FastAPI OpenAI Practice API

A simple practice project for building AI-powered API endpoints using Python, FastAPI, and the OpenAI API.

This project exposes several AI endpoints for:

• Text summarization
• Translation
• Title generation
• Code explanation

---

Getting Started

1. Create and activate a Python virtual environment.
2. Install dependencies:

pip install -r requirements.txt

3. Create a .env file in the project root with your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

4. Start the app:

uvicorn app.main:app --reload

5. Open the API docs in your browser:

http://127.0.0.1:8000/docs

---

API Endpoints

GET /

Returns a simple health check message.

POST /summarize

Request body:

{
  "text": "Long text to summarize...",
  "style": "simple"
}

Response:

{
  "summary": "A concise summary..."
}

POST /translate

Request body:

{
  "text": "Text to translate...",
  "target_language": "French"
}

Response:

{
  "translation": "Texte traduit..."
}

POST /generate-title

Request body:

{
  "text": "Text to generate titles from...",
  "count": 5
}

Response:

{
  "titles": "1. Title idea one\n2. Title idea two..."
}

POST /explain-code

Request body:

{
  "code": "print(\"Hello world\")",
  "language": "python"
}

Response:

{
  "explaination": "Explanation of the code..."
}

---

Project Structure

fastapi-ai-api/
│
├── app/
│   ├── main.py
│   ├── openai_service.py
│   └── schemas.py
│
├── .env
├── .gitignore
├── requirements.txt
└── README.md

---

Notes

• The project uses openai with the responses API and gpt-4.1-mini.
• Validation is handled by Pydantic models defined in app/schemas.py.
• If you update your .env, restart the app to apply changes.