Input
POST https://gateway.appypie.com/virtual-tryon/v1/virtual-tryon HTTP/1.1

Content-Type: application/json
Cache-Control: no-cache

{
    "background": "https://storage.googleapis.com/imagesai.appypie.com/testing/00034_00.jpg",
    "garm_img": "https://storage.googleapis.com/imagesai.appypie.com/testing/04469_00.jpg",
    "garment_des": "half sleeve black tshirt",
    "garment_category": "lower_body",
    "is_checked": true,
    "is_checked_crop": false,
    "denoise_steps": 30,
    "seed": 42
}
import urllib.request, json

try:
    url = "https://gateway.appypie.com/virtual-tryon/v1/virtual-tryon"

    hdr ={
    # Request headers
    'Content-Type': 'application/json',
    'Cache-Control': 'no-cache',
    }

    # Request body
    data =  
    data = json.dumps(data)
    req = urllib.request.Request(url, headers=hdr, data = bytes(data.encode("utf-8")))

    req.get_method = lambda: 'POST'
    response = urllib.request.urlopen(req)
    print(response.getcode())
    print(response.read())
    except Exception as e:
    print(e)
// Request body
const body = {
    "background": "https://storage.googleapis.com/imagesai.appypie.com/testing/00034_00.jpg",
    "garm_img": "https://storage.googleapis.com/imagesai.appypie.com/testing/04469_00.jpg",
    "garment_des": "half sleeve black tshirt",
    "garment_category": "lower_body",
    "is_checked": true,
    "is_checked_crop": false,
    "denoise_steps": 30,
    "seed": 42
};

fetch('https://gateway.appypie.com/virtual-tryon/v1/virtual-tryon', {
        method: 'POST',
        body: JSON.stringify(body),
        // Request headers
        headers: {
            'Content-Type': 'application/json',
            'Cache-Control': 'no-cache',}
    })
    .then(response => {
        console.log(response.status);
        console.log(response.text());
    })
    .catch(err => console.error(err));
curl -v -X POST "https://gateway.appypie.com/virtual-tryon/v1/virtual-tryon" -H "Content-Type: application/json" -H "Cache-Control: no-cache" --data-raw "{
    \"background\": \"https://storage.googleapis.com/imagesai.appypie.com/testing/00034_00.jpg\",
    \"garm_img\": \"https://storage.googleapis.com/imagesai.appypie.com/testing/04469_00.jpg\",
    \"garment_des\": \"half sleeve black tshirt\",
    \"garment_category\": \"lower_body\",
    \"is_checked\": true,
    \"is_checked_crop\": false,
    \"denoise_steps\": 30,
    \"seed\": 42
}"
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.net.HttpURLConnection;
import java.net.URL;
import java.net.URLEncoder;
import java.util.HashMap;
import java.util.Map;
import java.io.UnsupportedEncodingException;
import java.io.DataInputStream;
import java.io.InputStream;
import java.io.FileInputStream;

public class HelloWorld {

  public static void main(String[] args) {
    try {
        String urlString = "https://gateway.appypie.com/virtual-tryon/v1/virtual-tryon";
        URL url = new URL(urlString);
        HttpURLConnection connection = (HttpURLConnection) url.openConnection();

        //Request headers
    connection.setRequestProperty("Content-Type", "application/json");
    
    connection.setRequestProperty("Cache-Control", "no-cache");
    
        connection.setRequestMethod("POST");

        // Request body
        connection.setDoOutput(true);
        connection
            .getOutputStream()
            .write(
             "{ \"background\": \"https://storage.googleapis.com/imagesai.appypie.com/testing/00034_00.jpg\", \"garm_img\": \"https://storage.googleapis.com/imagesai.appypie.com/testing/04469_00.jpg\", \"garment_des\": \"half sleeve black tshirt\", \"garment_category\": \"lower_body\", \"is_checked\": true, \"is_checked_crop\": false, \"denoise_steps\": 30, \"seed\": 42 }".getBytes()
             );
    
        int status = connection.getResponseCode();
        System.out.println(status);

        BufferedReader in = new BufferedReader(
            new InputStreamReader(connection.getInputStream())
        );
        String inputLine;
        StringBuffer content = new StringBuffer();
        while ((inputLine = in.readLine()) != null) {
            content.append(inputLine);
        }
        in.close();
        System.out.println(content);

        connection.disconnect();
    } catch (Exception ex) {
      System.out.print("exception:" + ex.getMessage());
    }
  }
}

$url = "https://gateway.appypie.com/virtual-tryon/v1/virtual-tryon";
$curl = curl_init($url);

curl_setopt($curl, CURLOPT_CUSTOMREQUEST, "POST");
curl_setopt($curl, CURLOPT_URL, $url);
curl_setopt($curl, CURLOPT_RETURNTRANSFER, true);

# Request headers
$headers = array(
    'Content-Type: application/json',
    'Cache-Control: no-cache',);
curl_setopt($curl, CURLOPT_HTTPHEADER, $headers);

# Request body
$request_body = '{
    "background": "https://storage.googleapis.com/imagesai.appypie.com/testing/00034_00.jpg",
    "garm_img": "https://storage.googleapis.com/imagesai.appypie.com/testing/04469_00.jpg",
    "garment_des": "half sleeve black tshirt",
    "garment_category": "lower_body",
    "is_checked": true,
    "is_checked_crop": false,
    "denoise_steps": 30,
    "seed": 42
}';
curl_setopt($curl, CURLOPT_POSTFIELDS, $request_body);

$resp = curl_exec($curl);
curl_close($curl);
var_dump($resp);
Output
virtual-tryon

Virtual Try-On: Experience the Future of Shopping with Appy Pie's Virtual Try-On

meta

Shopping has taken a giant leap forward with technology, and Appy Pie’s Virtual Try-On stands at the forefront of this transformation. Offering an innovative AI clothes changer, this tool allows shoppers to explore various outfits from the comfort of their homes. The virtual try-on feature provides a highly realistic and engaging experience, enabling users to visualize different styles effortlessly. The AI dress changer makes it incredibly simple to try out different looks, giving you the confidence to find the perfect fit without any hassle. By using the free cloth AI changer, users can experiment with multiple outfits, enhancing their shopping journey and saving time.

This advanced AI clothes changer free option opens up endless possibilities, allowing users to swap outfits, change clothes, and get creative with their style—all with just a few clicks. The cloth change AI technology ensures a lifelike experience, giving users the chance to see themselves in any outfit of their choice. It offers a seamless, interactive shopping journey that breaks the limitations of traditional retail. By integrating Appy Pie’s Virtual Try-On API, businesses can elevate their e-commerce platforms, providing customers with an interactive, personalized, and futuristic shopping experience that truly sets them apart.

How to Use Appy Pie’s Virtual Try-On API for Enhancing Your E-Commerce Shopping Experience?

To use Appy Pie’s Virtual Try-On API, follow these simple steps to integrate this innovative technology into your platform:

Sign Up and Access the API

Begin by signing up on Appy Pie Endpoint’s platform. Once registered, navigate to the Virtual Try-On API section and obtain your unique API key. This key is crucial for accessing the API's features.

Integrate the API

Using the provided documentation, integrate the Virtual Try-On API into your website or mobile app. The API can be integrated into various programming languages and frameworks, making it versatile for different platforms.

Upload or Capture User Image

Allow users to upload a clear image of themselves or use a live camera feed. This image will be used for the AI clothes changer feature, enabling the virtual try-on experience.

Select Outfits

Provide a selection of outfits or designs that users can choose from. The Virtual Try-On API uses AI technology to adapt the chosen outfit onto the user’s image seamlessly, creating a realistic try-on experience.

Preview and Adjust

Users can preview the outfits in real-time and make adjustments as needed. The cloth change AI technology ensures the outfit fits accurately on the user's image.

Save or Share

Offer options for users to save their try-on images or share them directly on social media, making their shopping experience more interactive and engaging.

Use Cases of Appy Pie’s Virtual Try-On API

E-Commerce Fashion Stores

Integrating the Virtual Try-On API allows fashion e-commerce stores to offer customers a personalized shopping experience. Shoppers can visualize themselves in different outfits, accessories, or even makeup products, making it easier to decide on purchases without physically trying on items. This feature significantly reduces return rates and boosts customer confidence in online shopping.

Jewelry and Accessories Retailers

Jewelry and accessory retailers can use the API to enable customers to virtually try on rings, necklaces, bracelets, and more. By allowing users to see how these items look on them before purchasing, the shopping experience becomes more interactive and engaging, leading to increased sales and customer satisfaction.

Eyewear Brands

Eyewear brands can leverage the Virtual Try-On API to let customers try different frames, styles, and colors of glasses. This makes the selection process more convenient and helps customers find the perfect pair that complements their face shape, ultimately enhancing the online eyewear shopping experience.

Footwear Brands

Footwear brands can use this technology to provide a virtual fitting experience. Shoppers can see how different shoes look on their feet, whether it’s sneakers, boots, or heels, without having to visit a physical store. This immersive experience helps customers make informed decisions and fosters a stronger connection with the brand.

Beauty and Cosmetics Brands

Beauty brands can integrate the Virtual Try-On API to let customers experiment with different makeup products, such as lipsticks, eyeshadows, and foundation shades, in real-time. This makes the shopping experience more interactive and allows customers to find products that best match their preferences and skin tones.

Retailers Offering Custom Apparel

Businesses offering custom apparel can use the Virtual Try-On API to let customers visualize their personalized designs, such as T-shirts, hoodies, or jackets. It helps customers get a better sense of how their custom designs will look when worn, reducing uncertainty and enhancing their satisfaction.

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