const Clarifai = require('clarifai');
// Instantiate a new Clarifai app by passing in your API key.
const app = new Clarifai.App({apiKey: 'YOUR_API_KEY'});
// Predict the contents of an image by passing in a URL.
app.models.predict(Clarifai.GENERAL_MODEL, 'https://samples.clarifai.com/metro-north.jpg')
.then(response => {
console.log(response);
})
.catch(err => {
console.log(err);
});
from clarifai.rest import ClarifaiApp
# Instantiate a new Clarifai app by passing in your API key.
app = ClarifaiApp(api_key='YOUR_API_KEY')
# Choose one of the public models.
model = app.public_models.general_model
# Predict the contents of an image by passing in a URL.
response = model.predict_by_url(url='https://samples.clarifai.com/metro-north.jpg')
package yourPackageName;
import clarifai2.api.ClarifaiBuilder;
import clarifai2.api.ClarifaiClient;
import clarifai2.dto.input.ClarifaiInput;
import clarifai2.dto.model.output.ClarifaiOutput;
import clarifai2.dto.prediction.Concept;
import java.util.List;
public class QuickStartPredict {
public static void main(String[] args) {
ClarifaiClient client = new ClarifaiBuilder("YOUR_API_KEY")
.buildSync();
final List<ClarifaiOutput<Concept>> response =
// You can also do client.getModelByID("id") to get your custom model
client.getDefaultModels().generalModel()
.predict()
.withInputs(ClarifaiInput.forImage("https://samples.clarifai.com/metro-north.jpg"))
.executeSync()
.get();
}
}
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.API.Responses;
using Clarifai.DTOs.Inputs;
using Clarifai.DTOs.Models.Outputs;
using Clarifai.DTOs.Predictions;
namespace YourNamespace
{
public class YourClassName
{
public static async Task Main()
{
var client = new ClarifaiClient("YOUR_API_KEY");
ClarifaiResponse<ClarifaiOutput<Concept>> response =
await client.PublicModels.GeneralModel
.Predict(new ClarifaiURLImage("https://samples.clarifai.com/metro-north.jpg"))
.ExecuteAsync();
}
}
}
ClarifaiApp *app = [[ClarifaiApp alloc] initWithApiKey:@"YOUR_API_KEY"];
ClarifaiImage *image = [[ClarifaiImage alloc] initWithURL:@"https://samples.clarifai.com/metro-north.jpg"];
[app getModelByName:@"general-v1.3" completion:^(ClarifaiModel *model, NSError *error) {
[model predictOnImages:@[image]
completion:^(NSArray<ClarifaiSearchResult *> *outputs, NSError *error) {
NSLog(@"outputs: %@", outputs);
}];
}];
use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Models\VideoModel;
$client = new ClarifaiClient('YOUR_API_KEY');
/** @var VideoModel $model */
$model = $client->publicModels()->generalModel();
$response = $model->predict(
new ClarifaiURLImage('https://samples.clarifai.com/metro-north.jpg'))
->executeSync();
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '
{
"inputs": [
{
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
}
]
}'\
https://api.clarifai.com/v2/models/aaa03c23b3724a16a56b629203edc62c/outputs
const Clarifai = require('clarifai');
// Instantiate a new Clarifai app by passing in your API key.
const app = new Clarifai.App({apiKey: 'YOUR_API_KEY'});
// Predict the contents of an image by passing in a URL.
app.models.predict(Clarifai.GENERAL_MODEL, 'https://samples.clarifai.com/metro-north.jpg')
.then(response => {
console.log(response);
})
.catch(err => {
console.log(err);
});
from clarifai.rest import ClarifaiApp
# Instantiate a new Clarifai app by passing in your API key.
app = ClarifaiApp(api_key='YOUR_API_KEY')
# Choose one of the public models.
model = app.public_models.general_model
# Predict the contents of an image by passing in a URL.
response = model.predict_by_url(url='https://samples.clarifai.com/metro-north.jpg')
package yourPackageName;
import clarifai2.api.ClarifaiBuilder;
import clarifai2.api.ClarifaiClient;
import clarifai2.dto.input.ClarifaiInput;
import clarifai2.dto.model.output.ClarifaiOutput;
import clarifai2.dto.prediction.Concept;
import java.util.List;
public class QuickStartPredict {
public static void main(String[] args) {
ClarifaiClient client = new ClarifaiBuilder("YOUR_API_KEY")
.buildSync();
final List<ClarifaiOutput<Concept>> response =
// You can also do client.getModelByID("id") to get your custom model
client.getDefaultModels().generalModel()
.predict()
.withInputs(ClarifaiInput.forImage("https://samples.clarifai.com/metro-north.jpg"))
.executeSync()
.get();
}
}
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.API.Responses;
using Clarifai.DTOs.Inputs;
using Clarifai.DTOs.Models.Outputs;
using Clarifai.DTOs.Predictions;
namespace YourNamespace
{
public class YourClassName
{
public static async Task Main()
{
var client = new ClarifaiClient("YOUR_API_KEY");
ClarifaiResponse<ClarifaiOutput<Concept>> response =
await client.PublicModels.GeneralModel
.Predict(new ClarifaiURLImage("https://samples.clarifai.com/metro-north.jpg"))
.ExecuteAsync();
}
}
}
ClarifaiApp *app = [[ClarifaiApp alloc] initWithApiKey:@"YOUR_API_KEY"];
ClarifaiImage *image = [[ClarifaiImage alloc] initWithURL:@"https://samples.clarifai.com/metro-north.jpg"];
[app getModelByName:@"general-v1.3" completion:^(ClarifaiModel *model, NSError *error) {
[model predictOnImages:@[image]
completion:^(NSArray<ClarifaiSearchResult *> *outputs, NSError *error) {
NSLog(@"outputs: %@", outputs);
}];
}];
use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Models\VideoModel;
$client = new ClarifaiClient('YOUR_API_KEY');
/** @var VideoModel $model */
$model = $client->publicModels()->generalModel();
$response = $model->predict(
new ClarifaiURLImage('https://samples.clarifai.com/metro-north.jpg'))
->executeSync();
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '
{
"inputs": [
{
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
}
]
}'\
https://api.clarifai.com/v2/models/aaa03c23b3724a16a56b629203edc62c/outputs
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "b8b2d1188ed141e092df4a0989d5d8f9",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T17:28:36Z",
"model": {
"name": "travel-v1.0",
"id": "eee28c313d69466f836ab83287a54ed9",
"created_at": "2016-09-17T22:18:59Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "d2ffbf9730fd41fea79063270847be82",
"created_at": "2016-09-17T22:18:59Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "b8b2d1188ed141e092df4a0989d5d8f9",
"data": {
"image": {
"url": "https://clarifai.com/cms-assets/20180308185817/travel.jpg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_tbXzfXXz",
"name": "Outdoor Pool",
"app_id": null,
"value": 0.95201385
},
{
"id": "ai_CtFL0TP6",
"name": "Wellness & Spa",
"app_id": null,
"value": 0.5191682
},
{
"id": "ai_XB2b9m4v",
"name": "Summer",
"app_id": null,
"value": 0.49868092
},
{
"id": "ai_MbpTFJxj",
"name": "Garden",
"app_id": null,
"value": 0.47994247
},
{
"id": "ai_8v2Tm2Gl",
"name": "Hot Tub",
"app_id": null,
"value": 0.38965574
},
{
"id": "ai_q0SJrBZW",
"name": "Water Park",
"app_id": null,
"value": 0.3072436
},
{
"id": "ai_TCZdTr6B",
"name": "Yoga",
"app_id": null,
"value": 0.28053865
},
{
"id": "ai_zTxfQQKH",
"name": "Kids Area",
"app_id": null,
"value": 0.27112955
},
{
"id": "ai_T4GN2Cdl",
"name": "Water Sports",
"app_id": null,
"value": 0.19529618
},
{
"id": "ai_sf6B79BS",
"name": "Sports & Activities",
"app_id": null,
"value": 0.17493547
},
{
"id": "ai_CVQVtMzc",
"name": "Nighttime",
"app_id": null,
"value": 0.10337732
},
{
"id": "ai_3j6nNvsH",
"name": "Casino",
"app_id": null,
"value": 0.09855646
},
{
"id": "ai_lt2nKTB2",
"name": "Terrace",
"app_id": null,
"value": 0.097663336
},
{
"id": "ai_7Wn1cVgv",
"name": "Indoor Swimming Pool",
"app_id": null,
"value": 0.09621443
},
{
"id": "ai_hmSzCZd5",
"name": "Daytime",
"app_id": null,
"value": 0.07237845
},
{
"id": "ai_J2x9qKdS",
"name": "Animals",
"app_id": null,
"value": 0.06246137
},
{
"id": "ai_m7PCbqbx",
"name": "Golf Course",
"app_id": null,
"value": 0.05252952
},
{
"id": "ai_2jkxl4Fz",
"name": "Spring",
"app_id": null,
"value": 0.04537405
},
{
"id": "ai_dXWGf1bz",
"name": "Building",
"app_id": null,
"value": 0.044785205
},
{
"id": "ai_N09hW8gK",
"name": "Massage",
"app_id": null,
"value": 0.043336816
}
]
}
}
]
}Go ahead and click the 'Run it!' button above. That will make an API call to predict the concepts inside the sample image.
The response will contain concept names along with the probabilities that they are inside the image.
After you have clicked the button, explore below to learn all about our other API features.
Go ahead and click the 'Run it!' button above. That will make an API call to predict the concepts inside the sample image.
Access your applications, train models in our Explorer, and view your account info here.
Take me there!
