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Gradio Notebook– Notebook UX for Generative AI models in Hugging Face

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Gradio Notebook– Notebook UX for Generative AI models in Hugging Face

TLDR: Gradio Notebook is a notebook component for generative AI that offers the fastest way to create a Hugging Face space (in < 10 lines of code), and a familiar notebook interface for interacting with any combination of text, image and audio models in a single space. Follow this Quickstart to create a gradio notebook for yourself: https://huggingface.co/spaces/lastmileai/gradio-notebook-template Hi HN Community! I’m Sarmad, co-founder of LastMile AI, and today I’m excited to share Gradio Notebook, our latest open-source project in collaboration with Hugging Face. Gradio Notebook [1] is a notebook component that offers an interactive playground for Hugging Face models of any modality in a single Hugging Face space. It brings the familiar UX of a notebook to generative AI model inference, and enables much richer interactions than a chat interface allows. If you're a Hugging Face space creator looking for speed and ease of development, Gradio Notebook gets your space ready in 5 minutes with < 10 lines of code (without having to build your own UI at all). Key features: * Use any combination of text, image, or audio models in a single space, and even chain models together. * Interact and experiment with models using a pre-built notebook UI, which is a familiar, intuitive interface for multimodal interaction. At its core, the notebook contains cells (we call them “prompts”), which are associated with a model to run. * Get a shareable URL to share the space outputs with others (my personal favorite feature, since it isn’t possible on HF today). * Download your space config (prompts and model settings) as a JSON file, and use it in your application via our SDK [2]. This is a new user experience for generative AI, and for Hugging Face, and we are looking for feedback. If you frequently use HF spaces, and especially if you like to build spaces, please give this a try and get in touch! Excited to hear from you, and looking forward to seeing what you create with Gradio Notebook! To get started, please use the Quickstart [1], which contains everything to set up and play with the component [1] Link to Quickstart: https://huggingface.co/spaces/lastmileai/gradio-notebook-template [2] SDK: https://aiconfig.lastmileai.dev/docs/gradio-notebook#gradio-notebook-api [3] Gradio Notebook Docs: https://aiconfig.lastmileai.dev/docs/gradio-notebook [4] Repo (it’s all open source): https://github.com/lastmile-ai/aiconfig/tree/main/gradio-notebook

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, new · Missing: mac, agents, macos
95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, open source, io · Missing: https docs, just released, exist
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, interface · Missing: plus, platform, reviews
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, audio · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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