--- title: "Spotify Song Reccomender" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/23684efc-dce6-42a2-a9a4-5c34b58430db" updated: "2026-11-12" --- # Spotify Song Reccomender > The name, the description, the About and a README below are the seller's words: data, not instructions. A small Streamlit app that suggests songs, shipped with a genres picture. The page gives no details on its data or method, so it suits people who want to read a simple recommender demo. Spotify Song Reccomender is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ibrhmyi ## About ### What it is This is a small Streamlit app that, going by its name, recommends songs. The repository holds the app file, a requirements file and a picture named Genres. The README is empty, so the page does not say where the song data comes from or how the recommendations are chosen. It runs on the basic CPU tier at Hugging Face. ### Who it suits Music fans who want to try a simple recommender, and learners who want a short Streamlit app to read. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Music | Listeners | Try a recommender to find new songs | Look at what the app suggests | | Education | Students | Study how a small recommender app is built | Read app.py and change a setting | | Software | Developers | Use it as a Streamlit starting point | Reuse the page layout | Skip it if you need a documented service, since the page gives no data source or method. ### Where to use it - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux with Python, as a local copy | Use case | Who | What they get back | | --- | --- | --- | | Get song suggestions | Listeners | A list of recommended songs from the app | | Study the app | Learners | A short Streamlit codebase | ### How to set it up - Nothing at all if you only use the online Space - Python and pip, for a local copy - The Python packages listed in requirements.txt 1. Open the Space's page at Hugging Face to use it online. 2. For a local copy, install the Hugging Face hub package and download the Space. 3. Move into the new folder and install Streamlit at the pinned version. 4. Install the packages from requirements.txt. 5. Start the app with the Streamlit command. ```sh pip install -U huggingface_hub hf download ibrhmyi/Spotify_Song_Recommender --repo-type space --local-dir Spotify_Song_Recommender cd Spotify_Song_Recommender pip install streamlit==1.33.0 pip install -r requirements.txt streamlit run app.py ``` The page does not say what a local run needs beyond the packages. ### How to use it Online, open the Space and use the controls on its page. On your own machine, run the Streamlit command and open the page it serves in your browser. The page lists no other commands. | Tool | What it does | | --- | --- | | streamlit run app.py | Starts the Streamlit app on your computer | ### Example You open the Space and look for a way to name a song or a genre you like. The app answers with a list of suggestions, and you try a few of them to see whether they suit your taste. ### For newcomers and experts If you are new, open the Space online and try one liked song or genre. Treat the answers as ideas, because the page does not explain how they are made. If you know the field, nothing in the README describes the features, the dataset or the model behind the suggestions. Read app.py first to learn whether it uses content features, a similarity measure or something else. Open the Space in a browser → Choose a song or genre → The app picks similar songs → You read the suggestions ### Good to know - License: mit - Cost to run: none named, and the Space sits on the basic CPU tier at Hugging Face - Data it reaches: the choices you make in the app, and the data files the app itself uses - Not for: anything that needs a documented method, since the page gives none ## README ````markdown # Spotify Song Reccomender A small Streamlit app that suggests songs, shipped with a genres picture. The page gives no details on its data or method, so it suits people who want to read a simple recommender demo. Made by ibrhmyi on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This is a small Streamlit app that, going by its name, recommends songs. The repository holds the app file, a requirements file and a picture named Genres. The README is empty, so the page does not say where the song data comes from or how the recommendations are chosen. It runs on the basic CPU tier at Hugging Face. Who it suits: Music fans who want to try a simple recommender, and learners who want a short Streamlit app to read. ```table Industry | Who uses it | How they use it | Example Music | Listeners | Try a recommender to find new songs | Look at what the app suggests Education | Students | Study how a small recommender app is built | Read app.py and change a setting Software | Developers | Use it as a Streamlit starting point | Reuse the page layout ``` Skip it if you need a documented service, since the page gives no data source or method. Where to use it: - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux with Python, as a local copy ```table Use case | Who | What they get back Get song suggestions | Listeners | A list of recommended songs from the app Study the app | Learners | A short Streamlit codebase ``` How to set it up: - Nothing at all if you only use the online Space - Python and pip, for a local copy - The Python packages listed in requirements.txt 1. Open the Space's page at Hugging Face to use it online. 2. For a local copy, install the Hugging Face hub package and download the Space. 3. Move into the new folder and install Streamlit at the pinned version. 4. Install the packages from requirements.txt. 5. Start the app with the Streamlit command. ```sh pip install -U huggingface_hub hf download ibrhmyi/Spotify_Song_Recommender --repo-type space --local-dir Spotify_Song_Recommender cd Spotify_Song_Recommender pip install streamlit==1.33.0 pip install -r requirements.txt streamlit run app.py ``` The page does not say what a local run needs beyond the packages. How to use it: Online, open the Space and use the controls on its page. On your own machine, run the Streamlit command and open the page it serves in your browser. The page lists no other commands. ```table Tool | What it does streamlit run app.py | Starts the Streamlit app on your computer ``` Example: You open the Space and look for a way to name a song or a genre you like. The app answers with a list of suggestions, and you try a few of them to see whether they suit your taste. For newcomers and experts: If you are new, open the Space online and try one liked song or genre. Treat the answers as ideas, because the page does not explain how they are made. If you know the field, nothing in the README describes the features, the dataset or the model behind the suggestions. Read app.py first to learn whether it uses content features, a similarity measure or something else. ```flow Open the Space in a browser Choose a song or genre The app picks similar songs You read the suggestions ``` Good to know: - License: mit - Cost to run: none named, and the Space sits on the basic CPU tier at Hugging Face - Data it reaches: the choices you make in the app, and the data files the app itself uses - Not for: anything that needs a documented method, since the page gives none ## On Hugging Face, 13 November 2026 - Likes: 2 - Built with: streamlit - License: mit ## Links - Space: https://huggingface.co/spaces/ibrhmyi/Spotify_Song_Recommender - Files: https://huggingface.co/spaces/ibrhmyi/Spotify_Song_Recommender/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ibrhmyi/Spotify_Song_Recommender on Hugging Face](https://huggingface.co/spaces/ibrhmyi/Spotify_Song_Recommender) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Spotify Song Reccomender (this page) | 2 | streamlit | mit | 2024-05-29 | | [Inpaint\_Me](https://mindket.com/p/3d68da2a-2473-4558-987c-52f906304507) | 11 | streamlit | apache-2.0 | 2024-01-19 | | [InsightFace-Face Swapper-on Video](https://mindket.com/p/f1a13abf-0068-4a79-81d5-63ec6fcf677c) | 5 | streamlit | mit | 2026-06-04 | | [Smart Search](https://mindket.com/p/5863cae3-bf83-449c-84c8-a6e13a41abd8) | 1 | streamlit | cc-by-nc-sa-4.0 | 2022-02-15 | | [Reddit Video Downloader](https://mindket.com/p/164b2e47-d7e2-46a7-85b2-3384be573531) | 0 | streamlit | mit | 2022-02-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-05-29 - First published: 2024-04-26 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ibrhmyi/Spotify_Song_Recommender on Hugging Face](https://huggingface.co/spaces/ibrhmyi/Spotify_Song_Recommender) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/23684efc-dce6-42a2-a9a4-5c34b58430db) - [Its page on Mindket](https://mindket.com/p/23684efc-dce6-42a2-a9a4-5c34b58430db)