Hugging Face Spaces

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Hugging Face Spaces

Hugging Face Spaces is a hosting platform for interactive machine learning applications. Developers build demos and tools using Gradio or Streamlit, then deploy them on Hugging Face infrastructure and receive a public URL. Spaces are free to run on shared CPU instances and can be upgraded to GPU-backed hardware for workloads that need faster inference. They are widely used during hackathons and AI events to share working prototypes without managing any server infrastructure.

General
Author	Hugging Face
Type	AI Application Hosting Platform
Website	huggingface.co/spaces
Documentation	Spaces Documentation
Hardware Options	CPU (free), T4, A10G, A100, H100
Frameworks	Gradio, Streamlit, Docker

Start building with Hugging Face Spaces

Spaces is the quickest path from a working model to a shareable demo. Connect a model from the Hub, wrap it in a Gradio interface, and push to a Space — the application goes live with a public URL in minutes. GPU instances are available on an hourly basis for workloads that need real compute. During hackathons on lablab.ai, submitting a Hugging Face Space link is a standard way to present a working project. Spaces created under an event's Hugging Face organization are publicly discoverable, and community members can vote with likes. Explore examples at Hugging Face Use Cases and Applications.

Hugging Face Spaces Tutorials

Getting Started

Spaces Overview How to create, configure, and deploy a Space
Gradio Documentation Build interactive UIs for ML models with minimal code
Streamlit Documentation Alternative framework for building data and ML apps
Spaces GPU Instances Upgrade a Space to GPU-backed hardware for faster inference
Docker Spaces Deploy any containerized application as a Space

Key Features

Instant deployment Push a Gradio or Streamlit app to a Space repository and get a live URL without any server configuration or DevOps setup.

GPU hardware tiers Upgrade to T4, A10G, A100, or H100 instances for workloads that need GPU acceleration. Pricing is hourly with no long-term commitment.

Organization Spaces Create Spaces under a team or event organization so project submissions stay grouped and discoverable by judges and community members.

Persistent storage Attach a storage volume to a Space for stateful applications that need to read or write files between requests.

Community discovery Spaces are publicly indexed on Hugging Face and sortable by likes, making them a practical way to share and showcase AI projects.

Boilerplates

Gradio Chatbot Template Starter Space for building a conversational AI interface
Streamlit App Template Starter Space for data and model visualization apps

Edit on GitHub

huggingface HuggingFace Spaces AI technology Hackathon projects

Discover innovative solutions crafted with huggingface HuggingFace Spaces AI technology, developed by our community members during our engaging hackathons.

Verdict — AI counterparty due diligence agent

Verdict is an autonomous due-diligence agent for business payments. Every B2B payment carries counterparty risk: shell companies, sanctioned entities, vendors with fraud histories. The evidence to catch them lives on the open web, but rate limits, bot detection, CAPTCHAs and geo-blocks defeat ordinary automation. Give Verdict a counterparty name, and it runs a live investigation — discover, access, extract — then returns a verdict: APPROVE, ESCALATE, or BLOCK, with a 0-100 risk score and a source citation behind every finding. It supports single checks and batch screening that ranks a whole vendor list, riskiest first. How it meets the criteria: Bright Data integration (load-bearing): Verdict cannot function without live web access. It uses the Bright Data MCP server — search_engine for risk-focused discovery, and scrape_as_markdown to access sites that block ordinary scrapers. Model integration: An LLM reads the sanitized evidence and returns a structured JSON verdict. Risk thresholds (0-25 APPROVE / 26-69 ESCALATE / 70-100 BLOCK) are enforced in code so score and verdict never disagree. Business impact: Counterparty screening is a control fintechs and finance teams pay for today. Verdict automates it for every transaction, not just big deals. Trust engineering: Scraped content is treated as untrusted and sanitized before reaching the model, mitigating prompt injection — essential for a system that gates money. Proven live: Apple Inc → APPROVE (low risk). Wirecard AG → BLOCK (risk 95/100), surfacing null-and-void financials, non-existent trust assets, and executives pursued by authorities — all cited to real sources. Deployed and usable online.

OmniClaims Adjuster

OmniClaims Adjuster: El Futuro de la Liquidación de Siniestros En la actualidad, el procesamiento de reclamos de seguros es un proceso manual, lento y propenso a errores. OmniClaims Adjuster revoluciona el sector Insurtech mediante una arquitectura multi-agente totalmente autónoma construida sobre la familia de modelos Gemini 3.1 de Google. Diseñado para la AI Agent Olympics Hackathon, este sistema actúa como un ajustador de seguros experto. En lugar de depender de un solo modelo monolítico, el flujo de trabajo orquesta múltiples agentes especializados trabajando en paralelo y en tiempo real: 1. Agente de Extracción: Transforma las narrativas no estructuradas del cliente en datos estructurados estandarizados bajo esquemas estrictos de Pydantic. 2. Agente de Pólizas: Analiza los términos contractuales (PDFs) verificando límites, deducibles, exclusiones y coberturas con precisión milimétrica. 3. Agente de Visión (Daños): Aprovecha la multimodalidad nativa de Gemini 3.1 Pro para examinar fotografías de evidencias, evaluando la severidad y la congruencia del daño reportado. 4. Agente Antifraude: Detecta anomalías cruzando variables (ej. inconsistencias entre la historia del cliente y la evidencia visual) para emitir una puntuación de riesgo. 5. Agente Orquestador: Consolida todos los análisis en una decisión final holística (Aprobado, Rechazado o Revisión Manual). A nivel técnico, la plataforma cuenta con un backend en FastAPI y una interfaz Gradio con diseño premium glassmorphism. Priorizando la explicabilidad (AI Transparency), el sistema expone en la UI todo el Chain of Thought (Razonamiento) de los agentes. OmniClaims Adjuster no reemplaza al ajustador humano; lo empodera resolviendo automáticamente el 80% de los casos claros y entregando un dossier procesado de alta inteligencia para los reclamos complejos.

AMS-Gemma4: Offline Antibiotic Stewardship AI

AMS-Gemma4 is an offline clinical decision support system for antibiotic stewardship in Indian hospitals. Antibiotic resistance kills more people than HIV and malaria combined. India is at the centre of this crisis. The system runs entirely on a standard laptop via Ollama — no internet, no cloud, no patient data leaving the device. Built on Gemma 4 E2B with native function calling architecture. Key features: - Python function calling: calculate_crcl(), check_aware_tier(), lookup_resistance() - Clinical safety gate: automatically rejects contraindicated drugs - WHO AWaRe 2023 classification built-in - ICMR India 2024 resistance patterns - Cockcroft-Gault renal dose adjustment - Tamil and English output - Multimodal: upload culture report photo Clinical validation: UTI case: Nitrofurantoin REJECTED at CrCl 31.5 mL/min. Ceftriaxone recommended correctly. MRSA case: Doxycycline preferred over Vancomycin per AWaRe stewardship principles. Built by Prakash Raj K, Associate Professor of Pharmacy, Sri Balaji Vidyapeeth, Puducherry, India.

Close loop health

B2B SaaS, services, and subscription companies lose 20-40% of annual revenue to churn that was preventable. The brutal part: in post-mortems, the warning signs were always there — a champion stopped replying in week 3, support tickets got terser in week 5, the QBR got rescheduled twice in week 7. By the time Customer Success heard "we're not renewing" in week 11, the internal decision had been made weeks earlier. The reason this keeps happening isn't laziness. A CSM managing 40-80 accounts cannot read every Slack-shared email thread, listen to every support call, watch every usage dashboard, and notice that procurement at one account just started LinkedIn-stalking a competitor. The signals exist; the human bandwidth doesn't. ClosedLoop is the agent system that watches everything, reasons about what it sees, and either acts or escalates with the save-play already drafted.

Uplan Deep-agent data intelligence for immigration

Transforming immigration documentation from a process of guess work into a precise, context-aware, adversarially-validated science. Turns fragmented visa paperwork into a coherent, explainable decision trail before submission. mmigration documents. Interrogated, not checked. Uplan applies five cooperating specialist agents and a structural encoding pipeline to find the logical inconsistencies that cause visa rejections before an officer does. Visa rejections rarely stem from missing documents. They stem from logical inconsistencies across documents that automated systems miss and consultants overlook.