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https://github.com/lunchb0ne/neuralcodegenerator

Code for C code generator
https://github.com/lunchb0ne/neuralcodegenerator

compiler lexer nlp parser similarity spacy-nlp

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Code for C code generator

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README 

        
# NeuralCodeGenerator



This is a small project that uses a opinionated pseudocode and auugments it with Natural Language Processing to allow users to use more keywords that are similar to the ones already in use.



```py

start

assign ( num int )

assign ( c int )

allot ( i int )

define ( flag int )

assign flag to 0

display "Enter the value to be checked "

read (num int)

for i from 2 to num

repetition

c = num%i

if c==0

then

assign flag to 1

endif

endfor

if flag==1

then

print "The value "

print (num int)

print " is not prime"

else

print "The value "

print (num int)

print " is prime"

endif

end

```



It produces the C code as the following



```c

#include



void main()

{

    int num;

    int c;

    int i;

    int flag;

    flag=0;

    printf("Enter the value to be checked ");

    scanf("%d",&num);

    for(i = 2 ; i <= num ; i++)

    {

        c = num%i;

        if(c == 0)

        {

            flag=1;

        }

    }

    if(flag == 1)

    {

        printf("The value ");

        printf("%d",num);

        printf(" is not prime");

    }

    else

    {

        printf("The value ");

        printf("%d",num);

        printf(" is prime");

    }

}

```



## NLP Techniques Used 



Pseudocode can vary between users, and to incorporate Natural Language and make translation easier, we make use of Similarity metrics. We use the Spacy module to check Similarity.

Our model uses a very limited set of keywords to demonstrate the basic functionality.

A user is free to use other words that correspond to a similar meaning to a word present in the dictionary, and it is then automatically mapped to perform the same functionality as illustrated below:



![Similarity with existing keywords](./doc/sim.png)



For example, user may use the word "whenever" instead of "if" and "during" instead of "while", etc.

This increases the usability as it is able to use NLP techniques to extract the meaning behind the word.



# Installation



Simply execute the following after cloning the git repository to a suitable directory.



```sh

pip install -r requirements.txt

```



The script tries to download the `en_web_core_lg` model for spacy, this model uses ~800MB of memory, so please make sure that you have enough beforehand.



Now you can finally use opyrator environment to test out the converter.



# Running the project



After all the install steps, you should launch the UI using



```sh

opyrator launch-ui converterfunc:C_Code_Generator

```



Doing so would prompt you to launch a beautiful UI allowing you to interactively test the converter.

![ui](./doc/ui.png)



You can provide it some input in the message box.

![message](./doc/input.png)



Then you execute it, be wary that the execution times will depend on the length of the code and the performace of the host computer as it heavily relies on finding vector similarity.

![output](./doc/c_code.png)



Please note that the project uses a compiled binary from the [Astyle Project](http://astyle.sourceforge.net/), and hence it's included in the source.