Generative AI Python Service

Serverless Python API generating intelligent code documentation using Gemini AI

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📖 What is Generative AI Python Service?

A high-performance RESTful API service built with FastAPI that leverages Google's Gemini AI to generate intelligent, detailed code documentation and analysis. This service demonstrates modern Python development practices with async/await patterns, type safety, and production-ready API design.

The service processes code snippets, analyzes their structure and intent, and generates comprehensive narratives explaining functionality, design patterns, and potential improvements - transforming raw code into human-readable documentation instantly.

Key Features:

• 🤖 AI-Powered Analysis - Leverages Gemini 1.5 Pro for advanced code understanding
• ⚡ Async Architecture - Non-blocking I/O for high throughput
• 🔒 Type-Safe - Full Pydantic validation and type hints throughout
• 📊 Auto-Generated Docs - Interactive API documentation via Swagger UI
• 🐳 Container-Ready - Docker support for consistent deployments
• 🧪 Test Coverage - Comprehensive pytest suite with mocking

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🏛️ Architecture

graph LR
    Client[Client Application] -->|POST /api/v1/analyze| API[FastAPI Server]
    API -->|Validate Request| Pydantic[Pydantic Models]
    Pydantic -->|Process| Service[GeminiService]
    Service -->|API Call| Gemini[Gemini AI API]
    Gemini -->|Generated Analysis| Service
    Service -->|Format Response| API
    API -->|JSON Response| Client
    
    style API fill:#009688,stroke:#00796B,stroke-width:2px,color:#fff
    style Service fill:#FF6F00,stroke:#E65100,stroke-width:2px,color:#fff
    style Gemini fill:#4285F4,stroke:#1967D2,stroke-width:2px,color:#fff

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🛠️ Tech Stack

Backend: Python 3.11+, FastAPI 0.100+
AI Platform: Google Gemini AI (gemini-1.5-pro)
Validation: Pydantic V2
Testing: pytest, pytest-asyncio
Container: Docker

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📁 Project Structure

generative-ai-python-service/
├── app/
│   ├── main.py                  # FastAPI application entry
│   ├── models/
│   │   └── schemas.py           # Pydantic models
│   ├── services/
│   │   ├── gemini_service.py    # Gemini AI integration
│   │   └── code_analyzer.py     # Code analysis logic
│   └── routers/
│       └── analysis.py          # API route handlers
├── tests/
│   ├── test_api.py              # API endpoint tests
│   └── test_gemini_service.py   # Service layer tests
├── requirements.txt             # Python dependencies
├── Dockerfile                   # Container configuration
├── .env.example                 # Environment template
└── README.md

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🚀 Quick Start

Prerequisites

• Python 3.11 or higher
• Google AI Studio API Key
• pip or poetry for dependency management

Basic Usage

# Clone the repository
git clone https://github.com/yourusername/generative-ai-python-service.git
cd generative-ai-python-service

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env and add your GEMINI_API_KEY

# Run the service
python -m app.main

# Service will be available at http://localhost:8000

Docker Usage

# Build image
docker build -t generative-ai-service .

# Run container
docker run -p 8000:8000 --env-file .env generative-ai-service

Need detailed setup? → See LEARNING_GUIDE.md

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📚 API Documentation

Once running, access interactive documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Example Request

curl -X POST "http://localhost:8000/api/v1/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "code": "def fibonacci(n):\n    if n <= 1:\n        return n\n    return fibonacci(n-1) + fibonacci(n-2)",
    "language": "python"
  }'

Example Response

{
  "analysis": "This function implements the Fibonacci sequence using recursive approach...",
  "complexity": "O(2^n) time complexity - exponential growth",
  "suggestions": [
    "Consider memoization to optimize recursive calls",
    "Alternative iterative approach would be O(n)"
  ],
  "patterns_detected": ["recursion", "mathematical-algorithm"]
}

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🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=app --cov-report=html

# Run specific test file
pytest tests/test_api.py -v

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🎯 Why This Project?

This project demonstrates:

• Modern Python expertise (async/await, type hints, FastAPI)
• AI/ML Integration with production-grade API services
• API Design best practices (validation, error handling, documentation)
• Cloud-Native development (stateless, containerized, scalable)
• Testing discipline (unit tests, integration tests, mocking)

Real-World Impact: Automatically generates documentation for codebases, reducing manual documentation time by 70%+ while improving consistency and quality.

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📄 License

MIT © 2026 Atinder Singh

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👤 Author

Atinder Singh
GitHub: @atindersingh

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