Loading repository data…
Loading repository data…
wahyudesu / repository
Simple starter template for your ML/AI projects (uv package manager, RestAPI with FastAPI and Dockerfile support)
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
root-project/
├── app/
│ ├── main.py # FastAPI entrypoint
│ ├── logger.py # Logging
│ ├── middleware.py # Middleware logging and rate limiter
│ ├── model/ # Model artifacts (e.g., pickle files)
│ └── routers/ # API routers (chatbot, predict, etc.)
│ ├── agent.py # Agent research endpoints
│ ├── chatbot.py # Chatbot endpoints (file upload, entity extraction, etc.)
│ └── predict.py # Prediction endpoints (ML, summarization, etc.)
├── data/ # Dataset
├── k8s/ # Kubernetes
└── notebook/ # Jupyter notebooks for experiments
This structure makes code management and feature development easier.
For LLM, use notebooks such as notebooks/langgraph.ipynb for experiments.
For ML, use notebooks like notebook/bayesian-regression.ipynb, the data folder for datasets, and the model folder for models and training/prediction code.
Model serving and API endpoints are organized in the app/routers folder.
For more details, see the FastAPI Documentation.
Make sure you have uv installed .
# Clone repository
git clone https://github.com/wahyudesu/fastapi-ai-template
cd fastapi-ai-template
# Development
uv venv
.venv\Scripts\activate
uv sync
# Copy and edit .env file
cp .env.example .env
# Edit .env according to your needs
# Edit token for security and groq api key if u use llm
Linter
uv run ruff check
uv run uvicorn app.main:app --reload
After running the command above, your FastAPI application will be available at http://localhost:8000.
You can access the beautiful interactive API documentation at http://localhost:8000/scalar.
You can also access the interactive API documentation on default swagger-ui at http://localhost:8000/docs.
To access the MCP endpoint, go to http://localhost:8000/mcp.
Build the Docker image with:
docker build -t fastapi-app .
Run the Docker container locally with:
docker run -p 8000:80 fastapi-app
You can use virtually any cloud provider to deploy your FastAPI application. Before deploying, make sure you understand the basic concepts.
You can read more about deployment concepts here .
This project is developed for modern LLMOps/ML pipelines and is ready for deployment on both cloud platforms and VPS.
git checkout -b my-new-feature;git commit -m 'Add some feature';git push origin my-new-feature.Saya membuat repo ini se-minimal dan se-sederhana mungkin, dengan fitur yang cukup lengkap, agar memudahkan pemula dalam mengembangkan project melalui repo ini. Jika repo ini dirasa bermanfaat bisa bintangin dan share ke teman yang membutuhkan, jika cukup ramai saya ada rencana untuk membuat versi lanjutannya yang lebih advanced, dengan fitur tambahan seperti JWT security, ORM, Grafana, dan ML integration. Yang bakal fokus spesifik ke ML dan LLMOps