Have you ever written a text (email, message, etc) and you feel your tone seems too casual and your message might be misinterpreted, you want to change the tone of your text but have no idea how? As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership. -Amit Ray In this article, we are going to build a Neural Network powered Artificial Intelligence Application that enables you to transfer the tone of your text from , , , , we will also host our Application on Hugging Face spaces. Casual-to-Formal Formal-to-Casual Active-to-Passive Passive-to-Active For this article, we will be making use of the Open Source library , and . PyTorch StyleFormer Gradio What is StyleFormer StyleFormer is relatively unknown amongst our 3 projects above, I feel it would be beneficial if we get a clear picture of what StyleFormer entails. StyleFormer is a Neural Language Style Transfer framework that transfers natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. It was created by . Prithiviraj Damodaran Project Workflow Pre-Requisites To efficiently follow up this article: You must have Installed Python Have an or Installed IDE Jupyter Notebook Have basic Python Knowledge. If at any point during the tutorial, you can always refer to: Code Repository Hosted App Install Dependencies Installing Gradio pip install gradio In a jupyter Cell: !pip install gradio Installing StyleFormer pip install git+https://github.com/PrithivirajDamodaran/Styleformer.git In a Jupyter Cell: !pip install git+https://github.com/PrithivirajDamodaran/Styleformer.git Installing Pytorch To install Pytorch you have to install a specialized version, in the photo below you will find my personalized version, feel free to use a version that suits your system's requirements. To install your version of PyTorch visit the and install your specialized version. PyTorch website Importing Dependencies Here we are going to import our installed dependencies. #Importingdependancies
from styleformer import Styleformer
import gradio as gr
import torch
import warnings
warnings.filterwarnings("ignore") Above we imported our dependent libraries and told python to ignore all warnings in the last line. Set Seed def set_seed(seed):
  torch.manual_seed(seed)
  if torch.cuda.is_available():
    torch.cuda.manual_seed_all(seed)

set_seed(1234) Above we set the torch seed in other to enable reproducibility. Unfortunately, explaining this is way out of the scope of this article but if you want to know more about this feature, check out the PyTorch Documentation Downloading and Instantiating Style Transfer models #Casual-Formal
sf_0 = Styleformer(style=0)

#Formal-Casual
sf_1 = Styleformer(style=1)

#Active-Passive
sf_2 = Styleformer(style=2)

#Passive-Active
sf_3 = Styleformer(style=3) Above we installed and instantiated all the models that are going to make up our App's features. For the style argument, you pass 0 for the Casual to Formal model, 1 for the Formal to Casual model, 2 for the Active to Passive model, 3 for the Passive to Active model. NOTE: Build Gradio Function COPY COPY def func(text, tone):
  if tone=="Casual-Formal":
    return sf_0.transfer(text)
  elif tone=="Formal-Casual":
    return sf_1.transfer(text)
  elif tone=="Active-Passive":
    return sf_2.transfer(text)
  eliif tone=="Passive-Active":
    return sf_3.transfer(text)
  else:
    return "No available Transfers" Above we created a function that will act as a workflow to tell gradio how we plan to process our input text, we created conditional statements which will enable gradio to efficiently and correctly send text input to the model requested by the user. Initializing Gradio App COPY COPY #Initalizing Gradio App
app_description = "This model transforms the tone of the text, from formal to informal, from Active to Passive. Choose your option below."
app_title = "Tone Transfer"

app = gr.Interface(func,["text",gr.inputs.Radio(["Casual-Formal", "Formal-Casual", "Active-Passive","Passive-Active"])],"text",description=app_description, title=app_title)

app.launch() Above we wrote down the description and the title of our application. We will pass the variables later into our app Interface. For the app interface, we passed our wrapping function, our input UI component is made up of a text box that will take in the user's text, then a radio where the user will choose what style process he would like to undertake either , , etc. Casual-to-Formal Formal-To-Casual We then passed our and variables. description title Lastly, we launched our gradio application. Hosting on Hugging Face Spaces , is essentially free hosting offered by Hugging face for models, it provides containers where engineers can host, run and share their application free of charge. Hugging Face Spaces To host on Hugging face spaces, create a account, once you do that you go onto the and click on the , choose as your option and then follow the instructions on the resulting page. Hugging Face HuggingFace Space page create Space Button gradio Requirements.txt Items In order, to enable the Hugging Face to build your App using their containers, you have to provide a list of libraries your app is dependent on, this is where your requirements.txt file comes in. When you want to push the final repo containing your file to gradio spaces also add a requirement.txt file and put in the items listed below. app.py git+https://github.com/PrithivirajDamodaran/Styleformer.git
torch Important Links Code Repository Hosted App EndNote AI tools are quickly taking over most of the boring tasks that used to be performed by humans, and StyleFormer is one of those tools.

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