Gemini Interface Clone Large Model Web Application: A Teaching Project from Design to Implementation

This is a project for Swinburne University of Technology's SWE40006 course. It fully clones Google Gemini's web interface, integrates multi-vendor large models (e.g., OpenAI GPT, Anthropic Claude) via OpenRouter, demonstrates the full-stack development process of modern AI applications, and has both teaching practice and technical exploration value.

Against the backdrop of generative AI's popularity, building fully functional and user-friendly large model dialogue applications has become an important topic in software engineering education. The SWE40006 course project at Swinburne University of Technology requires students to build a Gemini-like web application from scratch to deeply understand the full-stack development process of AI applications.

The project uses a monolithic architecture, balancing the advantages of simple deployment and easy debugging in teaching scenarios, and guiding students to think about scenario adaptation for architecture choices. The tech stack uses Python ecosystem for the backend (mainstream in AI development), and the frontend references Gemini's interface design to pursue a simple and intuitive experience.

It integrates dozens of models (including OpenAI GPT, Anthropic Claude, Google Gemini, etc.) via OpenRouter's unified LLM API gateway, bringing three core advantages: 1. Flexible model switching, making it easy to compare the performance of different models; 2. Cost optimization, allowing selection of the most cost-effective model based on tasks; 3. Future expansion readiness, enabling new models to be integrated without modifying code.

Cloning Gemini's benchmark interface design: streaming dialogue display (message bubbles + Markdown rendering), immersive bottom input area (multi-line text + shortcuts), sidebar history management (search/archiving), and responsive layout adapting to multiple devices.

Although it is a teaching project, it has good engineering practices: Git version control and collaboration, separation of environment configurations (using environment variables to manage sensitive information), error handling and degradation (friendly prompts to avoid crashes), and API rate limiting and cost control (to prevent uncontrolled expenses).

Insights for AI application development learners: 1. No need to train models from scratch; quickly integrate advanced models via aggregation platforms like OpenRouter; 2. Excellent interface design is key to AI product success; cloning benchmark designs helps understand product philosophy; 3. Monolithic architecture still has value in specific scenarios (small teams, rapid iteration), so there's no need to blindly pursue microservices.

This course project at Swinburne University of Technology integrates cutting-edge technology into education. Students master full-stack skills by cloning industry-leading AI products, deeply understand the product logic and engineering practices of AI applications, and it is an open-source project worth studying and referencing for AI application development beginners.