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zippy's Issues

[BUG] Zippy won't run on Windows

Describe the bug
Running on Windows 11 Python 3.10.9 the ai-generated.txt file cannot be read and the program crashes.

To Reproduce

image

Expected behavior
Either the README should specify the environment in which the tool works (perhaps a linux host is required) or the read of ai-generated.txt should be fixed to ignore these chars or process them differently.

Desktop (please complete the following information):

  • OS: [e.g. iOS]
  • Browser [e.g. chrome, safari]
  • Version [e.g. 22]

ImportError: cannot import name 'Zippy' from 'zippy.zippy' (/kaggle/working/zippy/zippy/__init__.py)

Describe the bug

  1. Install zippy in kaggle using the following command.
!pip install --upgrade numpy==1.25.2
!python3 -c "import numpy as np; print('> NumPy version: {}'.format(np.__version__))"

!git clone https://github.com/thinkst/zippy.git && cd zippy && pip install -r requirements.txt &&python3 setup.py build && python3 setup.py install

The output information is as follows:

Collecting numpy==1.25.2
  Downloading numpy-1.25.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 18.2/18.2 MB 45.7 MB/s eta 0:00:0000:0100:01
Installing collected packages: numpy
  Attempting uninstall: numpy
    Found existing installation: numpy 1.23.5
    Uninstalling numpy-1.23.5:
      Successfully uninstalled numpy-1.23.5
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.7 which is incompatible.
apache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 1.25.2 which is incompatible.
momepy 0.6.0 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.
numba 0.57.1 requires numpy<1.25,>=1.21, but you have numpy 1.25.2 which is incompatible.
pymc3 3.11.5 requires numpy<1.22.2,>=1.15.0, but you have numpy 1.25.2 which is incompatible.
pymc3 3.11.5 requires scipy<1.8.0,>=1.7.3, but you have scipy 1.11.2 which is incompatible.
tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.25.2 which is incompatible.
woodwork 0.26.0 requires numpy<1.25.0,>=1.22.0, but you have numpy 1.25.2 which is incompatible.
ydata-profiling 4.3.1 requires numpy<1.24,>=1.16.0, but you have numpy 1.25.2 which is incompatible.
ydata-profiling 4.3.1 requires scipy<1.11,>=1.4.1, but you have scipy 1.11.2 which is incompatible.
Successfully installed numpy-1.25.2
> NumPy version: 1.25.2

Cloning into 'zippy'...
remote: Enumerating objects: 829, done.
remote: Counting objects: 100% (222/222), done.
remote: Compressing objects: 100% (135/135), done.
remote: Total 829 (delta 130), reused 152 (delta 82), pack-reused 607
Receiving objects: 100% (829/829), 46.75 MiB | 25.33 MiB/s, done.
Resolving deltas: 100% (343/343), done.
Updating files: 100% (319/319), done.
Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from -r requirements.txt (line 2)) (1.25.2)
Collecting brotli (from -r requirements.txt (line 4))
  Downloading Brotli-1.1.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.0 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.0/3.0 MB 29.0 MB/s eta 0:00:00a 0:00:01
Installing collected packages: brotli
Successfully installed brotli-1.1.0
/opt/conda/lib/python3.10/site-packages/setuptools/_distutils/cmd.py:66: SetuptoolsDeprecationWarning: setup.py install is deprecated.
!!

        ********************************************************************************
        Please avoid running ``setup.py`` directly.
        Instead, use pypa/build, pypa/installer or other
        standards-based tools.

        See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details.
        ********************************************************************************

!!
  self.initialize_options()
/opt/conda/lib/python3.10/site-packages/setuptools/_distutils/cmd.py:66: EasyInstallDeprecationWarning: easy_install command is deprecated.
!!

        ********************************************************************************
        Please avoid running ``setup.py`` and ``easy_install``.
        Instead, use pypa/build, pypa/installer or other
        standards-based tools.

        See https://github.com/pypa/setuptools/issues/917 for details.
        ********************************************************************************

!!
  self.initialize_options()

  1. I can use the following command to work properly
    import zippy.zippy
  2. However, running the following command,
    from zippy.zippy import Zippy, EnsembledZippy, PRELUDE_STR, LzmaLlmDetector, BrotliLlmDetector, ZlibLlmDetector, CompressionEngine

The error message is as follows:

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
Cell In[4], line 1
----> 1 from zippy.zippy import Zippy, EnsembledZippy, PRELUDE_STR, LzmaLlmDetector, BrotliLlmDetector, ZlibLlmDetector, CompressionEngine

ImportError: cannot import name 'Zippy' from 'zippy.zippy' (/kaggle/working/zippy/zippy/__init__.py)
  1. System version in kaggle
!uname -a
Linux eb546a198970 5.15.133+ #1 SMP Sat Nov 4 11:53:48 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux

How would it be possible to help with the detection of more models?

Hello,

I saw the post on beehaw.org about this project and started playing around but I've gotten false negatives when using a 7B model. How would it be possible to improve detection so I could try and get more accurate results? And is it possible without lessening detection on larger models such as ChatGPT?

I've gotten the following results using the 7B model:
Silly generation: Human 0.60
Serious generation: Human 0.039
My own text: Human 0.69

Scoring formula normalization coefficient

Hi,

in the README.md you are mentioning a paper which applies the same technique for anomay detection. However you do not apply the same formula in your code.
From what I understood the normalization factors are different.

I wonder if you had any reason to do so, and would be interested to discuss more about it, since it seems to me that the formula in the original paper is more suited - but maybe you found some empirical optimization.

Thank you, and again congratulations for the project and the presentation at Hacklu

Pypi registration

Is your feature request related to a problem? Please describe.
Hey there! Can we have library of zippy?
Describe the solution you'd like
just a clean pip3 install zippy/xyz would be smoother :)

Describe alternatives you've considered
none
Additional context
Please consider implementing it to https://pypi.org/

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