Send text to girlfriend in the morning

Last update: Oct 25, 2022

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Overview

Girlfriend Text

Send text to girlfriend (or really anyone with a phone number) in the morning

1. Configure your settings in `utils.py`.

phone_number = "phone number" # enter the target phone number inside quotation marks. use the format "+15555555555"
message = ["Good Morning Babe", "Good morning my love", "Good morning cutie"] # just make sure the message is inside quotation marks. An example is "Did you sleep well?"
scheduled_time = "08:00" # make sure the hour has 2 digits (24-hour standard).

Note: If you want to send a message everyday at 8:45 AM, you will need to set scheduled_time = "08:45" in utils.py

2. Install requirements: `pip install -r requirements.txt`

3. Run the script with: `python3 main.py`

Owner

Paras Adhikary

Entrepreneur, video creator, and software engineer.

GitHub Repository

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Send text to girlfriend in the morning

Related tags

Overview

Girlfriend Text

1. Configure your settings in `utils.py`.

2. Install requirements: `pip install -r requirements.txt`

3. Run the script with: `python3 main.py`

Owner

Paras Adhikary

Official Implementation of "Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras"

Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

Evaluation suite for large-scale language models.

CVPR 2021 Official Pytorch Code for UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

A package, and script, to perform imaging transcriptomics on a neuroimaging scan.

Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Parameterized Explainer for Graph Neural Network

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Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

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Pre-trained Deep Learning models and demos (high quality and extremely fast)

Send text to girlfriend in the morning

Related tags

Overview

Girlfriend Text

1. Configure your settings in utils.py.

2. Install requirements: pip install -r requirements.txt

3. Run the script with: python3 main.py

Owner

Paras Adhikary

Official Implementation of "Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras"

Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)

Evaluation suite for large-scale language models.

CVPR 2021 Official Pytorch Code for UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

A package, and script, to perform imaging transcriptomics on a neuroimaging scan.

Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Parameterized Explainer for Graph Neural Network

Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR)

Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

Official implementation of the paper 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution' in CVPR 2022

A Pytorch Implementation for Compact Bilinear Pooling.

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

StarGAN - Official PyTorch Implementation (CVPR 2018)

Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

MAT: Mask-Aware Transformer for Large Hole Image Inpainting

Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.

Pre-trained Deep Learning models and demos (high quality and extremely fast)

1. Configure your settings in `utils.py`.

2. Install requirements: `pip install -r requirements.txt`

3. Run the script with: `python3 main.py`