Mateverse APIs Clients

Mateverse APIs Clients(Python, NodeJS)

https://www.mateverse.com/ http://www.matelabs.in

Introduction

The Mateverse APIs call will return you the 'confidence score'/'accuracy score' of your 'image files'/text/'text files'/'csv values' that are trained on a particular model of your choice. When you make a prediction through the API, you pass in the 'image file'/url or 'text file'/text or 'csv values' and tell it which model to use, authenticating the API call with your api_secret key and model_id

Usage

In request.py you need to pass in the following parameters to make the API work:

api_secret key

The api_secret key serves as the authentication key to the API call. Just copy-paste your api_secret key in request.py and you are good to go.

model_id

The model_id is to tell the API which model to use for your prediction.

file_paths - Path to your images/text files

You need to give the path to the images/text files which will go inside a list. You can pass in the path to as much images/text files as you want.

Examples:

If using an images model
['images/image1.jpg', 'images/image2.jpg']

If using a text model
['text/text1.txt', 'text/text2.txt']

image_url(Optional) - Url to a publicly accessible image

Provide an url of a publicly accessible image. This is an optional parameter.

Example:
https://c1.staticflickr.com/1/155/354864230_a8fe1fe864.jpg

text_sample(Optional) - Sample text. Can be a word or a sentence or a paragraph

Provide a text sample which you want to use for the prediction. This is an optional parameter.

Example:
"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque finibus neque tortor, non lobortis nibh tempor id"

values - When you use a csv model

Provide a valid json string containing all column names and column values.

Example:
"[{\"column_value\": ***, \"column_name\": \"******\"}, {\"column_value\": ***, \"column_name\": \"******\"}, {\"column_value\": ***, \"column_name\": \"******\"}, {\"column_value\": ***, \"column_name\": \"******\"}]"

Run

Take predictions from an images model:
python request_images.py

Take predictions from a text model:
python request_text.py

Take predictions from a csv model:
python request_csv.py

Response

Response will be a JSON object, easily parsable in all programming languages.

Response from an images model:
{
   "status":"success",
   "message":"Predictions",
   "predictions":[
      {
         "sample":"image.jpeg",
         "predictions":[
            {
               "predicted_score":"0.962045",
               "predicted_label":"predicted label 1"
            },
            {
               "predicted_score":"0.037955",
               "predicted_label":"predicted label 2"
            }
         ]
      },
      {
         "sample":"https://www.example.com/image.jpg",
         "predictions":[
            {
               "predicted_score":"0.932396",
               "predicted_label":"predicted label 1"
            },
            {
               "predicted_score":"0.067604",
               "predicted_label":"pfredicted label 2"
            }
         ]
      }
   ]
}

Response from a text model:
[
  {
    "status": "success",
    "message": "Predictions.",
    "predictions": [
      {
        "sample": "Lorem ipsum dolor sit amet, consectetur adipiscing elit.",
        "label": "predicted label(class) or predicted number(integer/float)"
      }
    ]
  }
]

Response from a csv model:
{
   "status":"success",
   "message":[],
   "predictions":[
      {
         "sample":"[{\"column_value\": ***, \"column_name\": \"******\"}, {\"column_value\": ***, \"column_name\": \"******\"}, {\"column_value\": ***, \"column_name\": \"******\"}, {\"column_value\": ***, \"column_name\": \"******\"}]",
         "label":"predicted label(class) or predicted number(integer/float)"
      }
   ]
}

Licensing

MIT License