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Commit ec1ba2c0 authored by Sidney233's avatar Sidney233
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test: Delete previous unit tests and add new end-to-end test.

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tests/unittest/test_integrations/test_rag/test_api.py deleted 100644 → 0
View file @ 343eaac1
import json
import os
import shutil
import tempfile
from magic_pdf.integrations.rag.api import DataReader, RagDocumentReader
from magic_pdf.integrations.rag.type import CategoryType
from magic_pdf.integrations.rag.utils import \
convert_middle_json_to_layout_elements
def test_rag_document_reader():
# setup
unitest_dir = '/tmp/magic_pdf/unittest/integrations/rag'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir=unitest_dir)
os.makedirs(temp_output_dir, exist_ok=True)
# test
with open('tests/unittest/test_integrations/test_rag/assets/middle.json') as f:
json_data = json.load(f)
res = convert_middle_json_to_layout_elements(json_data, temp_output_dir)
doc = RagDocumentReader(res)
assert len(list(iter(doc))) == 1
page = list(iter(doc))[0]
assert len(list(iter(page))) >= 10
assert len(page.get_rel_map()) >= 3
item = list(iter(page))[0]
assert item.category_type == CategoryType.text
# teardown
shutil.rmtree(temp_output_dir)
def test_data_reader():
# setup
unitest_dir = '/tmp/magic_pdf/unittest/integrations/rag'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir=unitest_dir)
os.makedirs(temp_output_dir, exist_ok=True)
# test
data_reader = DataReader('tests/unittest/test_integrations/test_rag/assets', 'ocr',
temp_output_dir)
assert data_reader.get_documents_count() == 2
for idx in range(data_reader.get_documents_count()):
document = data_reader.get_document_result(idx)
assert document is not None
# teardown
shutil.rmtree(temp_output_dir)
tests/unittest/test_integrations/test_rag/test_utils.py deleted 100644 → 0
View file @ 343eaac1
import json
import os
import shutil
import tempfile
from magic_pdf.integrations.rag.type import CategoryType
from magic_pdf.integrations.rag.utils import (
convert_middle_json_to_layout_elements, inference)
def test_convert_middle_json_to_layout_elements():
# setup
unitest_dir = '/tmp/magic_pdf/unittest/integrations/rag'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir=unitest_dir)
os.makedirs(temp_output_dir, exist_ok=True)
# test
with open('tests/unittest/test_integrations/test_rag/assets/middle.json') as f:
json_data = json.load(f)
res = convert_middle_json_to_layout_elements(json_data, temp_output_dir)
assert len(res) == 1
assert len(res[0].layout_dets) > 0
assert res[0].layout_dets[0].anno_id == 0
assert res[0].layout_dets[0].category_type == CategoryType.text
assert len(res[0].extra.element_relation) >= 2
# teardown
shutil.rmtree(temp_output_dir)
def test_inference():
asset_dir = 'tests/unittest/test_integrations/test_rag/assets'
# setup
unitest_dir = '/tmp/magic_pdf/unittest/integrations/rag'
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir=unitest_dir)
os.makedirs(temp_output_dir, exist_ok=True)
# test
res = inference(
asset_dir + '/one_page_with_table_image.pdf',
temp_output_dir,
'ocr',
)
assert res is not None
assert len(res) == 1
assert len(res[0].layout_dets) > 0
assert res[0].layout_dets[0].anno_id == 0
assert res[0].layout_dets[0].category_type == CategoryType.text
assert len(res[0].extra.element_relation) >= 2
# teardown
shutil.rmtree(temp_output_dir)
tests/unittest/test_metascan_classify/test_classify.py.bak deleted 100644 → 0
View file @ 343eaac1
import os
import pytest
from magic_pdf.filter.pdf_classify_by_type import classify_by_area, classify_by_text_len, classify_by_avg_words, \
classify_by_img_num, classify_by_text_layout, classify_by_img_narrow_strips
from magic_pdf.filter.pdf_meta_scan import get_pdf_page_size_pts, get_pdf_textlen_per_page, get_imgs_per_page
from test_commons import get_docs_from_test_pdf, get_test_json_data
# 获取当前目录
current_directory = os.path.dirname(os.path.abspath(__file__))
'''
根据图片尺寸占页面面积的比例，判断是否为扫描版
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_area",
[
("the_eye/the_eye_cdn_00391653", True), # 特殊文字版1.每页存储所有图片，特点是图片占页面比例不大，每页展示可能为0也可能不止1张
("scihub/scihub_08400000/libgen.scimag08489000-08489999.zip_10.1016/0370-1573(90)90070-i", False), # 特殊扫描版2，每页存储的扫描页图片数量递增，特点是图占比大，每页展示1张
("zlib/zlib_17216416", False), # 特殊扫描版3，有的页面是一整张大图，有的页面是通过一条条小图拼起来的，检测图片占比之前需要先按规则把小图拼成大图
("the_eye/the_eye_wtl_00023799", False), # 特殊扫描版4，每一页都是一张张小图拼出来的，检测图片占比之前需要先按规则把小图拼成大图
("the_eye/the_eye_cdn_00328381", False), # 特殊扫描版5，每一页都是一张张小图拼出来的，存在多个小图多次重复使用情况，检测图片占比之前需要先按规则把小图拼成大图
("scihub/scihub_25800000/libgen.scimag25889000-25889999.zip_10.2307/4153991", False), # 特殊扫描版6，只有三页，其中两页是扫描版
("scanned_detection/llm-raw-scihub-o.O-0584-8539%2891%2980165-f", False), # 特殊扫描版7，只有一页且由小图拼成大图
("scanned_detection/llm-raw-scihub-o.O-bf01427123", False), # 特殊扫描版8，只有3页且全是大图扫描版
("scihub/scihub_41200000/libgen.scimag41253000-41253999.zip_10.1080/00222938709460256", False), # 特殊扫描版12，头两页文字版且有一页没图片，后面扫描版11页
("scihub/scihub_37000000/libgen.scimag37068000-37068999.zip_10.1080/0015587X.1936.9718622", False) # 特殊扫描版13，头两页文字版且有一页没图片，后面扫描版3页
])
def test_classify_by_area(book_name, expected_bool_classify_by_area):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
docs = get_docs_from_test_pdf(book_name)
median_width, median_height = get_pdf_page_size_pts(docs)
page_width = int(median_width)
page_height = int(median_height)
img_sz_list = test_data[book_name]["expected_image_info"]
total_page = len(docs)
text_len_list = get_pdf_textlen_per_page(docs)
bool_classify_by_area = classify_by_area(total_page, page_width, page_height, img_sz_list, text_len_list)
# assert bool_classify_by_area == expected_bool_classify_by_area
'''
广义上的文字版检测，任何一页大于100字，都认为为文字版
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_text_len",
[
("scihub/scihub_67200000/libgen.scimag67237000-67237999.zip_10.1515/crpm-2017-0020", True), # 文字版，少于50页
("scihub/scihub_83300000/libgen.scimag83306000-83306999.zip_10.1007/978-3-658-30153-8", True), # 文字版，多于50页
("zhongwenzaixian/zhongwenzaixian_65771414", False), # 完全无字的宣传册
])
def test_classify_by_text_len(book_name, expected_bool_classify_by_text_len):
docs = get_docs_from_test_pdf(book_name)
text_len_list = get_pdf_textlen_per_page(docs)
total_page = len(docs)
bool_classify_by_text_len = classify_by_text_len(text_len_list, total_page)
# assert bool_classify_by_text_len == expected_bool_classify_by_text_len
'''
狭义上的文字版检测，需要平均每页字数大于200字
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_avg_words",
[
("zlib/zlib_21207669", False), # 扫描版，书末尾几页有大纲文字
("zlib/zlib_19012845", False), # 扫描版，好几本扫描书的集合，每本书末尾有一页文字页
("scihub/scihub_67200000/libgen.scimag67237000-67237999.zip_10.1515/crpm-2017-0020", True),# 正常文字版
("zhongwenzaixian/zhongwenzaixian_65771414", False), # 宣传册
("zhongwenzaixian/zhongwenzaixian_351879", False), # 图解书/无字or少字
("zhongwenzaixian/zhongwenzaixian_61357496_pdfvector", False), # 书法集
("zhongwenzaixian/zhongwenzaixian_63684541", False), # 设计图
("zhongwenzaixian/zhongwenzaixian_61525978", False), # 绘本
("zhongwenzaixian/zhongwenzaixian_63679729", False), # 摄影集
])
def test_classify_by_avg_words(book_name, expected_bool_classify_by_avg_words):
docs = get_docs_from_test_pdf(book_name)
text_len_list = get_pdf_textlen_per_page(docs)
bool_classify_by_avg_words = classify_by_avg_words(text_len_list)
# assert bool_classify_by_avg_words == expected_bool_classify_by_avg_words
'''
这个规则只针对特殊扫描版1，因为扫描版1的图片信息都由于junk_list的原因被舍弃了，只能通过图片数量来判断
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_img_num",
[
("zlib/zlib_21370453", False), # 特殊扫描版1，每页都有所有扫描页图片，特点是图占比大，每页展示1至n张
("zlib/zlib_22115997", False), # 特殊扫描版2，类似特1，但是每页数量不完全相等
("zlib/zlib_21814957", False), # 特殊扫描版3，类似特1，但是每页数量不完全相等
("zlib/zlib_21814955", False), # 特殊扫描版4，类似特1，但是每页数量不完全相等
])
def test_classify_by_img_num(book_name, expected_bool_classify_by_img_num):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
docs = get_docs_from_test_pdf(book_name)
img_num_list = get_imgs_per_page(docs)
img_sz_list = test_data[book_name]["expected_image_info"]
bool_classify_by_img_num = classify_by_img_num(img_sz_list, img_num_list)
# assert bool_classify_by_img_num == expected_bool_classify_by_img_num
'''
排除纵向排版的pdf
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_text_layout",
[
("vertical_detection/三国演义_繁体竖排版", False), # 竖排版本1
("vertical_detection/净空法师_大乘无量寿", False), # 竖排版本2
("vertical_detection/om3006239", True), # 横排版本1
("vertical_detection/isit.2006.261791", True), # 横排版本2
])
def test_classify_by_text_layout(book_name, expected_bool_classify_by_text_layout):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
text_layout_per_page = test_data[book_name]["expected_text_layout"]
bool_classify_by_text_layout = classify_by_text_layout(text_layout_per_page)
# assert bool_classify_by_text_layout == expected_bool_classify_by_text_layout
'''
通过检测页面是否由多个窄长条图像组成，来过滤特殊的扫描版
这个规则只对窄长条组成的pdf进行识别，而不会识别常规的大图扫描pdf
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_img_narrow_strips",
[
("scihub/scihub_25900000/libgen.scimag25991000-25991999.zip_10.2307/40066695", False), # 特殊扫描版
("the_eye/the_eye_wtl_00023799", False), # 特殊扫描版4，每一页都是一张张小图拼出来的，检测图片占比之前需要先按规则把小图拼成大图
("the_eye/the_eye_cdn_00328381", False), # 特殊扫描版5，每一页都是一张张小图拼出来的，存在多个小图多次重复使用情况，检测图片占比之前需要先按规则把小图拼成大图
("scanned_detection/llm-raw-scihub-o.O-0584-8539%2891%2980165-f", False), # 特殊扫描版7，只有一页且由小图拼成大图
("scihub/scihub_25800000/libgen.scimag25889000-25889999.zip_10.2307/4153991", True), # 特殊扫描版6，只有三页，其中两页是扫描版
("scanned_detection/llm-raw-scihub-o.O-bf01427123", True), # 特殊扫描版8，只有3页且全是大图扫描版
("scihub/scihub_53700000/libgen.scimag53724000-53724999.zip_10.1097/00129191-200509000-00018", True), # 特殊文本版，有一长条，但是只有一条
])
def test_classify_by_img_narrow_strips(book_name, expected_bool_classify_by_img_narrow_strips):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
img_sz_list = test_data[book_name]["expected_image_info"]
docs = get_docs_from_test_pdf(book_name)
median_width, median_height = get_pdf_page_size_pts(docs)
page_width = int(median_width)
page_height = int(median_height)
bool_classify_by_img_narrow_strips = classify_by_img_narrow_strips(page_width, page_height, img_sz_list)
# assert bool_classify_by_img_narrow_strips == expected_bool_classify_by_img_narrow_strips
\ No newline at end of file
tests/unittest/test_metascan_classify/test_commons.py.bak deleted 100644 → 0
View file @ 343eaac1
import io
import json
import os
import fitz
import boto3
from botocore.config import Config
from magic_pdf.libs.config_reader import get_s3_config_dict
from magic_pdf.libs.commons import join_path, json_dump_path, read_file, parse_bucket_key
from loguru import logger
test_pdf_dir_path = "s3://llm-pdf-text/unittest/pdf/"
def get_test_pdf_json(book_name):
json_path = join_path(json_dump_path, book_name + ".json")
s3_config = get_s3_config_dict(json_path)
file_content = read_file(json_path, s3_config)
json_str = file_content.decode('utf-8')
json_object = json.loads(json_str)
return json_object
def read_test_file(book_name):
test_pdf_path = join_path(test_pdf_dir_path, book_name + ".pdf")
s3_config = get_s3_config_dict(test_pdf_path)
try:
file_content = read_file(test_pdf_path, s3_config)
return file_content
except Exception as e:
if "NoSuchKey" in str(e):
logger.warning("File not found in test_pdf_path. Downloading from orig_s3_pdf_path.")
try:
json_object = get_test_pdf_json(book_name)
orig_s3_pdf_path = json_object.get('file_location')
s3_config = get_s3_config_dict(orig_s3_pdf_path)
file_content = read_file(orig_s3_pdf_path, s3_config)
s3_client = get_s3_client(test_pdf_path)
bucket_name, bucket_key = parse_bucket_key(test_pdf_path)
file_obj = io.BytesIO(file_content)
s3_client.upload_fileobj(file_obj, bucket_name, bucket_key)
return file_content
except Exception as e:
logger.exception(e)
else:
logger.exception(e)
def get_docs_from_test_pdf(book_name):
file_content = read_test_file(book_name)
return fitz.open("pdf", file_content)
def get_test_json_data(directory_path, json_file_name):
with open(os.path.join(directory_path, json_file_name), "r", encoding='utf-8') as f:
test_data = json.load(f)
return test_data
def get_s3_client(path):
s3_config = get_s3_config_dict(path)
try:
return boto3.client(
"s3",
aws_access_key_id=s3_config["ak"],
aws_secret_access_key=s3_config["sk"],
endpoint_url=s3_config["endpoint"],
config=Config(s3={"addressing_style": "path"}, retries={"max_attempts": 8, "mode": "standard"}),
)
except:
# older boto3 do not support retries.mode param.
return boto3.client(
"s3",
aws_access_key_id=s3_config["ak"],
aws_secret_access_key=s3_config["sk"],
endpoint_url=s3_config["endpoint"],
config=Config(s3={"addressing_style": "path"}, retries={"max_attempts": 8}),
)
tests/unittest/test_metascan_classify/test_meta_scan.py.bak deleted 100644 → 0
View file @ 343eaac1
import os
import pytest
from magic_pdf.filter.pdf_meta_scan import get_pdf_page_size_pts, get_image_info, get_pdf_text_layout_per_page, get_language
from test_commons import get_docs_from_test_pdf, get_test_json_data
# 获取当前目录
current_directory = os.path.dirname(os.path.abspath(__file__))
'''
获取pdf的宽与高，宽和高各用一个list，分别取中位数
'''
@pytest.mark.parametrize("book_name, expected_width, expected_height",
[
("zlib/zlib_17058115", 795, 1002), # pdf中最大页与最小页差异极大个例
("the_eye/the_eye_wtl_00023799", 616, 785) # 采样的前50页存在中位数大小页面横竖旋转情况
])
def test_get_pdf_page_size_pts(book_name, expected_width, expected_height):
docs = get_docs_from_test_pdf(book_name)
median_width, median_height = get_pdf_page_size_pts(docs)
# assert int(median_width) == expected_width
# assert int(median_height) == expected_height
'''
获取pdf前50页的图片信息，为了提速，对特殊扫描版1的情况做了过滤，其余情况都正常取图片信息
'''
@pytest.mark.parametrize("book_name",
[
"zlib/zlib_21370453", # 特殊扫描版1，每页都有所有扫描页图片，特点是图占比大，每页展示1至n张
"the_eye/the_eye_cdn_00391653", # 特殊文字版1.每页存储所有图片，特点是图片占页面比例不大，每页展示可能为0也可能不止1张，这种pdf需要拿前10页抽样检测img大小和个数，如果符合需要清空junklist
"scihub/scihub_08400000/libgen.scimag08489000-08489999.zip_10.1016/0370-1573(90)90070-i", # 扫描版2，每页存储的扫描页图片数量递增，特点是图占比大，每页展示1张，需要清空junklist跑前50页图片信息用于分类判断
"zlib/zlib_17216416", # 特殊扫描版3，有的页面是一整张大图，有的页面是通过一条条小图拼起来的
"the_eye/the_eye_wtl_00023799", # 特殊扫描版4，每一页都是一张张小图拼出来的
"the_eye/the_eye_cdn_00328381", # 特殊扫描版5，每一页都是一张张小图拼出来的，但是存在多个小图多次重复使用情况
"scihub/scihub_25800000/libgen.scimag25889000-25889999.zip_10.2307/4153991", # 特殊扫描版6，只有3页且其中两页是扫描页
"scanned_detection/llm-raw-scihub-o.O-0584-8539%2891%2980165-f", # 特殊扫描版7，只有一页，且是一张张小图拼出来的
"scanned_detection/llm-raw-scihub-o.O-bf01427123", # 特殊扫描版8，只有3页且全是大图扫描版
"zlib/zlib_22115997", # 特殊扫描版9，类似特1，但是每页数量不完全相等
"zlib/zlib_21814957", # 特殊扫描版10，类似特1，但是每页数量不完全相等
"zlib/zlib_21814955", # 特殊扫描版11，类似特1，但是每页数量不完全相等
"scihub/scihub_41200000/libgen.scimag41253000-41253999.zip_10.1080/00222938709460256", # 特殊扫描版12，头两页文字版且有一页没图片，后面扫描版11页
"scihub/scihub_37000000/libgen.scimag37068000-37068999.zip_10.1080/0015587X.1936.9718622" # 特殊扫描版13，头两页文字版且有一页没图片，后面扫描版3页
])
def test_get_image_info(book_name):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
docs = get_docs_from_test_pdf(book_name)
page_width_pts, page_height_pts = get_pdf_page_size_pts(docs)
image_info, junk_img_bojids = get_image_info(docs, page_width_pts, page_height_pts)
# assert image_info == test_data[book_name]["expected_image_info"]
# assert junk_img_bojids == test_data[book_name]["expected_junk_img_bojids"]
'''
获取pdf前50页的文本布局信息，输出list，每个元素为一个页面的横竖排信息
'''
@pytest.mark.parametrize("book_name",
[
"vertical_detection/三国演义_繁体竖排版", # 竖排版本1
"vertical_detection/净空法师_大乘无量寿", # 竖排版本2
"vertical_detection/om3006239", # 横排版本1
"vertical_detection/isit.2006.261791" # 横排版本2
])
def test_get_text_layout_info(book_name):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
docs = get_docs_from_test_pdf(book_name)
text_layout_info = get_pdf_text_layout_per_page(docs)
# assert text_layout_info == test_data[book_name]["expected_text_layout"]
'''
获取pdf的语言信息
'''
@pytest.mark.parametrize("book_name, expected_language",
[
("scihub/scihub_05000000/libgen.scimag05023000-05023999.zip_10.1034/j.1601-0825.2003.02933.x", "en"), # 英文论文
])
def test_get_text_language_info(book_name, expected_language):
docs = get_docs_from_test_pdf(book_name)
text_language = get_language(docs)
# assert text_language == expected_language
tests/unittest/test_model/assets/test_01.model.json deleted 100644 → 0
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tests/unittest/test_model/assets/test_02.model.json deleted 100644 → 0
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tests/unittest/test_model/test_magic_model.py deleted 100644 → 0
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import json
from magic_pdf.data.read_api import read_local_pdfs
from magic_pdf.model.magic_model import MagicModel
def test_magic_model_image_v2():
datasets = read_local_pdfs('tests/unittest/test_model/assets/test_01.pdf')
with open('tests/unittest/test_model/assets/test_01.model.json') as f:
model_json = json.load(f)
magic_model = MagicModel(model_json, datasets[0])
imgs = magic_model.get_imgs_v2(0)
print(imgs)
tables = magic_model.get_tables_v2(0)
print(tables)
def test_magic_model_table_v2():
datasets = read_local_pdfs('tests/unittest/test_model/assets/test_02.pdf')
with open('tests/unittest/test_model/assets/test_02.model.json') as f:
model_json = json.load(f)
magic_model = MagicModel(model_json, datasets[0])
tables = magic_model.get_tables_v2(5)
print(tables)
tables = magic_model.get_tables_v2(8)
print(tables)
tests/unittest/test_table/test_rapidtable.py deleted 100644 → 0
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import unittest
import os
from PIL import Image
from lxml import etree
from magic_pdf.model.sub_modules.model_init import AtomModelSingleton
from magic_pdf.model.sub_modules.table.rapidtable.rapid_table import RapidTableModel
class TestppTableModel(unittest.TestCase):
def test_image2html(self):
img = Image.open(os.path.join(os.path.dirname(__file__), "assets/table.jpg"))
atom_model_manager = AtomModelSingleton()
ocr_engine = atom_model_manager.get_atom_model(
atom_model_name='ocr',
ocr_show_log=False,
det_db_box_thresh=0.5,
det_db_unclip_ratio=1.6,
lang='ch'
)
table_model = RapidTableModel(ocr_engine, 'slanet_plus')
html_code, table_cell_bboxes, logic_points, elapse = table_model.predict(img)
# 验证生成的 HTML 是否符合预期
parser = etree.HTMLParser()
tree = etree.fromstring(html_code, parser)
# 检查 HTML 结构
assert tree.find('.//table') is not None, "HTML should contain a <table> element"
assert tree.find('.//tr') is not None, "HTML should contain a <tr> element"
assert tree.find('.//td') is not None, "HTML should contain a <td> element"
# 检查具体的表格内容
headers = tree.xpath('//table/tr[1]/td')
assert len(headers) == 5, "Thead should have 5 columns"
assert headers[0].text and headers[0].text.strip() == "Methods", "First header should be 'Methods'"
assert headers[1].text and headers[1].text.strip() == "R", "Second header should be 'R'"
assert headers[2].text and headers[2].text.strip() == "P", "Third header should be 'P'"
assert headers[3].text and headers[3].text.strip() == "F", "Fourth header should be 'F'"
assert headers[4].text and headers[4].text.strip() == "FPS", "Fifth header should be 'FPS'"
# 检查第一行数据
first_row = tree.xpath('//table/tr[2]/td')
assert len(first_row) == 5, "First row should have 5 cells"
assert first_row[0].text and 'SegLink' in first_row[0].text.strip(), "First cell should be 'SegLink [26]'"
assert first_row[1].text and first_row[1].text.strip() == "70.0", "Second cell should be '70.0'"
assert first_row[2].text and first_row[2].text.strip() == "86.0", "Third cell should be '86.0'"
assert first_row[3].text and first_row[3].text.strip() == "77.0", "Fourth cell should be '77.0'"
assert first_row[4].text and first_row[4].text.strip() == "8.9", "Fifth cell should be '8.9'"
# 检查倒数第二行数据
second_last_row = tree.xpath('//table/tr[position()=last()-1]/td')
assert len(second_last_row) == 5, "second_last_row should have 5 cells"
assert second_last_row[0].text and second_last_row[0].text.strip() == "Ours (SynText)", "First cell should be 'Ours (SynText)'"
assert second_last_row[1].text and second_last_row[1].text.strip() == "80.68", "Second cell should be '80.68'"
assert second_last_row[2].text and second_last_row[2].text.strip() == "85.40", "Third cell should be '85.40'"
# assert second_last_row[3].text and second_last_row[3].text.strip() == "82.97", "Fourth cell should be '82.97'"
# assert second_last_row[3].text and second_last_row[4].text.strip() == "12.68", "Fifth cell should be '12.68'"
if __name__ == "__main__":
unittest.main()
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