{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gradio-spaces","slug":"gradio-spaces","name":"Gradio Spaces","type":"platform","url":"https://huggingface.co/spaces","page_url":"https://unfragile.ai/gradio-spaces","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gradio-spaces__cap_0","uri":"capability://automation.workflow.one.click.gradio.app.deployment.with.automatic.containerization","name":"one-click gradio app deployment with automatic containerization","description":"Automatically packages Gradio Python applications into Docker containers and deploys them to Hugging Face infrastructure without requiring manual Dockerfile creation or container registry management. The platform detects Gradio app code from a Git repository, infers dependencies from requirements.txt or pyproject.toml, and orchestrates the full deployment pipeline including container building, registry push, and service initialization.","intents":["Deploy a Gradio demo to the web in under 5 minutes without Docker knowledge","Share an interactive ML model interface with collaborators via a public URL","Iterate on a model demo and auto-redeploy on each Git push"],"best_for":["ML researchers prototyping model demos","solo developers building quick proof-of-concepts","teams sharing internal model interfaces without DevOps overhead"],"limitations":["Limited to Gradio and Streamlit frameworks — custom web frameworks require manual containerization","Automatic dependency detection may fail for complex multi-stage builds or non-standard package managers","No built-in CI/CD pipeline customization — deployment triggered only on Git push to main branch"],"requires":["Gradio 3.0+ or Streamlit 1.0+","Git repository (GitHub, GitLab, or Hugging Face Spaces repo)","requirements.txt or pyproject.toml for dependency declaration","Hugging Face account with write access to Spaces"],"input_types":["Python source code","requirements.txt or pyproject.toml","Git repository metadata"],"output_types":["Deployed web application","Public HTTPS URL","Container logs and deployment status"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_1","uri":"capability://automation.workflow.gpu.accelerated.inference.runtime.with.dynamic.allocation","name":"gpu-accelerated inference runtime with dynamic allocation","description":"Provisions ephemeral GPU resources (T4, A40, A100) on-demand for Space applications, with automatic scaling based on concurrent user load and request queue depth. The platform manages CUDA toolkit installation, GPU driver compatibility, and memory allocation without requiring manual infrastructure configuration, exposing GPU availability through environment variables that Gradio apps can query.","intents":["Run computationally expensive model inference (LLMs, diffusion models) without paying for always-on GPU instances","Scale GPU resources automatically when multiple users access a demo simultaneously","Avoid managing CUDA/cuDNN versions and GPU driver compatibility across deployment environments"],"best_for":["researchers demoing large language models or vision transformers","teams with variable traffic patterns who want to minimize GPU idle time","developers prototyping GPU-accelerated features without cloud infrastructure expertise"],"limitations":["GPU allocation is non-deterministic — cold-start latency ranges from 10-60 seconds on first request","No GPU persistence across deployments — model weights must be re-downloaded or cached in persistent storage on each restart","Limited to Hugging Face's GPU inventory — cannot guarantee specific GPU type or availability during peak hours","Billing model charges per GPU-hour, making long-running batch jobs expensive compared to reserved instances"],"requires":["Hugging Face Pro subscription or GPU upgrade token","CUDA-compatible Python libraries (torch, tensorflow, etc.)","Model weights accessible via Hugging Face Hub or public URL","Application code that gracefully handles GPU unavailability"],"input_types":["Python application code","Model checkpoint files or Hub references","User inference requests (text, image, audio)"],"output_types":["GPU-accelerated inference results","Deployment logs with GPU utilization metrics","Cost tracking data (GPU-hours consumed)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_10","uri":"capability://automation.workflow.scheduled.task.execution.with.cron.like.syntax","name":"scheduled task execution with cron-like syntax","description":"Allows Space owners to define periodic tasks (e.g., model retraining, data refresh, cache cleanup) using cron expressions, executed within the Space container on a schedule. Tasks are defined in a space.yaml configuration file and run with the same environment variables and persistent storage access as the main application. Execution logs are captured and available in the Space's log viewer.","intents":["Automatically retrain a model on a schedule without manual intervention","Refresh cached data (e.g., download latest dataset) at regular intervals","Perform cleanup tasks (delete old files, clear caches) to manage storage"],"best_for":["applications requiring periodic model updates or data refresh","teams automating maintenance tasks without external schedulers","demos that need to stay synchronized with upstream data sources"],"limitations":["Cron execution is best-effort, not guaranteed — no SLA for task execution time or reliability","Tasks run within the Space container, consuming GPU/CPU quota — long-running tasks may block user requests","No task queuing or retry logic — failed tasks are not automatically retried","Cron syntax is limited — no support for complex scheduling logic (e.g., run on first Monday of month)"],"requires":["space.yaml configuration file with cron schedule","Python script or shell command to execute","Awareness of Space resource limits and task duration"],"input_types":["Cron expression (e.g., '0 0 * * *' for daily at midnight)","Python script or shell command","space.yaml configuration"],"output_types":["Task execution logs","Exit status (success/failure)","Updated application state or data"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_11","uri":"capability://tool.use.integration.webhook.integration.for.external.event.triggers","name":"webhook integration for external event triggers","description":"Exposes Space-specific webhook endpoints that can be triggered by external services (GitHub, GitLab, custom applications) to redeploy the Space or execute custom logic. Webhooks are authenticated via HMAC signatures and can pass payload data to the Space application. Integration with Git platforms enables automatic redeploy on push or pull request events.","intents":["Automatically redeploy a Space when code is pushed to a Git repository","Trigger model retraining or data refresh from external CI/CD pipelines","Integrate Space updates into larger automation workflows"],"best_for":["teams using Git-based workflows with automatic deployment","applications integrating Spaces into CI/CD pipelines","researchers automating model updates from training pipelines"],"limitations":["Webhook authentication is HMAC-based, not OAuth — requires manual secret management","No built-in retry logic — failed webhook deliveries are not automatically retried","Webhook payload size is limited — large payloads may be truncated","No webhook history or audit trail — cannot track which webhooks were triggered or when"],"requires":["Webhook URL provided by Hugging Face","HMAC secret for authentication","External service (GitHub, GitLab, custom app) capable of sending HTTP POST requests"],"input_types":["HTTP POST request with JSON payload","HMAC signature header","Git event metadata (commit hash, branch, author)"],"output_types":["Space redeploy trigger","Custom application logic execution","Webhook delivery logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_12","uri":"capability://code.generation.editing.multi.file.code.editing.with.git.based.version.control","name":"multi-file code editing with git-based version control","description":"Provides a web-based code editor integrated into the Space interface, allowing inline editing of Python files, requirements.txt, and configuration files. Changes are automatically committed to the Space's Git repository with commit messages, enabling version history tracking and rollback to previous versions. The editor supports syntax highlighting, basic autocomplete, and file tree navigation.","intents":["Make quick edits to a Space without cloning the repository locally","Iterate on a demo by editing code directly in the browser","Track changes to a Space through Git commit history"],"best_for":["rapid prototyping and iteration on demos","non-technical users making simple configuration changes","teams collaborating on Spaces through Git pull requests"],"limitations":["Web editor lacks advanced IDE features (debugging, refactoring, linting) — complex edits are better done locally","No conflict resolution for concurrent edits — simultaneous edits by multiple users may cause merge conflicts","Syntax highlighting and autocomplete are basic — no language server protocol (LSP) support","File size limit of ~1MB per file — large files cannot be edited in the browser"],"requires":["Hugging Face account with write access to the Space","Basic Git knowledge for understanding commits and history"],"input_types":["Python source code","requirements.txt or pyproject.toml","Configuration files (space.yaml, README.md)"],"output_types":["Edited source code","Git commits with change history","Automatic Space redeploy on save"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_13","uri":"capability://text.generation.language.model.card.and.metadata.generation.with.hub.integration","name":"model card and metadata generation with hub integration","description":"Automatically generates and displays model cards (README.md with structured metadata) for Spaces, including model name, description, task type, and framework. Metadata is extracted from Space configuration and Git repository, and can be manually edited through the web interface. Model cards are rendered on the Hub with proper formatting and are indexed for search and discovery.","intents":["Create a professional model card for a Space without manual markdown writing","Ensure consistent metadata across Spaces for better discoverability","Document model capabilities, limitations, and intended use cases"],"best_for":["researchers publishing models with interactive demos","teams maintaining multiple Spaces with consistent documentation","educators creating model showcases with detailed descriptions"],"limitations":["Automatic metadata extraction is limited — complex model cards require manual editing","No template system for model cards — each Space requires custom documentation","Metadata is not validated — incorrect or incomplete metadata may not be caught","Model card changes are not versioned separately — history is tracked through Git commits"],"requires":["README.md file in Space repository","Proper YAML frontmatter for metadata (title, description, tags)"],"input_types":["Space configuration metadata","Git repository information","Manual README.md edits"],"output_types":["Rendered model card on Hub","Searchable metadata","Hub profile page"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_2","uri":"capability://memory.knowledge.persistent.file.storage.with.automatic.backup.and.versioning","name":"persistent file storage with automatic backup and versioning","description":"Provides a 50GB persistent filesystem mounted at /data that survives Space restarts, container updates, and deployment cycles. Storage is backed by Hugging Face's distributed object store with automatic daily snapshots and version history, accessible via standard Python file I/O or the Hugging Face Hub API for programmatic access.","intents":["Store model fine-tuning checkpoints or training artifacts that persist across Space redeploys","Cache downloaded model weights to avoid re-downloading on every cold start","Accumulate user-generated data (logs, uploaded files, inference results) for later analysis"],"best_for":["applications requiring state persistence across deployments","demos that download large model weights and need to cache them","collaborative spaces where multiple users contribute data over time"],"limitations":["50GB quota is shared across all Space instances — no per-instance isolation or quota enforcement","No built-in encryption at rest — sensitive data should be encrypted by application code","Backup snapshots are daily, not real-time — data written in the last 24 hours may be lost if storage fails","No transactional guarantees — concurrent writes from multiple Space instances may cause data corruption"],"requires":["Hugging Face account with Space ownership or write access","Standard Python file I/O (open, read, write) or Hugging Face Hub SDK","Awareness of 50GB quota and cleanup strategy for old files"],"input_types":["Python file objects","Binary data (model checkpoints, images)","Text files (logs, JSON, CSV)"],"output_types":["Persisted files in /data directory","File metadata (size, modification time)","Backup snapshots (daily)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_3","uri":"capability://search.retrieval.community.sharing.and.discoverability.with.hub.integration","name":"community sharing and discoverability with hub integration","description":"Automatically publishes deployed Spaces to the Hugging Face Hub with searchable metadata, README rendering, and social features (likes, comments, discussions). Spaces are indexed by model name, task type, and framework, enabling discovery through the Hub's search API and web interface. Integration with Hugging Face authentication allows users to fork Spaces, create private copies, and contribute improvements via pull requests.","intents":["Make a model demo discoverable to the broader ML community without managing separate hosting or marketing","Allow other researchers to fork and modify a Space for their own use cases","Track engagement metrics (views, likes, comments) to understand demo popularity"],"best_for":["researchers publishing model papers with interactive demos","open-source projects seeking community contributions","educators creating interactive tutorials and model showcases"],"limitations":["Discoverability depends on Hub's search ranking algorithm — no paid promotion or featured placement options","Community features (comments, discussions) are moderated by Hugging Face, not Space owner — no custom moderation rules","Fork/PR workflow is Git-based — requires users to understand Git and Hugging Face Hub authentication","No analytics beyond basic view counts — no detailed user behavior tracking or conversion funnel analysis"],"requires":["Hugging Face account with public Space","README.md file in Space repository for Hub display","Proper Space metadata (title, description, tags) in README frontmatter"],"input_types":["Space metadata (README, tags, description)","Git repository history","User interactions (likes, comments)"],"output_types":["Hub profile page with Space listing","Search index entries","Fork/PR notifications","Engagement metrics (views, likes)"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_4","uri":"capability://automation.workflow.streamlit.application.deployment.with.automatic.reload.on.code.changes","name":"streamlit application deployment with automatic reload on code changes","description":"Deploys Streamlit applications using the same containerization and infrastructure as Gradio, with built-in support for Streamlit's session state management, caching decorators (@st.cache_data, @st.cache_resource), and widget interactivity. The platform automatically detects Streamlit apps (streamlit run app.py) and configures the container entrypoint, exposing Streamlit's web server on port 7860.","intents":["Deploy a Streamlit data dashboard or interactive analysis tool without manual Docker setup","Leverage Streamlit's reactive programming model for rapid prototyping of data apps","Share a Streamlit app with collaborators who can interact with widgets and see live updates"],"best_for":["data analysts building interactive dashboards","teams prototyping data exploration tools","educators creating interactive data science tutorials"],"limitations":["Streamlit's session state is ephemeral — resets on every user session or Space restart, requiring explicit persistence to /data for state recovery","Caching decorators (@st.cache_data) may cause stale results if dependencies change — no automatic cache invalidation on code updates","Streamlit's reactive model reruns the entire script on every widget interaction, causing latency for computationally expensive operations","No built-in support for concurrent user sessions — multiple simultaneous users may experience performance degradation"],"requires":["Streamlit 1.0+","Python 3.8+","requirements.txt with Streamlit and dependencies","app.py or main entry point file"],"input_types":["Python source code","Data files (CSV, Parquet, JSON)","User widget interactions"],"output_types":["Rendered Streamlit web interface","Interactive visualizations and tables","Cached computation results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_5","uri":"capability://safety.moderation.environment.variable.and.secrets.management.with.hub.integration","name":"environment variable and secrets management with hub integration","description":"Provides a Space-level configuration interface for setting environment variables and secrets (API keys, database credentials) that are injected into the container at runtime. Secrets are encrypted at rest in Hugging Face's vault and never exposed in logs or container images, accessible only to the Space owner and authorized collaborators. Integration with Hugging Face Hub allows secrets to be referenced in code as standard environment variables (os.environ).","intents":["Securely pass API keys (OpenAI, Anthropic, etc.) to a Space without hardcoding them in source code","Configure database connection strings or authentication tokens for external services","Allow collaborators to set their own secrets without sharing credentials in Git"],"best_for":["applications integrating with third-party APIs requiring authentication","teams sharing Spaces where each member needs different credentials","production-grade demos requiring secure credential management"],"limitations":["Secrets are Space-level, not user-level — all collaborators with write access see the same secrets","No audit trail for secret access — cannot track which user accessed a secret or when","Secrets are injected as environment variables, exposing them to any process running in the container — no fine-grained access control","No secret rotation mechanism — manual update required to change credentials"],"requires":["Hugging Face account with Space ownership or write access","Knowledge of environment variable naming conventions","Application code that reads secrets from os.environ"],"input_types":["Secret key-value pairs (API keys, tokens, connection strings)","Space configuration metadata"],"output_types":["Encrypted secret storage","Environment variables injected at runtime","Access logs (limited)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_6","uri":"capability://automation.workflow.custom.domain.and.https.with.automatic.certificate.management","name":"custom domain and https with automatic certificate management","description":"Allows Space owners to configure custom domains (e.g., mymodel.example.com) and automatically provisions TLS certificates via Let's Encrypt with renewal handled by the platform. DNS configuration is simplified through CNAME records pointing to Hugging Face infrastructure, eliminating manual certificate management and renewal workflows.","intents":["Host a Space on a branded domain for professional presentation","Integrate a Space into a larger web application using a custom subdomain","Ensure HTTPS encryption for user data transmitted to the Space"],"best_for":["organizations deploying production-grade model demos","teams integrating Spaces into customer-facing applications","researchers publishing model papers with professional demo URLs"],"limitations":["Custom domain setup requires DNS access — not available for users without domain ownership","Certificate provisioning may take 5-15 minutes after DNS propagation — not instant","No support for wildcard certificates — each subdomain requires separate configuration","DNS propagation delays (24-48 hours) may prevent immediate domain activation"],"requires":["Domain ownership with DNS provider access","Hugging Face Pro subscription or custom domain upgrade","CNAME record pointing to Hugging Face infrastructure"],"input_types":["Domain name","DNS provider credentials or CNAME configuration"],"output_types":["HTTPS-enabled custom domain","TLS certificate (Let's Encrypt)","Automatic certificate renewal"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_7","uri":"capability://safety.moderation.private.space.access.control.with.hugging.face.authentication","name":"private space access control with hugging face authentication","description":"Restricts Space access to authenticated Hugging Face users or specific collaborators through role-based access control (owner, write, read). Private Spaces are not indexed by the Hub's search and require explicit invitation or authentication to access. Integration with Hugging Face's OAuth system allows seamless login without additional credential management.","intents":["Share a Space with a team or collaborators without making it public","Restrict access to a Space containing sensitive data or proprietary models","Control who can modify a Space (write access) vs. only view it (read access)"],"best_for":["teams collaborating on internal model demos","organizations deploying proprietary models with restricted access","researchers sharing preliminary work before publication"],"limitations":["Access control is binary (private/public) — no fine-grained permission levels beyond owner/write/read","No audit trail for access attempts — cannot track who accessed a Space or when","Invitation-based access requires manual user management — no LDAP/SAML integration for enterprise SSO","Private Spaces are not discoverable, limiting organic community engagement"],"requires":["Hugging Face account for Space owner and collaborators","Explicit invitation or access grant from Space owner"],"input_types":["Collaborator Hugging Face usernames","Access level (owner, write, read)","Space privacy setting"],"output_types":["Access control list","Invitation notifications","Authentication redirects"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_8","uri":"capability://automation.workflow.automatic.dependency.resolution.and.python.version.selection","name":"automatic dependency resolution and python version selection","description":"Parses requirements.txt or pyproject.toml to automatically resolve Python package dependencies and select an appropriate Python version (3.8, 3.9, 3.10, 3.11, 3.12) for the container. The platform uses pip or uv (fast dependency resolver) to install packages during container build, with caching of previously built layers to accelerate subsequent deployments.","intents":["Deploy a Space without manually writing a Dockerfile or specifying Python version","Ensure consistent dependency versions across development and deployment environments","Accelerate Space redeploys by caching dependency layers"],"best_for":["developers unfamiliar with Docker and dependency management","teams with simple, standard Python dependency stacks","rapid prototyping scenarios where build speed matters"],"limitations":["Automatic Python version selection may choose a version incompatible with some packages — no explicit version pinning in Space config","Complex dependency resolution (conflicting versions, platform-specific packages) may fail silently or choose suboptimal versions","No support for non-Python dependencies (system libraries, compiled binaries) — requires manual Dockerfile for advanced cases","Dependency caching is per-Space, not shared across Spaces — each Space rebuilds its dependency layer independently"],"requires":["requirements.txt (pip format) or pyproject.toml (Poetry/setuptools format)","Valid Python package names and version specifiers","Python 3.8+ compatibility for all dependencies"],"input_types":["requirements.txt or pyproject.toml","Python source code"],"output_types":["Installed Python packages","Container image with dependencies","Build logs with dependency resolution details"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__cap_9","uri":"capability://automation.workflow.real.time.application.logs.and.deployment.status.monitoring","name":"real-time application logs and deployment status monitoring","description":"Streams application logs (stdout, stderr) to the Space's web interface in real-time, with filtering by log level and timestamp. Deployment status is tracked through multiple stages (building, pushing, deploying, running) with detailed error messages and stack traces for debugging. Logs are retained for 7 days and searchable through the Hub API.","intents":["Debug application errors and exceptions without SSH access to the container","Monitor Space health and detect performance issues or crashes","Troubleshoot deployment failures (dependency conflicts, build errors)"],"best_for":["developers debugging Space issues without direct container access","teams monitoring production demos for errors and crashes","researchers diagnosing model inference failures"],"limitations":["Logs are retained for only 7 days — long-term log analysis requires external log aggregation (ELK, Datadog)","No structured logging — logs are plain text, not JSON, making programmatic parsing difficult","Real-time log streaming has ~1-2 second latency — not suitable for high-frequency event monitoring","No log sampling or filtering at the source — high-volume logging may impact Space performance"],"requires":["Application code that writes to stdout/stderr","Hugging Face account with Space access"],"input_types":["Application stdout/stderr output","Deployment events (build, push, deploy)"],"output_types":["Real-time log stream","Deployment status updates","Error messages and stack traces","Log search results (7-day retention)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gradio-spaces__headline","uri":"capability://deployment.infra.ai.model.deployment.platform","name":"ai model deployment platform","description":"Gradio Spaces is an AI model deployment platform that allows users to instantly deploy interactive machine learning demos with GPU support and community sharing features.","intents":["best AI model deployment platform","AI model deployment for interactive demos","top platforms for hosting ML demos","deploying Gradio apps online","how to share AI models with Gradio"],"best_for":["developers looking to showcase AI models"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Gradio 3.0+ or Streamlit 1.0+","Git repository (GitHub, GitLab, or Hugging Face Spaces repo)","requirements.txt or pyproject.toml for dependency declaration","Hugging Face account with write access to Spaces","Hugging Face Pro subscription or GPU upgrade token","CUDA-compatible Python libraries (torch, tensorflow, etc.)","Model weights accessible via Hugging Face Hub or public URL","Application code that gracefully handles GPU unavailability","space.yaml configuration file with cron schedule","Python script or shell command to execute"],"failure_modes":["Limited to Gradio and Streamlit frameworks — custom web frameworks require manual containerization","Automatic dependency detection may fail for complex multi-stage builds or non-standard package managers","No built-in CI/CD pipeline customization — deployment triggered only on Git push to main branch","GPU allocation is non-deterministic — cold-start latency ranges from 10-60 seconds on first request","No GPU persistence across deployments — model weights must be re-downloaded or cached in persistent storage on each restart","Limited to Hugging Face's GPU inventory — cannot guarantee specific GPU type or availability during peak hours","Billing model charges per GPU-hour, making long-running batch jobs expensive compared to reserved instances","Cron execution is best-effort, not guaranteed — no SLA for task execution time or reliability","Tasks run within the Space container, consuming GPU/CPU quota — long-running tasks may block user requests","No task queuing or retry logic — failed tasks are not automatically retried","builder identity is not verified yet","no observed match outcomes 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