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nanonets.ImagePredictApi

All URIs are relative to https://app.nanonet.com/api/v2

Method HTTP request Description
image_categorization_label_file_post POST /ImageCategorization/LabelFile/ Prediction for image File
image_categorization_label_urls_post2 POST /ImageCategorization/LabelUrls/ Prediction for image URLs

image_categorization_label_file_post

image_categorization_label_file_post(model_id, file)

Prediction for image File

Use the model to predict which one of the categories an image (given an image file) belongs to.

Example

from __future__ import print_function
import time
import nanonets
from nanonets.rest import ApiException
from pprint import pprint
# Configure HTTP basic authorization: ApiKey
configuration = nanonets.Configuration()
configuration.username = 'YOUR_USERNAME'
configuration.password = 'YOUR_PASSWORD'

# create an instance of the API class
api_instance = nanonets.ImagePredictApi(nanonets.ApiClient(configuration))
model_id = 'model_id_example' # str | 
file = 'file_example' # file | 

try:
    # Prediction for image File
    api_instance.image_categorization_label_file_post(model_id, file)
except ApiException as e:
    print("Exception when calling ImagePredictApi->image_categorization_label_file_post: %s\n" % e)

Parameters

Name Type Description Notes
model_id str
file file

Return type

void (empty response body)

Authorization

ApiKey

HTTP request headers

  • Content-Type: multipart/form-data
  • Accept: application/json

[Back to top] [Back to API list] [Back to Model list] [Back to README]

image_categorization_label_urls_post2

image_categorization_label_urls_post2(model_id, urls)

Prediction for image URLs

Use the model to predict which one of the categories an image (given the image url) belongs to. You can specify multiple image urls.

Example

from __future__ import print_function
import time
import nanonets
from nanonets.rest import ApiException
from pprint import pprint
# Configure HTTP basic authorization: ApiKey
configuration = nanonets.Configuration()
configuration.username = 'YOUR_USERNAME'
configuration.password = 'YOUR_PASSWORD'

# create an instance of the API class
api_instance = nanonets.ImagePredictApi(nanonets.ApiClient(configuration))
model_id = 'model_id_example' # str | 
urls = 'urls_example' # str | 

try:
    # Prediction for image URLs
    api_instance.image_categorization_label_urls_post2(model_id, urls)
except ApiException as e:
    print("Exception when calling ImagePredictApi->image_categorization_label_urls_post2: %s\n" % e)

Parameters

Name Type Description Notes
model_id str
urls str

Return type

void (empty response body)

Authorization

ApiKey

HTTP request headers

  • Content-Type: multipart/form-data
  • Accept: application/json

[Back to top] [Back to API list] [Back to Model list] [Back to README]