# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "torch",
#     "torchvision",
#     "transformers",
#     "accelerate",
#     "peft",
# ]
# ///

try:
    # The sentences to encode
    sentence_high = [
        "The chef prepared a delicious meal for the guests.",
        "A tasty dinner was cooked by the chef for the visitors."
    ]
    sentence_medium = [
        "She is an expert in machine learning.",
        "He has a deep interest in artificial intelligence."
    ]
    sentence_low = [
        "The weather in Tokyo is sunny today.",
        "I need to buy groceries for the week."
    ]
    
    for sentence in [sentence_high, sentence_medium, sentence_low]:
        print("🙋‍♂️")
        print(sentence)
        embeddings = model.encode(sentence)
        similarities = model.similarity(embeddings[0], embeddings[1])
        print("`-> 🤖 score: ", similarities.numpy()[0][0])
    with open('google_embeddinggemma-300m_2.txt', 'w', encoding='utf-8') as f:
        f.write('Everything was good in google_embeddinggemma-300m_2.txt')
except Exception as e:
    with open('google_embeddinggemma-300m_2.txt', 'w', encoding='utf-8') as f:
        import traceback
        traceback.print_exc(file=f)
finally:
    from huggingface_hub import upload_file
    upload_file(
        path_or_fileobj='google_embeddinggemma-300m_2.txt',
        repo_id='model-metadata/code_execution_files',
        path_in_repo='google_embeddinggemma-300m_2.txt',
        repo_type='dataset',
    )