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. 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 Casual-to-Formal, Formal-to-Casual, Active-to-Passive, Passive-to-Active, we will also host our Application on Hugging Face spaces. For this article, we will be making use of the Open Source library PyTorch StyleFormer, and Gradio. 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.
Gradio is free and open source python library . We can quickly and easily create UI interfaces within our python notebook,or share with anyone with just few lines of code and demonstrate our finished model results. Gradio helps quickly create customizable UI components within colab, jyupter notebook or scripts and around TensorFlow or PyTorch models, or even arbitrary Python functions. Gradio installation is fast and easy to setup .You can install Gradio using pip command.
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes it challenging for non-technical collaborators and endpoint users (e.g. physicians) to easily provide feedback on model development and to gain trust in ML. The accessibility challenge also makes collaboration more difficult and limits the ML researcher's exposure to realistic data and scenarios that occur in the wild. To improve accessibility and facilitate collaboration, we developed an open-source Python package, Gradio, which allows researchers to rapidly generate a visual interface for their ML models. Gradio makes accessing any ML model as easy as sharing a URL. Our development of Gradio is informed by interviews with a number of machine learning researchers who participate in interdisciplinary collaborations. Their feedback identified that Gradio should support a variety of interfaces and frameworks, allow for easy sharing of the interface, allow for input manipulation and interactive inference by the domain expert, as well as allow embedding the interface in iPython notebooks. We developed these features and carried out a case study to understand Gradio's usefulness and usability in the setting of a machine learning collaboration between a researcher and a cardiologist.
This is a write-up for my old project ClothingGAN. The project generates clothing design with AI using StyleGAN and semantically edits it with attributes such as sleeve, size, dress, jacket, etc. You can also do style transfer as shown in the image above by first generating 2 different clothing designs (output 1) with different seed numbers. Then, it will generate a third design (output 2) that mixes the previous 2 designs. You can then adjust how much style or structure you want it to inherit from the two original designs.
This tutorial is a step-by-step, beginner-friendly explanation of how you can integrate PyCaret and Gradio, the two powerful open-source libraries in Python, and supercharge your machine learning experimentation within minutes. This tutorial is a "hello world" example, I have used Iris Dataset from UCI, which is a multiclassification problem where the goal is to predict the class of iris plants. The code given in this example can be reproduced on any other dataset, without any major modifications. PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently.