Hugging Face Spaces

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

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.

Vendor Risk Radar

Enterprises depend on dozens of third-party vendors, and when one is breached they usually learn from the news - weeks too late. The hardest part isn't a single vendor; modern breaches cascade. One stolen OAuth token (Salesloft-Drift), one compromised identity provider (Okta), one poisoned dependency silently spreads to every connected vendor. Companies have a vendor list but no visibility into the connections between vendors - so they can't answer the question that matters: which of my other vendors are now exposed, and must I act today? Vendor Risk Radar turns the live web into continuous, cited vendor risk intelligence. For each vendor it runs real-time discovery across Google News, breach trackers, CVE feeds, status pages and regulatory portals, extracts structured risk signals with AI, and computes a transparent 0–100 risk score with recency decay - every signal backed by a real source URL, never invented. Our differentiator, Blast Radius, reads recent security incidents across all vendors, automatically discovers the connections between them (shared attacker, OAuth token, identity provider, cross-vendor mention), and clusters them into single incidents. For each it issues a clear verdict—INVESTIGATE / MONITOR / NO ACTION - with reasoning and citations, correctly separating the 4-vendor Salesloft-Drift OAuth cascade from the Okta–Cloudflare identity incident. Built with Bright Data SERP API for live discovery and Web Unlocker to bypass bot-protected breach trackers and trust centers (provable 403→200), plus AI/ML API (Claude) for extraction. A hosted MCP server exposes the data to any AI agent—just ask "Am I exposed to a cascading breach this week?" The stack (FastAPI + React + SQLite) is containerized and deployed live on Hugging Face Spaces, moving third-party risk from reactive headlines to proactive, connection-aware monitoring.

Gulf SWF Filing Intelligence Agent

Gulf Sovereign Wealth Funds — ADIA, PIF, QIA, Mubadala, and ADQ — collectively manage over $4 trillion in assets. When these funds file SC 13D/13G forms with the SEC, it signals a major investment move. But SEC EDGAR rate-limits scrapers, blocks bots, and returns stale data. Real-time monitoring previously required $50,000/year data vendor subscriptions. Our solution uses Bright Data MCP Server to bypass EDGAR's rate limits and bot detection entirely — routing requests through 400M+ residential IPs, solving CAPTCHAs automatically, and fetching live filing data the moment SEC publishes it. The agent monitors all 5 Gulf SWFs simultaneously using LangChain and LangGraph. Each filing is parsed and analyzed by Gemini 2.0 Flash, which generates institutional-grade intelligence including BEARISH/BULLISH/NEUTRAL signal classification, EXIT/MAINTAIN/INCREASE action classification, sector analysis, and key investment insights. Results are stored in MongoDB Atlas and displayed on a live Next.js dashboard deployed on Vercel, with auto-refresh every 60 seconds. The FastAPI backend runs on Hugging Face Spaces for 24/7 availability. We successfully scraped and analyzed 87 real SEC filings across all 5 Gulf SWFs — including ADIA exiting AGL Private Credit Income Fund, Mubadala exiting AMD, and QIA exiting Credit Suisse — all with real-time Gemini intelligence. Bright Data Tools Used: MCP Server, Scrape as Markdown, Web Unlocker, Search Engine.

DUAL-BROKER SOTA ENGINE

Dual-Broker SOTA Engine is an automated trading system capturing real-time arbitrage between TradFi and Web3 prediction markets (Polymarket). The project proves that combining robust web scraping with low-latency LLM intelligence creates a secure, enterprise-grade engine. **Bright Data: Bypassing the Web's Toughest Blocks** Arbitrage demands real-time data from highly protected platforms like Yahoo Finance and Polymarket, where stale data leads to losses. The engine implements a resilient 3-tier extraction fallback powered by Bright Data: - **Bright Data Scraping Browser (CDP):** Renders JS-heavy, dynamic order books and scrapes depth snapshots via Puppeteer. - **Web Unlocker:** Bypasses advanced browser fingerprinting and CAPTCHAs on news feeds to guarantee a 99.9% extraction success rate. - **Residential Proxies:** Rotates IPs across a massive pool, ensuring high-frequency scraping runs continuously without rate-limiting or bans. Standardized via a Bright Data MCP Server, this stack transforms the open web into a structured enterprise data feed. **AI/ML API: High-Concurrency, Cost-Effective Swarm Intelligence** Running financial forecasts in real-time requires a consensus mechanism that is fast and affordable. The engine deploys a 50-persona Bayesian Swarm Consensus powered by the AI/ML API: - **Ultra-Low Latency:** AI/ML API orchestrates up to 50 parallel LLM persona requests simultaneously, converging the decision matrix in under 5 seconds. - **Economic Viability:** Leveraging top-tier models (DeepSeek-V4-Pro) via the gateway keeps token costs at a fraction of a cent. - **Real-Time P&L Safeguards:** The dashboard integrates with AI/ML API's billing API to track consumption and prove positive net profitability. With Apache Flink streaming and a Saga-based transaction sandbox for atomic execution, the engine proves that web data unlocked by Bright Data and reasoned by AI/ML API is ready for enterprise production.

Verdict — AI counterparty due diligence agent

Verdict is an autonomous, event-driven counterparty 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 bot detection, CAPTCHAs and geo-blocks defeat ordinary automation, and raw AI output is not something you can gate money on. Different by design: Most web-research agents just summarize what they find. Verdict screens official OFAC sanctions lists BEFORE it trusts the web; it treats every scraped page as hostile input to prevent prompt injection; it verifies its own citations; and it remembers each counterparty so re-checks surface what CHANGED. It is not a research chatbot — it is an auditable control that shows its work. How it works: Give Verdict a company name. It (1) screens OFAC sanctions — a confirmed hit is an instant hard BLOCK; (2) reads the live web via Bright Data MCP (search_engine + scrape_as_markdown); (3) sanitizes scraped content; (4) returns a verdict — APPROVE / ESCALATE / BLOCK — with a 0-100 risk score and a cited source behind every finding. Single checks and batch screening that ranks a vendor list riskiest-first. Trust & explainability: Citation verification flags every finding as verified or unverified against collected evidence (anti-hallucination). A transparent risk calculation shows the score as a baseline plus signed contributions per factor. Auditor-style reasoning adds an evidence-confidence tag, a "why not higher risk?" block, and an adversarial/contradiction check. Each verdict exports an audit-grade PDF report. Tech: Bright Data MCP (live web, load-bearing), Groq Llama 3.3 with AI/ML API as an alternative provider, deployed on Hugging Face Spaces. Proven live: Apple → APPROVE. Wirecard AG → BLOCK 95/100, surfacing the real fraud, cited. Bank Melli Iran → instant OFAC sanctions hard-block.

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.