The Text-Input Suggest Project is an application that utilizes Natural Language Processing (NLP) techniques to provide real-time word suggestions as the user types. Inspired by modern predictive text features found in smartphones and text editors, this project enhances typing speed and accuracy by predicting the next word based on partially entered text.
The objective of the Text-Input Suggest is to implement a predictive text system that suggests possible word completions based on user input. The project uses relative word frequency from a given corpus to generate word predictions, aiming to improve typing efficiency and accuracy.
By analyzing the frequency of words, the system creates a probability dictionary where each word is associated with its likelihood of occurrence relative to other words. When a user starts typing, the system calculates the probabilities of potential word completions based on this dictionary, offering suggestions ranked by likelihood.
This approach enables the system to make informed predictions, helping users type faster by reducing keystrokes and minimizing errors. The project demonstrates the practical application of probability-based models in Natural Language Processing (NLP) for real-time text suggestion systems.
Step1: Download the zip file of the project and extract it.
Step2: Create a new project in PyCharm and open the extracted file in it.
Step3: Before running the project install flask, pandas & textdistance.
Step4: After completing the procedure run the file app.py it will generate a url in the terminal, open the url on web browser.
