.
On-Device vs Cloud AI
On-Device (TensorFlow Lite) - Fast inference, no network latency - Works offline - Limited model size and complexity - Good for: image classification, pose detection, text recognition
Cloud APIs (OpenAI, Google AI) - Powerful models, unlimited complexity - Requires network connection - Per-request pricing - Good for: text generation, complex reasoning, image generation
Speech-to-Text
For my AI Voice Assistant app, I used the speech_to_text package for real-time transcription. Key learnings: - Request microphone permissions early and gracefully - Handle partial vs final transcriptions differently - Provide visual feedback during listening - Support multiple languages from day one
Image Generation
Integrating AI image generation requires a backend proxy. Never expose API keys in your Flutter app. I use a FastAPI backend that: 1. Receives the prompt from the Flutter app 2. Calls the AI API server-side 3. Returns the generated image URL
Predictive Models
For Pneumonia Prediction, I trained a CNN model in Python, converted it to TFLite, and loaded it directly in the Flutter app. The key challenge was preprocessing — the image must be resized and normalized exactly like the training data.
Key Principles
- Start with cloud APIs for prototyping, optimize with on-device models later
- Always have fallback behavior when AI features are unavailable
- Be transparent with users about AI-generated content
- Test with diverse inputs — AI models can have surprising blind spots