Gradio Spaces

Automatically detects Gradio Python code in a Git repository, containerizes it with inferred dependencies, and deploys to Hugging Face infrastructure without manual Docker configuration. Uses git webhooks to trigger rebuilds on repository updates, enabling continuous deployment of UI changes without redeployment steps.

Allocates ephemeral GPU resources (NVIDIA T4, A100, or CPU) to running Spaces based on demand and tier, with automatic fallback to CPU if GPU quota is exhausted. Integrates with CUDA/cuDNN libraries pre-installed in the container runtime, enabling zero-configuration GPU inference for PyTorch, TensorFlow, and JAX models.

Deploys Streamlit apps alongside Gradio using the same containerization and infrastructure, with automatic detection of streamlit_app.py or app.py entry points. Supports Streamlit-specific features (caching, session state, secrets management) without additional configuration.

Generates embeddable iframe code that can be inserted into external websites, with postMessage-based communication enabling parent pages to send inputs and receive outputs from the Space. Handles CORS and iframe sandboxing automatically, allowing Spaces to be embedded on any domain.

Provides a library of pre-built Gradio components (Textbox, Image, Audio, Video, DataFrame, Plot) that abstract HTML/CSS/JavaScript, enabling rapid UI development without frontend expertise. Components handle input validation, serialization, and rendering automatically, with support for custom CSS and JavaScript extensions.

Provides ephemeral and persistent storage volumes mounted to the Space container, with automatic garbage collection after inactivity and quota enforcement per tier. Persistent storage survives container restarts and redeployments, while temporary storage is cleared on shutdown, enabling stateful applications without external databases.

Automatically publishes deployed Spaces to the Hugging Face Hub with metadata (title, description, tags, thumbnail), making them discoverable via search, trending lists, and model/dataset pages. Integrates with Hub authentication to enable private Spaces with access control, and embeds Space iframes on model cards for direct model evaluation.

Enables Gradio components to stream outputs in real-time to the browser using WebSocket connections, supporting long-running inference tasks, live video processing, and interactive chat interfaces. Handles connection lifecycle (open, message, close) and automatic reconnection on network interruption, with server-side session management per user.

Stores API keys, database credentials, and other secrets in encrypted environment variables that are injected into the Space container at runtime, with no plaintext storage in code or logs. Secrets are scoped per Space and encrypted at rest using Hugging Face-managed keys, with audit logs tracking access.

Maps custom domains (e.g., mymodel.com) to a Space with automatic SSL/TLS certificate provisioning via Let's Encrypt, enabling branded URLs without manual certificate management. Supports CNAME-based DNS routing and automatic certificate renewal before expiration.

Executes Python functions on a schedule (hourly, daily, weekly) using a cron-like syntax, enabling background jobs like model retraining, data refresh, or periodic cleanup without manual triggers. Tasks run in the same container as the Space and have access to persistent storage and environment variables.

Supports organizing Gradio/Streamlit apps across multiple Python files with automatic import resolution and dependency detection, enabling modular code structure without manual requirements.txt updates. Detects imports across all files and infers dependencies from package names, reducing configuration overhead.

Monitors a Git repository (GitHub, GitLab, Hugging Face Hub) for changes and automatically triggers Space rebuilds when code is pushed, using webhooks to detect updates without polling. Supports branch selection and automatic rollback to previous versions if deployment fails.