Python project folder structure for a GenAI application

📁 Project Structure Overview

genai_app/
│
├── app/
│   ├── __init__.py
│   ├── main.py               # FastAPI app entry point
│   ├── config.py             # Environment/config settings
│   ├── models/               # Pydantic models and data schemas
│   │   └── genai.py
│   ├── services/             # Core GenAI logic (e.g., LangChain, transformers)
│   │   └── genai_service.py
│   ├── api/                  # Route definitions
│   │   ├── __init__.py
│   │   └── genai_routes.py
│   ├── utils/                # Helper functions, logging, etc.
│   │   └── helpers.py
│   └── middleware/           # Custom middleware (e.g., logging, auth)
│       └── auth.py
│
├── tests/                    # Unit and integration tests
│   ├── __init__.py
│   └── test_genai.py
│
├── requirements.txt          # Python dependencies
├── .env                      # Environment variables
├── README.md                 # Project documentation
└── run.sh                    # Shell script to run the app with Uvicorn

---

🚀 Key Components Explained

main.py

from fastapi import FastAPI
from app.api.genai_routes import router as genai_router

app = FastAPI(title="GenAI FastAPI App")
app.include_router(genai_router)

genai_routes.py

from fastapi import APIRouter
from app.services.genai_service import generate_response
from app.models.genai import GenAIRequest

router = APIRouter()

@router.post("/generate")
def generate_text(request: GenAIRequest):
    return generate_response(request.prompt)

genai_service.py

def generate_response(prompt: str) -> dict:
    # Call to GenAI model (e.g., OpenAI, HuggingFace, LangChain)
    return {"response": f"Generated text for: {prompt}"}

---

🧪 Running the App

run.sh

#!/bin/bash
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

---

🧠 Optional Enhancements

• Docker support: Add Dockerfile and docker-compose.yml
• Async support: Use async def in routes and services
• Model integration: Add HuggingFace or OpenAI SDK in genai_service.py
• LangGraph or LangChain: Integrate in services/ for advanced workflows