import extract_doc_tag as ex_doc_tag

# --- 方式一：指定文件路径 ---
file_path = 'test.txt'

try:
    with open(file_path, 'r', encoding='utf-8') as file:
        my_article_content = file.read()
except FileNotFoundError:
    print(f"错误：找不到文件 '{file_path}'")
    exit(1)
except Exception as e:
    print(f"读取文件时发生错误: {e}")
    exit(1)

model = "qwen3:1.7b"
# model = "qwen3:0.6b"
# OLLAMA_BASE_URL = "http://127.0.0.1:11434/api/generate"
OLLAMA_BASE_URL = "http://152.136.153.72:27009/api/generate"
extracted_tags = ex_doc_tag.extract_tags_with_ollama_from_content(
    OLLAMA_BASE_URL,
    model,
    my_article_content,
    max_tags=5,
    min_length=2,
    max_length=10
)

if extracted_tags['code'] == 0:
    print('思考过程：')
    print(extracted_tags['data']['think'])
    print('=' * 60)
    print('文章内容长度', len(my_article_content))
    # print(f"Ollama 模型：{model}")
    print('=' * 60)
    print('文章标签：', extracted_tags['data']['tags'])
    print('=' * 60)
    print(f"总耗时: {extracted_tags['data']['consume']:.2f} 秒")
else:
    print(extracted_tags)