Dog breed Android App
Overview
I embraced modern development practices by leveraging AI and troubleshooting skills to create a dog breed classification application with no prior Android development experience. This project demonstrates my ability to adapt to new technologies and integrate complex systems.
The application successfully identifies dog breeds from uploaded images with high accuracy, showcasing my technical versatility and ability to bring complex ideas to life.
Technologies used: Python, TensorFlow Lite, FastAPI, Flask, Render.com deployment, RESTful API development.
Project Details
Challenge
The project aimed to develop a mobile solution for real-time dog breed identification, providing accurate classification capabilities directly on a smartphone device without requiring specialized knowledge of the breeds.
Methodology
I leveraged generative AI tools to develop the Android application architecture, integrating camera functionality and gallery access. This was combined with a custom-trained TensorFlow Lite model deployed via a RESTful API infrastructure on Render.com. The development process utilized an iterative approach to refine both the model accuracy and application performance.
Implementation
Despite having no prior Android development experience, I successfully implemented a fully functional application within a weekend timeframe. This required extensive troubleshooting of integration issues between the Android frontend and the model backend, as well as optimization of the API response handling to ensure efficient breed classification.
Key Insights
This project demonstrated the significant productivity enhancements possible through strategic use of generative AI tools combined with focused problem-solving skills. I gained valuable experience in Android Studio development workflows, RESTful API integration patterns, and efficient Java-based HTTP request handling for machine learning applications.
Future Development Roadmap
Planned enhancements include a comprehensive UI redesign to improve user experience, expanding the training dataset to include a broader range of dog breeds, and implementing lighting normalization algorithms to improve classification accuracy in variable environmental conditions.