Hey Bot - AI Text Summarizer

• Summarizes any text up to 100000 characters, using Facebook’s opensource Bart-large-CNN model. • Added 2 extra features: Text-to-voice (using Web-Speech API) & Copy-to-clipboard (Clipboard API)

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Inspiration 💡

I have always been fascinated by artificial intelligence (AI) and natural language processing (NLP). With the rise of open-source NLP models like BART (Facebook), I wanted to create an application where users could input long texts and receive concise, accurate summaries generated by these models. This led me to the idea of building a text summarizer app using the powerful Hugging Face API.

Learning Experience 📚

While doing this project, I gained valuable insights into building full-stack applications using various technologies such as HTML, CSS, JavaScript, Node.js + Express.js, and APIs. The most significant learning experience came from diving deep into the Hugging Face Inference API documentation and experimenting with different prompts and parameters to achieve optimal results.

Moreover, utilizing Postman's capabilities to automate code generation and integration saved time and effort while developing the application. Additionally, I expanded my knowledge of modern frontend technologies like the Web Speech API and Clipboard API to enhance user experience.

Building the Project 🔧

To build the text summarizer application, I followed these steps:

  1. Planning: Defined the scope, features, and requirements of the application.
  2. Design: Created wireframes and mock-ups using Figma.
  3. Backend Development: Set up a new Node.js project on Replit, installed required packages, and wrote server logic using Express.js.
  4. API Integration: Utilized Postman to explore Hugging Face API endpoints, test requests, and generate code snippets for easy integration into the backend.
  5. Frontend Development: Designed the UI using HTML and CSS, implemented interactivity with JavaScript, and integrated the backend API calls.
  6. Testing: Conducted thorough testing to ensure all functionalities worked correctly.
  7. Deployment: Deployed the completed application on Replit and shared the link with others.

Challenges 🌫️

Some of the challenges I encountered during development included:

  1. Optimizing Summarization Results: Fine-tuning the model's hyperparameters and understanding its behaviour was crucial to achieving satisfactory summarization results.
  2. Integrating Multiple APIs: Ensuring smooth communication between multiple APIs and handling their responses effectively was essential to maintaining proper flow within the application.
  3. Enhancing User Interface: Creating an intuitive and visually appealing interface that catered to both desktop and mobile devices proved challenging but rewarding.

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