gistfile1.txt

import difflib

def fuzzy_insert(words, new_word, threshold=0.6):
    # 1️⃣ Calculate similarity scores
    similarities = [
        (i, difflib.SequenceMatcher(None, new_word, w).ratio())
        for i, w in enumerate(words)
    ]

    # 2️⃣ Find best match
    best_index, best_score = max(similarities, key=lambda x: x[1])

    # 3️⃣ If similarity too low → append to end
    if best_score < threshold:
        words.append(new_word)
        return words

    # 4️⃣ Insert near best match (keep local alphabetical order)
    insert_index = best_index

    # If new word should come after best match alphabetically
    if new_word > words[best_index]:
        insert_index = best_index + 1

    words.insert(insert_index, new_word)

    return words