• Typing is a part of everyday digital communication.
• Predictive text models improve typing efficiency and user experience.
• Goal: Build a real-time next-word prediction model using NLP and deploy it using Shiny.
• Live App URL:
  https://nikhilchandra4321.shinyapps.io/AI_Text_Completion_App/

• Users spend time typing repetitive phrases.
• Predictive text can reduce typing effort and increase productivity.
• AI-powered completion engines are already being used in:
  • Keyboards (Gboard, SwiftKey)
  • Email tools (Gmail Smart Compose)
  • Code editors (GitHub Copilot)
• Opportunity: Build a lightweight, deployable NLP engine with Shiny.

• Uses N-gram Language Models (Unigram, Bigram, Trigram).
• Stupid Backoff Smoothing (α = 0.4) handles unseen phrases.
• Workflow:
  1. User enters a phrase.
  2. System identifies last 1–2 words.
  3. Predicts next word using most probable match.
  4. Displays top prediction + top 3 suggestions.
• Efficient and deployable in real-time.

Features: - Text input box to enter phrase. - Predict button triggers NLP model. - Displays the most likely next word. - Also shows top 3 suggestions.

User Feedback: - Fast, simple, and intuitive. - Demonstrates real-time prediction capabilities.

Try the app live:
https://nikhilchandra4321.shinyapps.io/AI_Text_Completion_App/

Applications: - Chatbots, typing assistants, email automation, code completion.

Future Enhancements: - Add deep learning models (LSTM, Transformer). - Train on larger, domain-specific datasets. - Personalization per user.

Conclusion: This project demonstrates a scalable AI-powered predictive text engine with real-world value and strong potential for commercial implementation.