WARNING Error during upload. Retry with the --verbose option for more details.

[tawsif]

Is there an existing issue for this?

• I have searched the existing issues (open and closed), and could not find an existing issue

What keywords did you use to search existing issues?

upload, error, password, api

What operating system(s) are you using?

Windows 11

If you selected 'Other', describe your Operating System here

No response

What version of Python are you running?

python 3.8.X

How did you install twine? Did you use your operating system's package manager or pip or something else?

pip install twine

What version of twine do you have installed (include the complete output)

twine version 4.0.2 (importlib-metadata: 6.1.0, keyring: 23.13.1, pkginfo: 1.9.6, requests: 2.27.1, requests-toolbelt: 0.9.1, urllib3: 1.26.7)

Which package repository are you using?

https://upload.pypi.org/legacy/

Please describe the issue that you are experiencing

I am providing my API token that have access to all of my project. Yet, it does not let me upload my package.

Please list the steps required to reproduce this behaviour

1. I built package and wheels locally
2. Have later used twine upload --repository pypi --username token --password <token> dist/* command
3. returns error " Error during upload. Retry with the --verbose option for more details`

Please include the PKG-INFO file contents from the artifact you're attempting to upload

Metadata-Version: 2.1
Name: cellmean
Version: 1.1.0
Author: TAWSIF AHMED
Author-email: [e-mail]
Description-Content-Type: text/markdown

# Function Usage Guide

This guide provides instructions on how to use two sets of image processing functions in Python. The first set focuses on cell segmentation, while the second set includes functions for denoising images and converting them to grayscale.

## Cell Segmentation Functions

### Required Libraries
- `os`
- `numpy`
- `skimage`
- `sklearn`
- `matplotlib`

### Functions:
1. **cell_segment(image_path, num_clusters=4)**: Segments cells in an image using K-means clustering.
    - `image_path`: Path to the input image file.
    - `num_clusters`: Number of clusters for K-means clustering (default is 4).
    - Returns: Segmented image array.

2. **img_save(segmented_image, save_path, image_path)**: Saves the segmented image to a specified location.
    - `segmented_image`: Segmented image array.
    - `save_path`: Path to the folder where the segmented image will be saved.
    - `image_path`: Path to the original image file.

3. **plot_img(segmented_image)**: Plots the segmented image.
    - `segmented_image`: Segmented image array.

4. **cell_folder(input_folder, output_folder, num_clusters=4)**: Segments cells in all images within a folder and saves the results.
    - `input_folder`: Path to the folder containing input images.
    - `output_folder`: Path to the folder where segmented images will be saved.
    - `num_clusters`: Number of clusters for K-means clustering (default is 4).

## Image Denoising and Grayscale Conversion Functions

### Required Libraries
- `numpy`
- `matplotlib`
- `skimage`
- `PIL`
- `os`

### Functions:
1. **denoise_images(input_folder, result_folder=None)**: Denoises images in a folder using Non-local Means Denoising.
    - `input_folder`: Path to the folder containing input images.
    - `result_folder`: Path to the folder where denoised images will be saved (optional).

2. **img_to_gray(input_folder, result_folder=None)**: Converts images to grayscale.
    - `input_folder`: Path to the folder containing input images.
    - `result_folder`: Path to the folder where grayscale images will be saved (optional).

## Example Usage:

from cellmean import cell_segment, img_save, plot_img, cell_folder, denoise_images, img_to_gray

# Cell Segmentation
segmented_image = cell_segment('input_image.jpg')
plot_img(segmented_image)
img_save(segmented_image, 'output_folder', 'input_image.jpg')
cell_folder('input_images_folder', 'output_images_folder', num_clusters=5)

# Image Denoising and Grayscale Conversion
denoise_images('input_images_folder', result_folder='denoised_images')
img_to_gray('input_images_folder', result_folder='gray_images')

A redacted version of your .pypirc file

[pypi]
  username = __token__
  password = <api>

Anything else you'd like to mention?

I have the .pypirc on my home directory but not sure how to use it

[Ian Stapleton Cordasco]

There's not enough information here to begin helping you. The message itself isn't enough. Did you rerun with --verbose and can you share the entire output?

[tawsif]

thanks for your comment! I actually figured it out.

the correct process wasn't outlined in the most immediate documentation and tutorials.

after
twine upload dist/*

username: token
password:

now. I figured it out!