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Use tailwindCSS/shadcn UI to build beautiful app in Python streamlit

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Use tailwindCSS/shadcn UI to build beautiful app in Python streamlit

streamlit-shadcn-ui is a python lib that allows you to use shadcn components in streamlit. Benefits: 1. It extends the components options for building streamlit app 2. A morden and clean designed components for streamlit developer 3. streamlit-shadcn-ui provide a component "element", which allows you to write react-like component in python. You can nest and combine all the components with existing code. Related resources: + Github: https://github.com/ObservedObserver/streamlit-shadcn-ui + Demo: https://shadcn.streamlit.app/ + Streamlit: https://streamlit.io/ + Shadcn: https://ui.shadcn.com/

Visit product View on Hacker NewsNov 28, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos

67%67% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

34%34% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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