Typing efficiently is essential in today$$s fast$-$paced digital world. Many devices offer predictive text assistance, suggesting letters or words to help users type faster. However, these predictions are not always accurate, leading to frustration and errors. Imagine you are developing a personalized text suggestion system that predicts the most likely next letter$($s$)$ based on your typing history. This system analyzes past usage to provide tailored suggestions, reducing errors and improving the overall typing experience. Unlike existing systems, which complete entire words, your system focuses on suggesting only the most probable next letter$($s$),$ ensuring better flexibility and precision. This program will simulate a text prediction engine capable of learning from user input and responding to queries with suggestions. It offers the following functionalities: $1.$ Adding words and their usage frequencies to the system$$s dictionary. $2.$ Respond to queries by predicting the most likely next letter based on the provided prefix. The program will appropriately inform the user if no words match a given prefix. Example: Suppose your dictionary contains the following data: $$ "hello" used $50$ times $$ "help" used $30$ times $$ "heat" used $20$ times $$ "hero" used $10$ times If you query with the prefix, $$he$,$ the system will analyze the words starting with $"$he$"$ and suggest the most likely next letter$($s$)$: $\text{'}$l$\text{'}$ appears in "hello" and "help" a total of $50+30=80$ times. $\text{'}$a$\text{'}$ appears in "heat" a total of $20$ times. $\text{'}$r$\text{'}$ appears in "hero" a total of $10$ times. Thus, the most likely next letter is l$.$ If you query with the prefix ho$,$ since no word starts with ho$,$ the program will respond appropriately, indicating that no matching prefix exists in the dictionary. The Problem You are tasked with designing a system that answers queries about the most likely next letter based on a list of words and their usage frequencies. Your program will process a series of operations, which include adding words to a dictionary and querying for prefixes.