PostgreSQL

ETL pipeline on movie data using Python and postgreSQL

Movies-ETL ETL pipeline on movie data using Python and postgreSQL Overview This project consisted on a automated Extraction, Transformation and Load pipeline. This ETL extracted movie data from wikipedia, kaggle, and MovieLens to clean it, transform it, and merge it using Pandas. The product was a merged table with movies and ratings loaded to PostgreSQL. Resources Data sources: movies_metadata.csv ratings.csv wikipedia_movies.json Software: Python PostgreSQL Pandas SQLAlchemy Regular Expressions Results Summary The pipeline was created under the following assumptions: I was […]

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Machine Learning

A Brain Tumor Detection and Classification model built using RESNET50 architecture

TumorInsight TumorInsight is a Brain Tumor Detection and Classification model built using RESNET50 architecture. It aims to detect and classify the brain tumours from MRI scans. The detection is done using Image Processing algorithms and classification using Deep learning techniques.The model is also deployed as a web application using Flask framework. Download trained model from here. def main() text = “Hello World!” return text if __name__ == ‘__main__’: text = main() print(text) Lorem ipsum dolor sit amet, consectetur adipiscing elit. […]

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Tool

A simple universal code generation tool in python

Żmija Żmija is a simple universal code generation tool. It is intended to be used as a means to generate code that is both efficient and easily maintainable. It is intended to be used in embdedded systems with limited resources, however it can be used anywhere else as well. Żmija lets you define sections in your code where code is generated automatically in accordance to a Python script that you provide. Such a section typically looks like this: /* ~ZMIJA.GENERATOR: […]

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Timer

A basic educational Python timer used to learn python and software testing libraries

minutaria minutaria is a basic educational Python timer. The project is educational, it aims to teach myself programming, python programming, python’s stdlib, tools (pdb, venv, mypy…) and ecosystem, development best pratices, git and some software testing libraries or frameworks. The project is separed in 3 parts: a module as a library a CLI utility a simple ncurses GUI using parts of the module, also usable with CLI a GTK GUI using the full module and so also usable with CLI […]

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Machine Learning

A set of reliable implementations of reinforcement learning algorithms in PyTorch

Stable Baselines3 Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a […]

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Framework

A Python web framework built for super fast app development

appier Appier is an object-oriented Python web framework built for super fast app development. It’s as lightweight as possible, but not too lightweight. It gives you the power of bigger frameworks, without their complexity. Your first app can be just a few lines long: import appier class HelloApp(appier.App): @appier.route(“/”, “GET”) def hello(self): return “Hello World” HelloApp().serve() The same app using the async/await syntax (Python 3.5+) for async execution reads pretty much the same: import appier class HelloApp(appier.App): @appier.route(“/”, “GET”) async […]

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App

A Python application to predict what is cooking

ez-cuisine-classifier A Python application to predict what is cooking. Environment Python 3.7 Windows 10 Install python -m venv venv .venvScriptsactivate python -m pip install -U pip setuptools pip install -r requirements-dev.txt Data Source The training data is from kaggle’s Recipe Ingredients Dataset. Demo GitHub https://github.com/zehengl/ez-cuisine-classifier    

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Tool

A Tool For Direction Brute Force And Gathering Direction by Dork

XDGe XDGe It’s Tool For Direction Brute Force And Gathering Direction by Dork IF You Want GoogleDork Function? def GOscan(URLTARGET,DORKUSERFILE): DOKERLIST = [] GODOKERFILE = open(DORKUSERFILE,”r”) for CONGODOKER in GODOKERFILE: DOKERLIST.append(CONGODOKER) for CONDOKERLIST in range(len(DOKERLIST)): DORKTARGET = DOKERLIST[CONDOKERLIST].replace(” “,”+”).replace(“n”,””) SETGODORK = “site:{URLTARGET}+{DORKTARGET}”.format(URLTARGET=URLTARGET, DORKTARGET=DORKTARGET) SETGOWEBS = ‘https://www.google.com/search?q=’+SETGODORK+’&client=firefox-b-d&start=0’ REQGODORK = urllib.request.Request(SETGOWEBS) REQGODORK.add_header(‘User-Agent’, ‘Mozilla/7000.0 XAR’) REQGODORK.add_header(‘Accept’, ‘text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8’) REQGODORK.add_header(‘Accept-Language’, ‘en-US,en;q=0.8’) #——————————————— REQGOOGLE = urllib.request.urlopen(REQGODORK, timeout=10) RESGODORK = REQGOOGLE.read() REQGOOGLE.close() CLEARREQD = re.findall(‘

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Machine Learning

A Katherine Crowson VQGAN+CLIP derived Google colab notebook

VQGAN-CLIP A repo for running VQGAN+CLIP locally. This started out as a Katherine Crowson VQGAN+CLIP derived Google colab notebook. Some example images: Environment: Tested on Ubuntu 20.04 GPU: Nvidia RTX 3090 Typical VRAM requirements: 24 GB for a 900×900 image 10 GB for a 512×512 image 8 GB for a 380×380 image Still a work in progress – I’ve not actually tested everything yet 🙂 Example set up using Anaconda to create a virtual Python environment with the prerequisites: conda […]

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