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Cheezam - Detect french cheese on image

CHEEZAM - What the heck is it?

Cheezam is the brainchild of a few data scientists at Prevision.io. Over lunch a few months ago, a debate took place as to which types of cheese were served on a dessert tray. Yep, things get pretty wild at lunches outside of our Paris, France office. Just imagine what these folks are like on the weekend!

From that lunch an idea was born, can machine learning be used to identify a type of cheese? The time has come to officially announce Cheezam, an artificial intelligence app built on the Prevision.io AI Management platform. It was trained on a dataset of french cheeses (here are the labels if you decide to try to replicate the model).

In this limited preview (aka ‘Alpha’ release) the model is currently only able to predict the type of cheese. Future iterations will be built if there is demand. As AI and machine learning experts, we may need to pull in additional experts who are more than just novice cheese lovers.

News

2021/12/27 - New model, Improved

New model with more image on real lighting condition instead of web image. Detection Improved.

2021/12/17 - Wine / Cheese pairings

With all this cheese, that makes us thirsty....Announcing the newest features of Cheezam. Wine Pairings with direct purchasing enabled. Take a photo of your cheese and select the appropriate wine pairing with a single click!To be consumed in moderation of course.

How does it work?

This is a very simple app!

Step 1. Have your iOS or Android phone ready

Step 2. Go to cheezam.fr

Step 3. Click Take photo (take a picture of the cheese!)

Step 4. Test the results

or call the rest API with the code snippets below.

Javascript Node ( Axios ) Python Curl Swift


var formdata = new FormData();
formdata.append("img", fileInput.files[0], "many_cheese_3.jpg");
var requestOptions = {
    method: 'POST',
    body: formdata,
    redirect: 'follow'
};
fetch("https://cheezam.fr/api/cheezam/prediction", requestOptions)
    .then(response => response.text())
    .then(result => console.log(result))
    .catch(error => console.log('error', error));


var axios = require('axios');
var FormData = require('form-data');
var fs = require('fs');
var data = new FormData();
data.append('img', fs.createReadStream('./many_cheese_3.jpg'));
var config = {
    method: 'post',
    url: 'https://cheezam.fr/api/cheezam/prediction',
    headers: { 
    ...data.getHeaders()
    },
    data : data
};
axios(config)
.then(function (response) {
    console.log(JSON.stringify(response.data));
})
.catch(function (error) {
    console.log(error);
});


import requests
url = "https://cheezam.fr/api/cheezam/prediction"
payload={}
files=[
    ('img',('many_cheese_3.jpg',open('./many_cheese_3.jpg','rb'),'image/jpeg'))
]
headers = {}
response = requests.request("POST", url, headers=headers, data=payload, files=files)
print(response.text)


curl --location --request POST 'https://cheezam.fr/api/cheezam/prediction' \
--form 'img=@"./many_cheese_3.jpg"'


import Foundation
#if canImport(FoundationNetworking)
import FoundationNetworking
#endif
var semaphore = DispatchSemaphore (value: 0)
let parameters = [
    [
    "key": "img",
    "src": "./many_cheese_3.jpg",
    "type": "file"
    ]] as [[String : Any]]
let boundary = "Boundary-\(UUID().uuidString)"
var body = ""
var error: Error? = nil
for param in parameters {
    if param["disabled"] == nil {
    let paramName = param["key"]!
    body += "--\(boundary)\r\n"
    body += "Content-Disposition:form-data; name=\"\(paramName)\""
    if param["contentType"] != nil {
        body += "\r\nContent-Type: \(param["contentType"] as! String)"
    }
    let paramType = param["type"] as! String
    if paramType == "text" {
        let paramValue = param["value"] as! String
        body += "\r\n\r\n\(paramValue)\r\n"
    } else {
        let paramSrc = param["src"] as! String
        let fileData = try NSData(contentsOfFile:paramSrc, options:[]) as Data
        let fileContent = String(data: fileData, encoding: .utf8)!
        body += "; filename=\"\(paramSrc)\"\r\n"
        + "Content-Type: \"content-type header\"\r\n\r\n\(fileContent)\r\n"
    }
    }
}
body += "--\(boundary)--\r\n";
let postData = body.data(using: .utf8)
var request = URLRequest(url: URL(string: "https://cheezam.fr/api/cheezam/prediction")!,timeoutInterval: Double.infinity)
request.addValue("multipart/form-data; boundary=\(boundary)", forHTTPHeaderField: "Content-Type")
request.httpMethod = "POST"
request.httpBody = postData
let task = URLSession.shared.dataTask(with: request) { data, response, error in 
    guard let data = data else {
    print(String(describing: error))
    semaphore.signal()
    return
    }
    print(String(data: data, encoding: .utf8)!)
    semaphore.signal()
}
task.resume()
semaphore.wait()

Cheezam - Detect french cheese on image

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