How to pass a user input through cosine similarity?

Question

I am an absolute beginner in coding. There is this book recommendation system, but I want to make a small change here. Currently, the program recommends similar books after receiving book ID from the user, but I want it to take a book title as input from the user and find similar titles after comparing the user's input with the titles in the csv file. Any step by step guide on how the code is working would also be appreciated :) . Here is the code

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
ds = pd.read_csv("test1.csv") 
tf = TfidfVectorizer(analyzer='word', ngram_range=(1, 3), min_df=0, stop_words='english')


tfidf_matrix = tf.fit_transform(ds['Book Title'])
cosine_similarities = cosine_similarity(tfidf_matrix,tfidf_matrix)

results = {} # dictionary created to store the result in a dictionary format (ID : (Score,item_id))

for idx, row in ds.iterrows(): #iterates through all the rows
    # the below code 'similar_indice' stores similar ids based on cosine similarity. sorts them in ascending order. [:-5:-1] is then used so that the indices with most similarity are got. 0 means no similarity and 1 means perfect similarity
    similar_indices = cosine_similarities[idx].argsort()[:-5:-1] #stores 5 most similar books, you can change it as per your needs
    similar_items = [(cosine_similarities[idx][i], ds['ID'][i]) for i in similar_indices]
    results[row['ID']] = similar_items[1:]

#below code 'function item(id)' returns a row matching the id along with Book Title. Initially it is a dataframe, then we convert it to a list
def item(id):
    return ds.loc[ds['ID'] == id]['Book Title'].tolist()[0]
def recommend(id, num):
    if (num == 0):
        print("Unable to recommend any book as you have not chosen the number of book to be recommended")
    elif (num==1):
        print("Recommending " + str(num) + " book similar to " + item(id))

    else :
        print("Recommending " + str(num) + " books similar to " + item(id))

    print("----------------------------------------------------------")
    recs = results[id][:num]
    for rec in recs:
        print("You may also like to read: " + item(rec[1]) + " (score:" + str(rec[0]) + ")")

#the first argument in the below function to be passed is the id of the book, second argument is the number of books you want to be recommended
recommend(5,2)