Mindket

Agrishield Ai

Newversion 1 · updated

Overview

Packages a plant disease model and a remedies file in a Docker web app, which by its files looks at plant pictures and suggests treatments. Suits growers, students and developers.

Agrishield Ai is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.

At a glance

You need
  • Docker on Windows, macOS or Linux
  • Python with pip, used here to download the files
  • The Hugging Face hub client, which the first step installs
Runs in
  • A browser: open the Space's page on Hugging Face, with nothing to install
  • A copy on Windows, macOS or Linux, using Docker
Start with
pip install -U huggingface_hub
hf download Vratika7/agrishield-ai --repo-type space --local-dir agrishield-ai
cd agrishield-ai

Platform

WindowsOperating system
macOSOperating system
LinuxOperating system
DockerContainer

About

What it is

This is a Docker Space with a plant disease model, a class list and a remedies file. By those files, it checks a plant picture for a disease and shows a remedy. The source has no description, so that reading comes from the file names. The templates and static folders point to a web page served by app.py.

Who it suits

It suits growers and students who want to try a plant disease checker, and developers who want to see how a trained model is wrapped in a small web app.

IndustryWho uses itHow they use itExample
FarmingSmall growersTry a picture-based check on a sick-looking leafUpload a leaf photo and read the result
EducationAgriculture studentsSee how an image model is paired with remedy notesCompare a result with the remedies file
SoftwareDevelopersStudy a small Docker web app around a Keras modelOpen app.py and remedies.py

Skip it if you need a diagnosis you can rely on, since the source gives no accuracy figures.

Where to use it

  • A browser: open the Space's page on Hugging Face, with nothing to install
  • A copy on Windows, macOS or Linux, using Docker
Use caseWhoWhat they get back
Checking a leaf photoGrowersA likely disease name and remedy, if the page works as its files suggest
Studying the modelStudentsA trained model file and its class list
Reading the codeDevelopersA small web app with templates and a Dockerfile

How to set it up

The online copy needs nothing. For a copy on your own computer, you need the following.

  • Docker on Windows, macOS or Linux
  • Python with pip, used here to download the files
  • The Hugging Face hub client, which the first step installs
  1. Install the Hugging Face hub client.
  2. Download the Space's files into a local folder.
  3. Move into that folder.
  4. Build the image with Docker.
  5. Run the container and publish port 7860.
pip install -U huggingface_hub
hf download Vratika7/agrishield-ai --repo-type space --local-dir agrishield-ai
cd agrishield-ai
docker build -t agrishield-ai .
docker run -it -p 7860:7860 agrishield-ai

How to use it

Online, open the Space's page and follow what it shows. On your own computer, build and run the container as shown above, then open port 7860 in your browser. The source does not describe the page, so follow its labels.

Example

You photograph a leaf that looks unhealthy and upload it to the page. If the app works as its files suggest, it names a likely disease and lists a remedy. The source gives no sample result.

For newcomers and experts

If you are new, try a clear, close photo of one leaf in good light. Treat the answer as a hint and ask a local expert before you treat a crop.

If you know the field, the repository holds a Keras model file, a JSON file of class indices and a remedies module, served from a Docker image on port 7860. The Space runs on cpu-basic hardware at Hugging Face. The source does not list the plant types or classes the model covers.

  1. You upload a plant picture
  2. The model predicts a class
  3. The class index maps to a disease name
  4. The remedies file supplies a treatment

Good to know

The source is short, so supported plants and accuracy are not described.

  • License: mit, as listed on Hugging Face
  • Cost to run: no API key or paid service is named, and the online copy runs on cpu-basic hardware
  • Data it reaches: the pictures you upload, which the online Space handles on Hugging Face's servers, and a local copy downloads the Space's files
  • Not for: the source lists no exclusions, but do not treat its output as expert advice on crop treatment

README and files

README

Agrishield Ai

Packages a plant disease model and a remedies file in a Docker web app, which by its files looks at plant pictures and suggests treatments. Suits growers, students and developers.

Made by Vratika7 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 Docker Space with a plant disease model, a class list and a remedies file. By those files, it checks a plant picture for a disease and shows a remedy. The source has no description, so that reading comes from the file names. The templates and static folders point to a web page served by app.py.

Who it suits: It suits growers and students who want to try a plant disease checker, and developers who want to see how a trained model is wrapped in a small web app.

Industry | Who uses it | How they use it | Example
Farming | Small growers | Try a picture-based check on a sick-looking leaf | Upload a leaf photo and read the result
Education | Agriculture students | See how an image model is paired with remedy notes | Compare a result with the remedies file
Software | Developers | Study a small Docker web app around a Keras model | Open app.py and remedies.py

Skip it if you need a diagnosis you can rely on, since the source gives no accuracy figures.

Where to use it:

  • A browser: open the Space's page on Hugging Face, with nothing to install
  • A copy on Windows, macOS or Linux, using Docker
Use case | Who | What they get back
Checking a leaf photo | Growers | A likely disease name and remedy, if the page works as its files suggest
Studying the model | Students | A trained model file and its class list
Reading the code | Developers | A small web app with templates and a Dockerfile

How to set it up: The online copy needs nothing. For a copy on your own computer, you need the following.

  • Docker on Windows, macOS or Linux
  • Python with pip, used here to download the files
  • The Hugging Face hub client, which the first step installs
  1. Install the Hugging Face hub client.
  2. Download the Space's files into a local folder.
  3. Move into that folder.
  4. Build the image with Docker.
  5. Run the container and publish port 7860.
pip install -U huggingface_hub
hf download Vratika7/agrishield-ai --repo-type space --local-dir agrishield-ai
cd agrishield-ai
docker build -t agrishield-ai .
docker run -it -p 7860:7860 agrishield-ai

How to use it: Online, open the Space's page and follow what it shows. On your own computer, build and run the container as shown above, then open port 7860 in your browser. The source does not describe the page, so follow its labels.

Example: You photograph a leaf that looks unhealthy and upload it to the page. If the app works as its files suggest, it names a likely disease and lists a remedy. The source gives no sample result.

For newcomers and experts: If you are new, try a clear, close photo of one leaf in good light. Treat the answer as a hint and ask a local expert before you treat a crop.

If you know the field, the repository holds a Keras model file, a JSON file of class indices and a remedies module, served from a Docker image on port 7860. The Space runs on cpu-basic hardware at Hugging Face. The source does not list the plant types or classes the model covers.

You upload a plant picture
The model predicts a class
The class index maps to a disease name
The remedies file supplies a treatment

Good to know: The source is short, so supported plants and accuracy are not described.

  • License: mit, as listed on Hugging Face
  • Cost to run: no API key or paid service is named, and the online copy runs on cpu-basic hardware
  • Data it reaches: the pictures you upload, which the online Space handles on Hugging Face's servers, and a local copy downloads the Space's files
  • Not for: the source lists no exclusions, but do not treat its output as expert advice on crop treatment

On Hugging Face, 13 November 2026

  • Likes: 1
  • Built with: docker
  • License: mit

Links

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.

Files
README.md  4 KB

1 file, 4 KB, downloaded as a zip.

What’s new

  • Version 1 · 2026-11-12

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Numbers as Mindket last read them, on 2026-11-12.

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