Hugging Face Spaces

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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.

CrosslaneAI

CrosslaneAI is an agentic migration platform that autonomously ports CUDA codebases to ROCm/HIP, so teams can run their GPU workloads on AMD hardware without rewriting a single kernel by hand. For over fifteen years, the entire GPU computing world has been written in CUDA. That accumulated codebase — not the silicon itself — is the real reason teams stay locked to a single vendor. AMD's MI300X is genuinely competitive today, offering 192GB of HBM3 and far better price-per-hour economics, yet most organizations can't make the switch. The migration is manual, slow, error-prone, and expensive enough that it's typically handed to specialist consultants charging weeks of engineering time. PortForge productizes that migration and collapses it from weeks into minutes.

Contxt

Every AI grew a memory in 2026 — ChatGPT, Claude, Gemini, Copilot, Grok — but they're five walled gardens that never talk to each other, so you re-introduce yourself every time you switch. And every memory product (Mem0, Supermemory, Letta) is cloud-first and built for developers, not for you. Contxt is the consumer piece nobody built: one portable, privacy-first context layer that works across every AI, where your most sensitive data never leaves your device. A browser extension connects your Gmail, Calendar and Notion, then classifies every item on-device through a Crown-Jewels Gateway — Gemma 3 270M running in WebGPU plus a deterministic-rules safety floor. PRIVATE "crown jewels" stay local and end-to-end encrypted (the cloud is a blind relay of ciphertext, with keys transferred device-to-device by QR). SHARED context is distilled into reusable cards that any AI can read over MCP, and injected live into the composer on Claude, ChatGPT and Gemini. A companion web dashboard mirrors the extension in real time. One context layer. Every AI. Your data stays yours.

CredIQ — Smart Credit Card Spending Awareness App

CredIQ is a real-time credit card spending awareness app for India's 100 million credit card users who overspend without realizing it until the bill arrives. When you pay using GPay or tap your card, there is no live feedback on how much budget you have consumed. You find out when the bill comes. By then the damage is done. CRED focuses on bill payments. Jupiter shows patterns after the fact. Neither gives a live budget bar that updates the moment a transaction happens. That is the gap CredIQ fills. The core feature is a live budget bar powered by Firebase Realtime Database showing status in real time — Very Good in green, Good in light green, OK in yellow, Bad in orange, Very Bad in red. No mental calculation needed. See green, feel safe. See red, stop spending. The status also shows as a persistent notification and home screen widget so spending consciousness is always visible. AI features run on Groq's Llama 3.3 70B model for fast, low latency responses. The chatbot receives the user's live financial context on every turn — budget, spent amount, savings, credit card due, card offers, and recent transactions — giving specific personalized answers, not generic advice. The photo analyzer lets users photograph any product and get an instant recommendation on which card gives the best cashback or reward points for that purchase. For the demo we built a mock bank simulator alongside the main app. Every transaction in the mock bank writes to Firebase and CredIQ updates in under three seconds. In production this is replaced by the RBI regulated Account Aggregator framework through TSPs like Setu. Revenue comes from a freemium subscription at ₹99 per month, card application commissions from banking partners, and merchant affiliate deals. Half a percent of India's credit card market at that price is fifty crore annual recurring revenue. CredIQ is the spending companion the mass market credit card user has never had.

ReachIQ AI - Competitive Intelligence Engine

ReachIQ AI is a Generative Growth as a Service platform for YouTube creators. For this hackathon, we built a Video RAG-powered Competitive Intelligence Engine: it ingests competitor videos, transcribes them with Whisper, extracts on-screen text via OCR, generates embeddings, and stores everything in a Supabase pgvector knowledge base. Using semantic retrieval, the engine surfaces hook patterns, CTA strategies, thumbnail trends, and content gaps — then synthesizes them into a full executive intelligence report via Gemma 3 4B, deployed and validated on AMD Developer Cloud using ROCm 7.2 and vLLM. This turns hours of manual competitor research into seconds of AI-accelerated strategy.

Smart_route_ai

Smart Route AI is a hybrid LLM routing agent designed to optimize inference cost without sacrificing response quality. Instead of sending every prompt to a cloud-hosted model, the system first performs lightweight local task classification and complexity estimation using deterministic heuristics. Simple tasks such as factual questions, summarization, sentiment analysis, and named entity recognition are answered locally using Qwen2.5-0.5B-Instruct, while more demanding tasks like code generation, mathematical reasoning, and logical reasoning are routed to a Fireworks-hosted LLM. The routing engine evaluates prompt type, complexity, length, and formatting requirements to make explainable routing decisions with zero remote token overhead during classification. This approach aligns with the goal of minimizing API usage while maintaining high-quality responses for complex tasks. The project includes a responsive Streamlit interface that visualizes routing decisions, an explainable complexity score, and the selected inference path. It is fully Dockerized, supports environment-based configuration, and is compatible with the AMD Hackathon evaluation harness through dynamic ALLOWED_MODELS support. By combining efficient local inference with selective remote execution, Smart Route AI demonstrates a practical and scalable approach to token-efficient hybrid AI systems.

CaptionCraft AI

**CaptionCraft AI**, developed by **Team Code Craft**, is an AI-powered video captioning platform that generates contextual captions using a multimodal AI pipeline. Instead of relying only on speech, it combines visual and audio understanding to create captions that accurately reflect a video's content. Each uploaded video first undergoes validation before entering the pipeline. Keyframes are extracted to capture important scenes, while the audio track is processed separately. If speech is detected, it is transcribed using an AI speech recognition model. The extracted keyframes and transcript, when available, are used to generate a summary of the video, which is then transformed into natural, context-aware captions. Videos without speech are also supported by generating captions primarily from visual information. To improve performance, CaptionCraft AI uses a four-worker parallel architecture. Each worker independently processes one video through the complete pipeline, enabling multiple videos to be captioned simultaneously and significantly reducing processing time. The backend is provider-agnostic, allowing seamless switching between **Google Gemini**, **Fireworks AI**, and **locally hosted language models** without modifying the pipeline. This enables deployment based on performance, cost, privacy, or offline requirements. Built with **FastAPI** and **React + Vite**, the application follows a modular architecture that is easy to extend with multilingual support, subtitle exports (SRT/VTT), timestamps, speaker identification, additional caption styles, and platform integrations. By combining multimodal AI, configurable LLM backends, and parallel processing, CaptionCraft AI provides a fast, scalable, and flexible solution for automatically generating high-quality video captions.