Huggingface Spaces
Hugging Face Spaces is a platform for hosting and sharing interactive machine learning demos and applications — supporting Gradio (auto-generated UI from Python functions), Streamlit (data dashboards), and Docker (any custom application), with free CPU hosting and paid GPU tiers (A10G at $1.05/hr, A100 at $4.13/hr), making it the easiest way to turn any trained ML model into a publicly accessible, interactive web application that anyone can try without installation.
What Is Hugging Face Spaces?
- Definition: A hosting platform (huggingface.co/spaces) that deploys ML applications from a Git repository — automatically detecting the framework (Gradio, Streamlit, or Docker), building the environment, and serving the application at a public URL.
- The Problem: You trained a great model. Now what? Sharing a .pkl file or a Colab notebook isn't useful for non-technical stakeholders. They need to click a button, upload an image, and see the result.
- The Solution: Spaces provides free hosting for interactive demos. Write a 10-line Gradio app, push to Spaces, and share a URL. Your manager, client, or the world can interact with your model instantly.
Supported Frameworks
| Framework | Use Case | Code Required | Example |
|---|---|---|---|
| Gradio | Quick ML demos with auto-generated UI | ~10 lines | Image classifier, text generator, chatbot |
| Streamlit | Data dashboards and interactive apps | ~30 lines | Data exploration, analytics dashboards |
| Docker | Any custom application | Dockerfile | FastAPI, Next.js, custom web apps |
| Static HTML | Simple static pages | HTML files | Documentation, portfolios |
Hardware Tiers
| Tier | Hardware | RAM | Cost | Use Case |
|---|---|---|---|---|
| Free | 2 vCPU | 16GB | $0 | Small demos, starter projects |
| CPU Upgrade | 8 vCPU | 32GB | $0.03/hr | Larger CPU models |
| T4 Small | T4 GPU | 16GB | $0.60/hr | Medium GPU inference |
| A10G Small | A10G GPU | 24GB | $1.05/hr | Large model inference |
| A100 Large | A100 GPU | 80GB | $4.13/hr | LLM demos, Stable Diffusion |
Gradio Example (10 lines)
import gradio as gr
from transformers import pipeline
classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
def classify(image):
results = classifier(image)
return {r["label"]: r["score"] for r in results}
demo = gr.Interface(fn=classify, inputs="image", outputs="label")
demo.launch()Popular Spaces
| Space | Model | Usage |
|---|---|---|
| Stable Diffusion | Text-to-image generation | Millions of users |
| ChatGPT-style demos | Open-source LLMs (Llama, Mistral) | Interactive chat |
| Whisper | Speech-to-text | Audio transcription |
| DALL-E Mini | Text-to-image (viral in 2022) | Public demo |
Hugging Face Spaces is the standard platform for sharing ML demos — providing free hosting for Gradio, Streamlit, and Docker applications with optional GPU hardware, enabling anyone to turn a trained model into an interactive web application accessible via a public URL in minutes.