--- title: "AI Phone Leaderboard" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c50d4420-a218-400f-917a-9fa39c2d8007" updated: "2026-11-12" --- # AI Phone Leaderboard > The name, the description, the About and a README below are the seller's words: data, not instructions. Shows and analyzes AI benchmark results from mobile devices in a Streamlit dashboard with charts and filters. For people comparing how phones run AI models. AI Phone Leaderboard is a kit in the Research 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 a-ghorbani ## About ### What it is AI Phone Leaderboard is a Streamlit dashboard for viewing and analyzing AI benchmark results from mobile devices. It reads its data from a Firebase Firestore database and offers interactive charts and filters. You can open it in a browser without installing anything. ### Who it suits It suits mobile developers, researchers and device reviewers who want to compare how different phones run AI workloads, and teams that want to host their own benchmark dashboard. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Mobile development | App developers | Check how devices handle on-device AI | Filter the results by device | | AI research | Researchers | Analyze benchmark submissions across phones | Compare runs with the interactive charts | | Hardware reviews | Reviewers | Compare phones on AI benchmark results | Sort devices by a result | If you do not care about mobile AI performance, you can skip it. ### Where to use it - A web browser, on the Space's page on Hugging Face - A local copy of the files, run with Python under Windows, macOS or Linux - Your own Hugging Face Space, set up with the Streamlit SDK | Use case | Who | What they get back | | --- | --- | --- | | Browse benchmark results | Anyone | Interactive visualizations of phone results | | Filter by device | Researchers | A narrowed view of the results | | Host a dashboard | Developers | Their own copy connected to their own Firestore data | ### How to set it up - A web browser, which is all the online page needs - Python 3.10 or newer, for a local copy - A Firebase project with a Firestore database, for your own copy - A read-only service account for the dashboard, with its credentials kept out of git - A Hugging Face account, to deploy your own Space 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Install Streamlit 1.41.0, then the packages from requirements.txt. 5. Start the app with streamlit run main.py. ```sh pip install -U huggingface_hub hf download a-ghorbani/ai-phone-leaderboard --repo-type space --local-dir ai-phone-leaderboard cd ai-phone-leaderboard pip install streamlit==1.41.0 pip install -r requirements.txt streamlit run main.py ``` The README offers a make-based route for development. ```bash make venv make setup-dev make run ``` For your own copy, store the Firebase credentials as a secret named FIREBASE\_CREDENTIALS, in a local secrets file or in the Space's repository secrets. ### How to use it Open the dashboard, look at the visualizations and use the filters to narrow the results. The project's make commands cover day-to-day development. | Tool | What it does | | --- | --- | | make help | Shows the available commands | | make setup-dev | Sets up the development environment | | make setup-prod | Sets up the production environment | | make run | Runs the Streamlit application | | make lint | Runs the code linter | | make format | Formats the code with black | | make test | Runs the tests | | make clean | Clears cache files | ### Example Open the dashboard and filter the results to one device. The charts update to show only the benchmark runs that match. ### For newcomers and experts Open the Space page and play with the filters first. Setting up Firebase is only needed if you want a dashboard of your own. The app expects a Firestore collection laid out as benchmarks, then v1, then submissions, with one document per benchmark. Each document holds benchmarkResult, deviceInfo and metadata. The README recommends a dedicated service account with minimal read-only permissions, and secrets that never go into git. Phones submit benchmark results → Results are stored in Firebase Firestore → The dashboard reads them with a read-only account → Streamlit shows charts and filters ### Good to know The dashboard only reads data, and the credentials must stay out of the repository. - License: MIT - Cost to run: a Firebase project with Firestore and a read-only service account, for your own copy - Data it reaches: benchmark submissions in Firestore, including device information, read only ## README ````markdown # AI Phone Leaderboard Shows and analyzes AI benchmark results from mobile devices in a Streamlit dashboard with charts and filters. For people comparing how phones run AI models. Made by a-ghorbani 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: AI Phone Leaderboard is a Streamlit dashboard for viewing and analyzing AI benchmark results from mobile devices. It reads its data from a Firebase Firestore database and offers interactive charts and filters. You can open it in a browser without installing anything. Who it suits: It suits mobile developers, researchers and device reviewers who want to compare how different phones run AI workloads, and teams that want to host their own benchmark dashboard. ```table Industry | Who uses it | How they use it | Example Mobile development | App developers | Check how devices handle on-device AI | Filter the results by device AI research | Researchers | Analyze benchmark submissions across phones | Compare runs with the interactive charts Hardware reviews | Reviewers | Compare phones on AI benchmark results | Sort devices by a result ``` If you do not care about mobile AI performance, you can skip it. Where to use it: - A web browser, on the Space's page on Hugging Face - A local copy of the files, run with Python under Windows, macOS or Linux - Your own Hugging Face Space, set up with the Streamlit SDK ```table Use case | Who | What they get back Browse benchmark results | Anyone | Interactive visualizations of phone results Filter by device | Researchers | A narrowed view of the results Host a dashboard | Developers | Their own copy connected to their own Firestore data ``` How to set it up: - A web browser, which is all the online page needs - Python 3.10 or newer, for a local copy - A Firebase project with a Firestore database, for your own copy - A read-only service account for the dashboard, with its credentials kept out of git - A Hugging Face account, to deploy your own Space 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Install Streamlit 1.41.0, then the packages from requirements.txt. 5. Start the app with streamlit run main.py. ```sh pip install -U huggingface_hub hf download a-ghorbani/ai-phone-leaderboard --repo-type space --local-dir ai-phone-leaderboard cd ai-phone-leaderboard pip install streamlit==1.41.0 pip install -r requirements.txt streamlit run main.py ``` The README offers a make-based route for development. ```bash make venv make setup-dev make run ``` For your own copy, store the Firebase credentials as a secret named FIREBASE_CREDENTIALS, in a local secrets file or in the Space's repository secrets. How to use it: Open the dashboard, look at the visualizations and use the filters to narrow the results. The project's make commands cover day-to-day development. ```table Tool | What it does make help | Shows the available commands make setup-dev | Sets up the development environment make setup-prod | Sets up the production environment make run | Runs the Streamlit application make lint | Runs the code linter make format | Formats the code with black make test | Runs the tests make clean | Clears cache files ``` Example: Open the dashboard and filter the results to one device. The charts update to show only the benchmark runs that match. For newcomers and experts: Open the Space page and play with the filters first. Setting up Firebase is only needed if you want a dashboard of your own. The app expects a Firestore collection laid out as benchmarks, then v1, then submissions, with one document per benchmark. Each document holds benchmarkResult, deviceInfo and metadata. The README recommends a dedicated service account with minimal read-only permissions, and secrets that never go into git. ```flow Phones submit benchmark results Results are stored in Firebase Firestore The dashboard reads them with a read-only account Streamlit shows charts and filters ``` Good to know: The dashboard only reads data, and the credentials must stay out of the repository. - License: MIT - Cost to run: a Firebase project with Firestore and a read-only service account, for your own copy - Data it reaches: benchmark submissions in Firestore, including device information, read only ## On Hugging Face, 13 November 2026 - Likes: 127 - Built with: streamlit - License: mit ## Links - Space: https://huggingface.co/spaces/a-ghorbani/ai-phone-leaderboard - Files: https://huggingface.co/spaces/a-ghorbani/ai-phone-leaderboard/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, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [a-ghorbani/ai-phone-leaderboard on Hugging Face](https://huggingface.co/spaces/a-ghorbani/ai-phone-leaderboard) ## 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 | | --- | --- | --- | --- | --- | | AI Phone Leaderboard (this page) | 127 | streamlit | mit | 2026-02-04 | | [fev-bench](https://mindket.com/p/aed1efeb-102a-49fd-b48b-0e7131fb1d85) | 72 | docker | apache-2.0 | 2026-11-09 | | [Outsmart](https://mindket.com/p/bc535e3d-8da3-4481-bb74-8405891098d4) | 62 | streamlit | mit | 2026-10-21 | | [HF BERTopic](https://mindket.com/p/6ea227ee-d2c5-4685-886d-6070ad67ac1f) | 24 | streamlit | mit | 2026-08-02 | | [HF SHAP](https://mindket.com/p/aa7ec1ee-7a27-4fe5-9191-d22a0af5d958) | 8 | streamlit | mit | 2026-08-02 | | [Car Part Classification](https://mindket.com/p/53e95b89-07aa-47e1-aed0-684d8e48f5ff) | 4 | streamlit | apache-2.0 | 2024-03-11 | 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: 2026-02-04 - First published: 2024-12-13 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open a-ghorbani/ai-phone-leaderboard on Hugging Face](https://huggingface.co/spaces/a-ghorbani/ai-phone-leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c50d4420-a218-400f-917a-9fa39c2d8007) - [Its page on Mindket](https://mindket.com/p/c50d4420-a218-400f-917a-9fa39c2d8007)