import gensim.downloader as api

print("Loading model... (This may take some time)")
model = api.load("word2vec-google-news-300")
print("Model Loaded")


def find_similarity(word):
    try:
        similar_words = model.most_similar(word)

        print(f"\nWords similar to '{word}':")
        for w, score in similar_words[:5]:
            print(f"{w}: {score:.4f}")

    except KeyError:
        print(f"'{word}' not found in the vocabulary.")


def word_arithmetic(word1, word2, word3):
    try:
        result = model.most_similar(
            positive=[word1, word2],
            negative=[word3]
        )

        print(
            f"\n'{word1}' - '{word3}' + '{word2}' = "
            f"'{result[0][0]}' (Most similar word)"
        )

    except KeyError as e:
        print(f"Error: {e}")


def check_similarity(word1, word2):
    try:
        similarity = model.similarity(word1, word2)

        print(
            f"\nSimilarity between '{word1}' and "
            f"'{word2}': {similarity:.4f}"
        )

    except KeyError as e:
        print(f"Error: {e}")


def odd_one_out(words):
    try:
        odd = model.doesnt_match(words)

        print(f"\nOdd one out from {words}: {odd}")

    except KeyError as e:
        print(f"Error: {e}")



find_similarity("king")

word_arithmetic("king", "woman", "man")

check_similarity("king", "queen")

odd_one_out(["apple", "banana", "grape", "car"])