Website style replicator
Methods, systems, and storage media for replicating a website. The method can include receiving a source website and determining a plurality of source parameters associated with the source website. The method can include extracting the source parameters associated with the source website. The method can comprise determining destination parameters associated with the destination website. The method can also include training a machine learning model. The machine learning model can be trained to determine model parameters associated with the source website. The machine model can also be trained to generate a set of resultant parameters based on a comparison between source parameters and model parameters and match a resultant parameter to an associated destination parameter. The method can comprise replacing the associated destination parameter with the resultant parameter to generate the replicated source website.
1 . A computer-implemented method for replicating a website, the method comprising:
receiving a destination website;
receiving a source website;
determining a plurality of source parameters associated with the source website;
extracting the plurality of source parameters associated with the source website;
determining a plurality of destination parameters associated with the destination website;
training a machine learning model to:
determine a plurality of model parameters associated with the source website;
generate a plurality of resultant parameters based on a comparison between the source parameters and model parameters;
match a resultant parameter of the plurality of resultant parameters to a destination parameter of the plurality of destination parameters; and
in response to matching the resultant parameters with an associated destination style parameter, replacing the associated destination parameter with the resultant parameter.
2 . The computer-implemented method of claim 1 , wherein training the machine model to generate a plurality of resultant parameters comprises predicting the model parameters based on stored data.
3 . The computer-implemented method of claim 1 , further comprising receiving a set of supplemental style parameters.
4 . The computer-implemented method of claim 3 , further comprising updating the plurality of resultant parameters with the set of supplemental parameters.
5 . The computer-implemented method of claim 3 , further comprising updating the machine learning model based on the set of supplemental style parameters and the plurality of resultant parameters.
6 . The computer-implemented method of claim 3 , further comprising storing the set of supplemental style parameters and the plurality of resultant parameters.
7 . The computer-implemented method of claim 1 , wherein extracting the plurality of source parameters comprises identifying: primary source data, secondary source data, and tertiary source data.
8 . The computer-implemented method of claim 1 , further comprising exporting the plurality of resultant parameters to the destination website.
9 . The computer-implemented method of claim 1 , wherein determining the plurality of source parameters associated with the source website comprises parsing identifiers associated with website style parameters.
10 . A system configured for replicating a website, the system comprising:
one or more hardware processors configured by machine-readable instructions to:
receive a destination website;
receive a source website;
determine a plurality of source parameters associated with the source website;
identify a primary source data, secondary source data, and tertiary source data from the plurality of source parameters;
determine a plurality of destination parameters associated with the destination website;
train a machine learning model to:
predict a plurality of model parameters associated with the source website;
generate a plurality of resultant parameters based on a comparison between the plurality of source parameters and the plurality of destination parameters;
match a resultant parameter of the plurality of resultant parameters to a destination parameter of the plurality of destination parameters;
in response to matching the resultant parameters with an associated destination parameter, replace the associated destination style parameter with the resultant parameter; and
generate a simulation of the destination website based on the resultant parameters.
11 . The system of claim 10 , wherein the processors are further configured to receive a set of supplemental parameters.
12 . The system of claim 11 , wherein the processors are further configured to update the plurality of resultant parameters with the set of supplemental parameters.
13 . The system of claim 11 , wherein the processors are further configured to export the plurality of resultant parameters to the destination website.
14 . The system of claim 11 , wherein the processors are configured to determine the plurality of source parameters associated with the source website by parsing identifiers associated with website style parameters.
15 . The system of claim 11 , wherein the processors are further configured to update the machine learning model based on the supplemental parameters and the resultant parameters.
16 . A non-transient computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method for replicating a website, the method comprising:
receiving a destination website;
receiving a source website;
determining a plurality of source parameters associated with the source website;
extracting the plurality of source parameters associated with the source;
determining a plurality of destination style parameters associated with the destination website;
training a machine learning model to:
determine a plurality of model parameters associated with the source website;
generate a plurality of resultant parameters based on a comparison between the source parameters and model parameters;
match a resultant parameter of the plurality of resultant parameters to a destination parameter of the plurality of destination style parameters; and
in response to matching the resultant parameters with an associated destination style parameter, replacing the associated destination style parameter with the resultant parameter.
17 . The computer-implemented instructions of claim 16 , wherein the training the machine model to generate the plurality of resultant parameters comprises predicting the model parameters based on stored data.
18 . The computer-implemented instructions of claim 16 , further comprising receiving a set of supplemental parameters.
19 . The computer-implemented instructions of claim 18 , further comprising updating the set of resultant parameters with the plurality of supplemental parameters.
20 . The computer-implemented instructions of claim 16 , wherein extracting the plurality of source parameters comprises identifying: primary source data, secondary source data, and tertiary source data.