import kenlm

lm = kenlm.Model("char.bin")

CORPUS = open("dict.txt", encoding="utf-8").read().splitlines()

def generate_candidates(prefix, max_n=50):
    cands = []

    for line in CORPUS:
        if prefix in line:   # 或 startswith优化
            words = line.split()
            for w in words:
                if w.startswith(prefix):
                    cands.append(w)

    return list(set(cands))[:max_n]


def predict(prefix):
    candidates = generate_candidates(prefix)

    scored = []
    for c in candidates:
        scored.append((c, lm.score(c)))

    return sorted(scored, key=lambda x: x[1], reverse=True)[:5]


while True:
    p = input("prefix: ")
    res = predict(p)

    print("\n候选:")
    for w, s in res:
        print(w, s)