from get_bibtex import WorkflowBuilder, CrossRefBibTeX, DBLPBibTeX

# 创建工作流
workflow = WorkflowBuilder()
workflow.add_fetcher(CrossRefBibTeX("your.email@example.com"))
workflow.add_fetcher(DBLPBibTeX())

# 批量处理
papers = [
    "10.1145/3292500.3330919",  # 用 DOI
    "Attention is all you need"   # 用标题
]
results = workflow.get_multiple_bibtex(papers)

from get_bibtex import CrossRefBibTeX

# 单个引用获取
fetcher = CrossRefBibTeX(email="your.email@example.com")
bibtex = fetcher.get_bibtex("10.1145/3292500.3330919")
print(bibtex)

# 从文件读取并保存
workflow.process_file(
    input_path="papers.txt",
    output_path="references.bib"
)

from get_bibtex import CrossRefBibTeX

fetcher = CrossRefBibTeX()
citations = [
    "Machine learning",
    "Deep learning",
    "10.1038/nature14539"
]

for citation in citations:
    bibtex = fetcher.get_bibtex(citation)
    print(bibtex)

from get_bibtex import WorkflowBuilder, CrossRefBibTeX, DBLPBibTeX

workflow = WorkflowBuilder()
workflow.add_fetcher(CrossRefBibTeX())
workflow.add_fetcher(DBLPBibTeX())

# 从文件读取文献列表
workflow.process_file("papers.txt", "bibliography.bib")

from apiModels import CrossRefBibTeX

fetcher = CrossRefBibTeX(email="your.email@example.com")

# FedMSA
bibtex = fetcher.get_bibtex("10.3390/s22197244")
print(bibtex)

# ECA-Net
bibtex = fetcher.get_bibtex("10.1109/cvpr42600.2020.01155")
print(bibtex)

@article{Sun_2022,
  title={FedMSA: A Model Selection and Adaptation System for Federated Learning},
  volume={22},
  ISSN={1424-8220},
  url={http://dx.doi.org/10.3390/s22197244},
  DOI={10.3390/s22197244},
  number={19},
  journal={Sensors},
  publisher={MDPI AG},
  author={Sun, Rui and Li, Yinhao and Shah, Tejal and Sham, Ringo W. H. and Szydlo, Tomasz and Qian, Bin and Thakker, Dhaval and Ranjan, Rajiv},
  year={2022},
  month=sep,
  pages={7244}
}

from apiModels import DBLPBibTeX

fetcher = DBLPBibTeX()

# CBAM
bibtex = fetcher.get_bibtex("CBAM: Convolutional Block Attention Module")
print(bibtex)

@article{DBLP:journals/access/WangZHLL24,
  author       = {Niannian Wang and Zexi Zhang and Haobang Hu and Bin Li and Jianwei Lei},
  title        = {Underground Defects Detection Based on {GPR} by Fusing Simple Linear Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention Module (CBAM)-YOLOv8},
  journal      = {{IEEE} Access},
  volume       = {12},
  pages        = {25888--25905},
  year         = {2024},
  url          = {https://doi.org/10.1109/ACCESS.2024.3365959},
  doi          = {10.1109/ACCESS.2024.3365959}
}

from apiModels import WorkflowBuilder, CrossRefBibTeX, DBLPBibTeX

# 创建工作流
workflow = WorkflowBuilder()
workflow.add_fetcher(CrossRefBibTeX(email="your.email@example.com"))
workflow.add_fetcher(DBLPBibTeX())

# 准备查询列表
queries = [
    "FedMSA: A Model Selection and Adaptation System for Federated Learning",
    "Attention Is All You Need",
    "Non-Local Neural Networks",
    "ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks",
    "CBAM: Convolutional Block Attention Module"
]

# 获取所有引用
results = workflow.get_multiple_bibtex(queries)

# 打印结果
for query, bibtex in results.items():
    print(f"\n查询: {query}")
    print(f"引用:\n{bibtex if bibtex else '未找到'}")

from apiModels import WorkflowBuilder, CrossRefBibTeX, DBLPBibTeX

workflow = WorkflowBuilder()
workflow.add_fetcher(CrossRefBibTeX(email="your.email@example.com"))
workflow.add_fetcher(DBLPBibTeX())

# 处理文件
workflow.process_file("papers.txt", "references.bib")

FedMSA: A Model Selection and Adaptation System for Federated Learning
Attention Is All You Need
Non-Local Neural Networks
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
CBAM: Convolutional Block Attention Module

% Query: FedMSA: A Model Selection and Adaptation System for Federated Learning
% Source: CrossRefBibTeX
@article{Sun_2022, title={FedMSA: A Model Selection and Adaptation System for Federated Learning}, volume={22}, ISSN={1424-8220}, url={http://dx.doi.org/10.3390/s22197244}, DOI={10.3390/s22197244}, number={19}, journal={Sensors}, publisher={MDPI AG}, author={Sun, Rui and Li, Yinhao and Shah, Tejal and Sham, Ringo W. H. and Szydlo, Tomasz and Qian, Bin and Thakker, Dhaval and Ranjan, Rajiv}, year={2022}, month=sep, pages={7244} }

% Query: Attention Is All You Need
% Source: DBLPBibTeX
@inproceedings{DBLP:conf/dac/ZhangYY21,
  author       = {Xiaopeng Zhang and
                  Haoyu Yang and
                  Evangeline F. Y. Young},
  title        = {Attentional Transfer is All You Need: Technology-aware Layout Pattern
                  Generation},
  booktitle    = {58th {ACM/IEEE} Design Automation Conference, {DAC} 2021, San Francisco,
                  CA, USA, December 5-9, 2021},
  pages        = {169--174},
  publisher    = {{IEEE}},
  year         = {2021},
  url          = {https://doi.org/10.1109/DAC18074.2021.9586227},
  doi          = {10.1109/DAC18074.2021.9586227},
  timestamp    = {Wed, 03 May 2023 17:06:11 +0200},
  biburl       = {https://dblp.org/rec/conf/dac/ZhangYY21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

% Query: Non-Local Neural Networks
% Source: CrossRefBibTeX
@article{Xu_2024, title={Adaptive selection of local and non-local attention mechanisms for speech enhancement}, volume={174}, ISSN={0893-6080}, url={http://dx.doi.org/10.1016/j.neunet.2024.106236}, DOI={10.1016/j.neunet.2024.106236}, journal={Neural Networks}, publisher={Elsevier BV}, author={Xu, Xinmeng and Tu, Weiping and Yang, Yuhong}, year={2024}, month=jun, pages={106236} }

% Query: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
% Source: CrossRefBibTeX
@inproceedings{Wang_2020, title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks}, url={http://dx.doi.org/10.1109/cvpr42600.2020.01155}, DOI={10.1109/cvpr42600.2020.01155}, booktitle={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, publisher={IEEE}, author={Wang, Qilong and Wu, Banggu and Zhu, Pengfei and Li, Peihua and Zuo, Wangmeng and Hu, Qinghua}, year={2020}, month=jun, pages={11531–11539} }

% Query: CBAM: Convolutional Block Attention Module
% Source: DBLPBibTeX
@article{DBLP:journals/access/WangZHLL24,
  author       = {Niannian Wang and
                  Zexi Zhang and
                  Haobang Hu and
                  Bin Li and
                  Jianwei Lei},
  title        = {Underground Defects Detection Based on {GPR} by Fusing Simple Linear
                  Iterative Clustering Phash (SLIC-Phash) and Convolutional Block Attention
                  Module (CBAM)-YOLOv8},
  journal      = {{IEEE} Access},
  volume       = {12},
  pages        = {25888--25905},
  year         = {2024},
  url          = {https://doi.org/10.1109/ACCESS.2024.3365959},
  doi          = {10.1109/ACCESS.2024.3365959},
  timestamp    = {Sat, 16 Mar 2024 15:09:59 +0100},
  biburl       = {https://dblp.org/rec/journals/access/WangZHLL24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

pip install get-bibtex

poetry add get-bibtex

fetcher = CrossRefBibTeX(email="your.email@example.com")

workflow = WorkflowBuilder()
workflow.add_fetcher(CrossRefBibTeX())  # 主要源
workflow.add_fetcher(DBLPBibTeX())      # 备用源

from apiModels import GoogleScholarBibTeX

# 初始化 Google Scholar 获取器
fetcher = GoogleScholarBibTeX(api_key="your-serpapi-key")

# 获取引用
bibtex = fetcher.get_bibtex("Deep learning with differential privacy")
print(bibtex)