--- title: "Mushroom_edibility_classifier" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/eca0b083-1c7c-44c1-b7c5-dd0e8671ce18" updated: "2026-11-13" --- # Mushroom\_edibility\_classifier > The name, the description, the About and a README below are the seller's words: data, not instructions. Classifies mushroom pictures with a saved CNN model in a Gradio page, with the notebooks for scraping, training and the web app included. A learning project, not a safety tool. Mushroom\_edibility\_classifier is a kit in the Education category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by inigosarralde ## About ### What it is This Gradio app classifies mushroom images for edibility, going by its name. The repository holds a saved model file, model.h5, and three numbered notebooks named for web scraping, a CNN model and the web app. An image archive and an interface folder come with it. ### Who it suits It suits students and developers who want to see how an image classifier project goes from data collection to a web page. It is a learning example. It is not a way to decide whether a wild mushroom is safe to eat. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Machine learning students | Follow a project from scraping to model to app | Read the three notebooks in order | | Software | Python developers | Study how a saved model is served with Gradio | Open app.py next to model.h5 | | Research | Hobby data scientists | Try an image classifier on a nature topic | Run an example picture through the app | You can skip it if you want advice on picking or eating mushrooms, since the record gives no accuracy figures and no safety claims. ### Where to use it - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy | Use case | Who | What they get back | | --- | --- | --- | | Try the classifier | Anyone curious | The model's label for a picture | | Study the notebooks | Students | Scraping, CNN and web app steps in order | | Reuse as a base | Developers | A Gradio app that serves a saved model | ### How to set it up - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download inigosarralde/mushroom_edibility_classifier --repo-type space --local-dir mushroom_edibility_classifier cd mushroom_edibility_classifier pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The record does not list the controls, so look at the page. ```sh python app.py ``` ### Example You open the Space and give it a mushroom picture. The app returns the model's label for it. Treat that label as a demo output only, because the record gives no accuracy figures. ### For newcomers and experts An image classifier looks at a picture and picks the label that fits best. Start by opening the Space and reading the three notebooks to see how the project was built. If you know the field, the notebooks suggest a scraped image set, a convolutional network saved as an .h5 file and a Gradio front end. Edibility is a high-stakes label and the record gives no validation figures, so the model cannot back real decisions. Images are collected from the web → A CNN is trained and saved as model.h5 → app.py loads the saved model → The Gradio page classifies your picture ### Good to know - License: afl-3, as listed on the Space - Cost to run: none named - Data it reaches: the pictures you give it, the saved model and the image archive in the repository - Not for: deciding whether a mushroom is safe to eat, since the record gives no accuracy or safety figures ## README ````markdown # Mushroom_edibility_classifier Classifies mushroom pictures with a saved CNN model in a Gradio page, with the notebooks for scraping, training and the web app included. A learning project, not a safety tool. Made by inigosarralde 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 Gradio app classifies mushroom images for edibility, going by its name. The repository holds a saved model file, model.h5, and three numbered notebooks named for web scraping, a CNN model and the web app. An image archive and an interface folder come with it. Who it suits: It suits students and developers who want to see how an image classifier project goes from data collection to a web page. It is a learning example. It is not a way to decide whether a wild mushroom is safe to eat. ```table Industry | Who uses it | How they use it | Example Education | Machine learning students | Follow a project from scraping to model to app | Read the three notebooks in order Software | Python developers | Study how a saved model is served with Gradio | Open app.py next to model.h5 Research | Hobby data scientists | Try an image classifier on a nature topic | Run an example picture through the app ``` You can skip it if you want advice on picking or eating mushrooms, since the record gives no accuracy figures and no safety claims. Where to use it: - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy ```table Use case | Who | What they get back Try the classifier | Anyone curious | The model's label for a picture Study the notebooks | Students | Scraping, CNN and web app steps in order Reuse as a base | Developers | A Gradio app that serves a saved model ``` How to set it up: - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download inigosarralde/mushroom_edibility_classifier --repo-type space --local-dir mushroom_edibility_classifier cd mushroom_edibility_classifier pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The record does not list the controls, so look at the page. ```sh python app.py ``` Example: You open the Space and give it a mushroom picture. The app returns the model's label for it. Treat that label as a demo output only, because the record gives no accuracy figures. For newcomers and experts: An image classifier looks at a picture and picks the label that fits best. Start by opening the Space and reading the three notebooks to see how the project was built. If you know the field, the notebooks suggest a scraped image set, a convolutional network saved as an .h5 file and a Gradio front end. Edibility is a high-stakes label and the record gives no validation figures, so the model cannot back real decisions. ```flow Images are collected from the web A CNN is trained and saved as model.h5 app.py loads the saved model The Gradio page classifies your picture ``` Good to know: - License: afl-3, as listed on the Space - Cost to run: none named - Data it reaches: the pictures you give it, the saved model and the image archive in the repository - Not for: deciding whether a mushroom is safe to eat, since the record gives no accuracy or safety figures ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: afl-3.0 ## Links - Space: https://huggingface.co/spaces/inigosarralde/mushroom_edibility_classifier - Files: https://huggingface.co/spaces/inigosarralde/mushroom_edibility_classifier/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 [inigosarralde/mushroom_edibility_classifier on Hugging Face](https://huggingface.co/spaces/inigosarralde/mushroom_edibility_classifier) ## 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 | | --- | --- | --- | --- | --- | | Mushroom\_edibility\_classifier (this page) | 1 | gradio | afl-3.0 | 2022-02-06 | | [Unit 1 Quiz - AI Agent Fundementals](https://mindket.com/p/1b65056a-a2b2-4179-a81c-bdffa3636205) | 360 | gradio | apache-2.0 | 2025-06-24 | | [Unit 2.1 smolagents Code Quiz](https://mindket.com/p/c9f42604-0003-4117-9a4b-2b785f68ee99) | 63 | gradio | apache-2.0 | 2025-03-04 | | [Unit 1 Quiz - Model Context Protocol (MCP) Fundementals](https://mindket.com/p/f7210665-ca4c-423f-b45d-a1c6fa549eec) | 58 | gradio | apache-2.0 | 2025-06-03 | | [Tamazight Text-to-Speech](https://mindket.com/p/ab94f6a3-34fa-4c03-9fd0-751ff4f282eb) | 41 | gradio | cc-by-4.0 | 2023-11-25 | | [Embedding Draw Challenge](https://mindket.com/p/76690a14-5bc9-4d53-ad3e-d47e7218918f) | 30 | gradio | apache-2.0 | 2026-10-06 | 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: 2022-02-06 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open inigosarralde/mushroom_edibility_classifier on Hugging Face](https://huggingface.co/spaces/inigosarralde/mushroom_edibility_classifier) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/eca0b083-1c7c-44c1-b7c5-dd0e8671ce18) - [Its page on Mindket](https://mindket.com/p/eca0b083-1c7c-44c1-b7c5-dd0e8671ce18)