https://github.com/davidteather/tinder-bot
Tinder Automation Bot
https://github.com/davidteather/tinder-bot
tinder tinder-automation tinder-bot
Last synced: about 2 months ago
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Tinder Automation Bot
- Host: GitHub
- URL: https://github.com/davidteather/tinder-bot
- Owner: davidteather
- License: mit
- Created: 2020-12-29T23:15:53.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-01-04T17:22:08.000Z (almost 5 years ago)
- Last Synced: 2024-05-02T05:06:47.604Z (over 1 year ago)
- Topics: tinder, tinder-automation, tinder-bot
- Language: Python
- Homepage: https://youtu.be/OnWH1GnzyNE
- Size: 24.5 MB
- Stars: 31
- Watchers: 6
- Forks: 9
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# tinder-bot
This is a repository to use AI to automate tinder stuff. AI part heavily based on [Auto-Tinder](https://github.com/joelbarmettlerUZH/auto-tinder), but it doesn't use the tinder API directly so it's more visual for the accompanying [YouTube Video](https://youtu.be/OnWH1GnzyNE).
# Demo/YouTube Video
[](https://youtu.be/OnWH1GnzyNE)
## Important Notes
I do recommend just following [Auto-Tinder](https://github.com/joelbarmettlerUZH/auto-tinder) if you don't want the visual aspect of swiping on a selenium instance because it complicates things a lot more. However, you can follow this messy guide to use this semi-messy code.
## Part 1 - Data Aggregation
We need photos to train the AI on, so first off, you'll need a Tinder account linked to a google account, you'll need to set `google_password` and `google_username` as environment variables that correspond to your google credentials. Then you can run `python extract_profiles.py` , it will log you into google and prompt you `please log into google` hit enter after you finish 2 factor authentication.
The script will save profiles to data.json. Once you're satisfied with the amount of profiles you've extracted run `python remove_dupes.py` to make sure that you don't have duplicate profiles. You can also run `python stats.py` if you want to see some basic statistics on your dataset, however this isn't required.
## Part 2 - Downloading Images
Run `python image_downloader.py` this will download all the images in data.json
## Part 3 - Labeling Data
You'll now need to classify a like or dislike or not a person for all the photos you downloaded. Run `python image_classifier.py` , left click is a like, right click is a dislike, and middle mouse is if the photo isn't a person. (If you have non-people in your training data it might mess up the AI)
## Part 4 - Pre-Processing
Run `python prepare_data.py` this will crop and convert images to gray-scale
## Part 5 - Training
Now you have to train the AI, **MAKE SURE ALL THE DIRECTORIES EXIST THAT ARE REFERENCED IN THIS COMMAND** (I made this mistake after 4 hours of training :( ), you can mess around with some of the arguments as to get a good model your training data is different from mine.
```
python retrain.py --bottleneck_dir=tf/training_data/bottlenecks --saved_model_dir=tf/training_data/inception --summaries_dir=tf/training_data/summaries/basic --output_graph=tf/training_output/retrained_graph.pb --output_labels=tf/training_output/retrained_labels.txt --image_dir=./images/classified --how_many_training_steps=25000 --testing_percentage=20 --learning_rate=0.0005
```
## Part 6 - Using The AI
Follow the steps for environment variables in part 1, but run `python use_model.py` and hopefully it'll work for you!
There are some directories that don't auto generate and I forget what directories they are as they were defined in [Auto-Tinder](https://github.com/joelbarmettlerUZH/auto-tinder). I also probably forgot to include something here that's critical to getting this working.