Count GitHub Stars per Day ⭐️

Track the daily growth of GitHub stars over a specified time period to gauge the popularity and engagement trends of various open-source repositories.

📌 Requirements

This script utilizes the PyGitHub library to interface with the GitHub REST API. This allows interaction with GitHub resources like repositories, user profiles, and organizations directly within your Python applications.

Installation

Run the following command in your terminal to install the necessary dependencies listed in the requirements.txt file:

pip install -r requirements.txt # Install required libraries

🚀 Usage

Before running the script, you need to update the TOKEN variable in count_stars.py (line 15) with your personal GitHub access token. Once the token is set, execute the script using Python:

# Ensure your GitHub access token is set in the TOKEN variable before running.
python count_stars.py

📈 Result

The script outputs the star count for the specified repositories over the defined period. Below is example output showing the star count increase over 30 days ending May 2nd, 2022:

Counting stars for last 30.0 days from 02 May 2022

ultralytics/yolov5                      1572 stars  (52.4/day)  :   6%|▌         | 1572/25683 [00:16<04:15, 94.53it/s]
facebookresearch/detectron2             391 stars   (13.0/day)  :   2%|▏         | 391/20723 [00:04<03:56, 85.86it/s]
deepmind/deepmind-research              165 stars   (5.5/day)   :   2%|▏         | 165/10079 [00:01<01:50, 89.52it/s]
aws/amazon-sagemaker-examples           120 stars   (4.0/day)   :   2%|▏         | 120/6830 [00:02<02:16, 49.17it/s]
awslabs/autogluon                       127 stars   (4.2/day)   :   3%|▎         | 127/4436 [00:01<01:00, 71.45it/s]
microsoft/LightGBM                      122 stars   (4.1/day)   :   1%|          | 122/13730 [00:01<03:10, 71.54it/s]
openai/gpt-3                            95 stars    (3.2/day)   :   1%|          | 95/11225 [00:01<03:34, 52.00it/s]
apple/turicreate                        40 stars    (1.3/day)   :   0%|          | 40/10676 [00:00<02:24, 73.59it/s]
apple/coremltools                       41 stars    (1.4/day)   :   2%|▏         | 41/2641 [00:00<00:46, 56.00it/s]
google/automl                           55 stars    (1.8/day)   :   1%|          | 55/4991 [00:00<01:25, 57.53it/s]
google-research/google-research         548 stars   (18.3/day)  :   2%|▏         | 548/23087 [00:07<05:11, 72.37it/s]
google-research/vision_transformer      279 stars   (9.3/day)   :   6%|▌         | 279/5043 [00:02<00:49, 95.93it/s]
google-research/bert                    283 stars   (9.4/day)   :   1%|          | 283/31066 [00:03<07:01, 73.11it/s]
NVlabs/stylegan3                        158 stars   (5.3/day)   :   4%|▍         | 158/4045 [00:01<00:44, 86.41it/s]
Tencent/ncnn                            278 stars   (9.3/day)   :   2%|▏         | 278/14440 [00:03<02:41, 87.55it/s]
Megvii-BaseDetection/YOLOX              273 stars   (9.1/day)   :   4%|▍         | 273/6286 [00:02<01:04, 92.53it/s]
PaddlePaddle/Paddle                     239 stars   (8.0/day)   :   1%|▏         | 239/18086 [00:02<03:33, 83.73it/s]
rwightman/pytorch-image-models          772 stars   (25.7/day)  :   4%|▍         | 772/18169 [00:08<03:21, 86.24it/s]
streamlit/streamlit                     375 stars   (12.5/day)  :   2%|▏         | 375/18834 [00:03<03:07, 98.67it/s]
explosion/spaCy                         234 stars   (7.8/day)   :   1%|          | 234/23249 [00:02<03:47, 101.24it/s]
PyTorchLightning/pytorch-lightning      407 stars   (13.6/day)  :   2%|▏         | 407/18246 [00:04<03:02, 97.83it/s]
ray-project/ray                         545 stars   (18.2/day)  :   3%|▎         | 545/20228 [00:05<03:03, 107.33it/s]
fastai/fastai                           136 stars   (4.5/day)   :   1%|          | 136/22202 [00:01<04:28, 82.22it/s]
AlexeyAB/darknet                        248 stars   (8.3/day)   :   1%|▏         | 248/18993 [00:02<03:40, 84.84it/s]
pjreddie/darknet                        201 stars   (6.7/day)   :   1%|          | 201/22651 [00:02<05:13, 71.62it/s]
WongKinYiu/yolor                        92 stars    (3.1/day)   :   6%|▌         | 92/1559 [00:01<00:16, 87.69it/s]
wandb/client                            66 stars    (2.2/day)   :   2%|▏         | 66/3853 [00:00<00:46, 82.16it/s]
Deci-AI/super-gradients                 74 stars    (2.5/day)   :  19%|█▉        | 74/380 [00:00<00:03, 96.71it/s]
neuralmagic/sparseml                    105 stars   (3.5/day)   :  11%|█         | 105/947 [00:01<00:08, 101.97it/s]
mosaicml/composer                       247 stars   (8.2/day)   :  19%|█▉        | 247/1306 [00:02<00:10, 104.76it/s]
nebuly-ai/nebullvm                      205 stars   (6.8/day)   :  20%|█▉        | 205/1045 [00:02<00:08, 97.46it/s]
Done in 125.7s

💡 Contribute

Contributions are the lifeblood of the open-source community, and we greatly appreciate your input! Whether it's bug fixes, feature suggestions, or documentation improvements, every contribution helps.

Please see our Contributing Guide for detailed instructions on how to get involved. We also encourage you to fill out our Survey to share your feedback. Thank you 🙏 to everyone who contributes!

📝 License

Ultralytics provides two licensing options to accommodate different use cases:

• AGPL-3.0 License: Ideal for students and enthusiasts, this OSI-approved open-source license promotes collaboration and knowledge sharing. See the LICENSE file for details.
• Enterprise License: Designed for commercial applications, this license allows for the integration of Ultralytics software and AI models into commercial products and services. For more information, visit Ultralytics Licensing.

📬 Contact Us

If you encounter bugs, have feature requests, or wish to contribute, please visit GitHub Issues. For broader discussions and questions about Ultralytics projects, join our vibrant community on Discord!