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Python

API Call Type

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Select An Model Type

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Next

Enter Image File Path

Enter class names

Independent Variable Name

Enter Input Value

Model Data Path

Input Sentence

Model Name

Dependent Variable Name

Model Name

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from clevrml import

Image_Model

import os

key = os.environ[ "API-KEY" ]

model = Image_Model( )

model.build_model(

       api_key=key,

       class_names=

       example_folders=

       model_name=

)

<Pending Input>,

<Pending Input>,

<Pending Input>,

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from clevrml import

Image_Model

import os

key = os.environ[ "API-KEY" ]

model = Image_Model( )

model.edit_model(

       api_key=key,

       class_names=

       example_folders=

       model_name=

)

<Pending Input>,

<Pending Input>,

<Pending Input>,

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from clevrml import

Image_Model

import os

key = os.environ[ "API-KEY" ]

model = Image_Model( )

model.predict(

       api_key=key,

       image_file_path=

       model_name=

)

<Pending Input>,

<Pending Input>,

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from clevrml import

Forecasting_Model

import os

key = os.environ[ "API-KEY" ]

model = Forecasting_Model( )

model.build_model(

       api_key=key,

       independent=

       dependent=

       reference_path=

       model_name=

)

<Pending Input>,

<Pending Input>,

<Pending Input>,

<Pending Input>,

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from clevrml import

Forecasting_Model

import os

key = os.environ[ "API-KEY" ]

model = Forecasting_Model( )

model.predict(

       api_key=key,

       input_value=

       model_name=

)

<Pending Input>,

<Pending Input>,

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from clevrml import

Forecasting_Model

import os

key = os.environ[ "API-KEY" ]

model = Forecasting_Model( )

model.edit_model(

       api_key=key,

       independent=

       dependent=

       reference_path=

       model_name=

)

<Pending Input>,

<Pending Input>,

<Pending Input>,

<Pending Input>,

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from clevrml import

Text_Model

import os

key = os.environ[ "API-KEY" ]

model = Text_Model( )

model.build_model(

       api_key=key,

       references_path=

       model_name=

)

<Pending Input>,

<Pending Input>,

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from clevrml import

Text_Model

import os

key = os.environ[ "API-KEY" ]

model = Text_Model( )

model.edit_model(

       api_key=key,

       references_path=

       model_name=

)

<Pending Input>,

<Pending Input>,

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from clevrml import

Text_Model

import os

key = os.environ[ "API-KEY" ]

model = Text_Model( )

model.predict(

       api_key=key,

       input_sentence=

       model_name=

)

<Pending Input>,

<Pending Input>,

Enter folder names

CSV File Path

Model Name

Copied!

Model Name

Coming soon: Support for Node.js, cURL and JSON.

API Call Type: The API you want to use

Select Model Type: The model type you want to build, edit or the type of model you have made that you want to make a prediction for.

Image File Path: The directory of where your image is on your computer.

Model Name: The name of the model that you want to either save or use for prediction.

Independent Variable: The data column in your CSV file that isn't affected by any other data (ie: Amount of customers vs. sales, the number of customers for a given day are unaffected by the number of sales that have occurred)

CSV File Path: The directory of where your .csv file is on your computer.

Folder Names: The names of the folders that contain the images for your model.

Folder Names: The names of the folders that contain the images for your model.

Input Sentence: The sentence that you want a prediction for.

Model Data Path: the name of the .txt file containing sentences or text for your model.

Dependent Variable: The data column in your CSV file that is affected by any other data (ie: Amount of customers vs. sales, the number of sales for a given day are affected by the number of customers that have visited)