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Gemini Analyzer - Advanced Project Intelligence Platform

Enterprise-grade project analysis platform with dual-mode AI intelligence and privacy-first architecture

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🚀 Overview

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Key Features

• 🔍 Comprehensive Analysis: Multi-language code analysis, dependency mapping, and project statistics
• 🤖 Dual-Mode AI: Cloud-powered insights with Google Gemini 2.5 or privacy-first local processing
• 🌐 REST API: Production-ready FastAPI backend with comprehensive documentation
• 🖥️ GUI Application: Standalone Tkinter interface for desktop users
• 🔒 Privacy-First: Complete local processing option for sensitive codebases
• 📊 Rich Insights: Code metrics, dependency analysis, and AI-generated summaries
• 🏢 Enterprise Ready: Multi-tenant architecture, audit trails, and compliance features

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Why Gemini Analyzer?

• Automated code analysis across 20+ programming languages
• AI-powered insights that understand project context and architecture
• Privacy-first design that keeps sensitive code local
• Enterprise-grade API for CI/CD integration

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Architecture Modes

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1. Standalone GUI Application

• Cross-platform desktop tool
• Complete local processing
• Instant analysis results
• No external dependencies required

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2. REST API Service

• Production-ready FastAPI backend
• Scalable container deployment
• CI/CD pipeline integration
• Multi-tenant support

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🛠️ Key Technologies

• Backend: FastAPI for high-performance, asynchronous API services.
• AI Integration: Google Generative AI SDK (google-generativeai), the official and current library for the Gemini family of models.
• Desktop GUI: Tkinter, Python’s standard cross-platform GUI toolkit.
• Data Validation: Pydantic for robust data validation and settings management in the API.
• Containerization: Docker for creating portable, production-ready containers for the backend service.

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🚀 Getting Started

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Prerequisites

• Python 3.8+
• Docker (for running the backend service)
• An optional GOOGLE_API_KEY environment variable for cloud-based AI features.

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Local Development & Running the GUI

1. Clone the repository:
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   git clone https://github.com/your-repo/gemini-magic.git
   cd gemini-magic
   

2. Create a virtual environment and install dependencies:
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   python -m venv venv
   source venv/bin/activate
   # Install base GUI dependencies + AI dependencies
   pip install -e ".[ai,dev]"
   

3. Run the GUI application:
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   python -m gemini_analyzer.gui.main
   

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Running the Backend Service with Docker

1. Build the Docker image:
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   docker build -t gemini-analyzer-service .
   

2. Run the container:
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   docker run -p 8000:8000 -e GOOGLE_API_KEY="your_api_key" gemini-analyzer-service
   

   The API will be available at http://localhost:8000. Interactive documentation can be found at http://localhost:8000/docs.

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🏗️ Project Structure

Gemini_Magic/
├── .github/                # CI/CD workflows
├── docs/                   # Project documentation
├── scripts/                # Build/deploy helper scripts
├── src/
│   └── gemini_analyzer/    # Main application package
│       ├── api/            # FastAPI backend service
│       ├── core/           # Core analysis logic (shared by GUI and API)
│       ├── gui/            # Standalone Tkinter GUI application
│       └── config.py       # Centralized configuration
├── tests/                  # Unit and integration tests
├── .dockerignore           # Files to ignore in Docker builds
├── .gitignore              # Files to ignore in Git
├── docker-compose.yml      # For local multi-container setups
├── Dockerfile              # Defines the container for the backend service
├── pyproject.toml          # Project definition and dependencies
└── README.md               # This file

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🤖 For AI Agents: How to Contribute

• If you are the GUI Agent: Your primary focus is the src/gemini_analyzer/gui/ directory. Start by reading its README.md to understand the component structure. Your goal is to build a responsive and intuitive user interface using the components and logic available.
• If you are the Core Logic Agent: Your domain is src/gemini_analyzer/core/. Read its README.md. You will implement the heavy lifting of project analysis, ensuring the code is efficient, accurate, and can be used by both the GUI and the API.
• If you are the API Agent: You will work within src/gemini_analyzer/api/. Your task is to create robust FastAPI endpoints that expose the functionality from the core modules, including proper data validation and error handling.

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License