Status: Maintained
Version: 2.2.1
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Extract text from images, videos, QR codes, barcodes and colors with a simple snip mechanism.

• Very quick extraction process (Can set fidelity between fast and accurate as per the Apple Vision framework)
• Can extract text based on all languages or select your language in settings for quicker/accurate captures
• Copies to clipboard automatically, ready to paste
• Post-processing option to execute shell commands when capture is taken. Can use the captured text via [ocr] token within the shell commands
• Customizable hotkeys for the main app functions
• Shows copied text in a floating window, which can be set to auto-hide as well after a custom wait period
• Enable or disable line breaks in the extracted text
• Append multiple text snippets to the clipboard by enabling Append option in Settings, otherwise it only holds one snippet at a time
• Saves captures in History and persists app restarts
• Also saves History to iCloud Drive if enabled
• Launch at login option
• Custom auto-updater that pulls latest release notes and binaries from GitHub Releases

• MacOS 13.0+ (App uses some newer SwiftUI functions/modifiers which don’t work on anything lower than 13.0)

brew install viz

Pearcleaner – An opensource app cleaner with privacy in mind

Sentinel – A GUI for controlling gatekeeper status on your mac

Viz – Utility for extracting text from images, videos, qr/barcodes

PearHID – Remap your macOS keyboard with a simple SwiftUI frontend

—|Welcome to my Go MCU api Project 👋|—

Experiment with GO, learn some new things, with docker / GO / GraphQL.

TODO

• GO
• Gorm
• gqlgen
• Postgres
• graphql
• docker

API-s used for MCU data

• MCU heroku app

Packages used

• github.com/99designs/gqlgen for GraphQl generator
• jwt-go
• Go chi
• gqlparser
• gorm

• Install go
• Install docker / docker-compose
• Set up .env file

with homebrew:

sudo brew install go

with apt-get):

sudo apt-get install golang

install Golang manually or compile it yourself

To run this project, you will need to add the following environment variables to your .env file

PORT= -> your localhost port for the qraphql playground defaults to 8080

DB_HOST= -> localhost
DB_PORT= -> 5432
DB_USER= -> anything
DB_PASSWORD= -> anything
DB_NAME=-> anything

Clone the project

  git clone https://github.com/NorbertRuff/go-mcu-graphql_api

Go to the project directory

  cd go-mcu-graphql_api

Install dependencies

go get

go mod tidy

First start Postgres server with docker compose:

 docker-compose up -d

Finally run the server:

go run server.go

Now navigate to https://localhost:8080 you can see graphiql playground and query the graphql server.

For example:

You can create a user, this command gives back a JWT Token

mutation createuser {
  createUser(
    variables: {
      username: "bob"
      password: "bob"
      email: "bob"
      firstname: "bob"
      lastName: "bob"
    }
  )
}

Login an existing user, this command gives back a JWT Token

mutation {
    login(variables: { username: "bob", password: "bob" })
}

Query all the movies requires: JWT token

query {
    movies
    {
        imdb_id
        movie_id
        title
        overview
        duration
        user {
            user_id
            username
        }
    }

}

You can generate resolvers and types from gql schema configured in gqlgen.yml

go run github.com/99designs/gqlgen generate 

or

go generate 

One great aspect of Golang is, that you can start go applications via go run server.go, but also compile it to an executable with go build server.go. After that you can start the compiled version which starts much faster.

Build your app and synthesize your stacks.

Generates a .build/ directory with the compiled files.

• Basic clean code
• Go project structure
• Go graphgl
• Go pointers
• Go structs, types
• Graphql schema
• Docker compose
• Sql
• Postgres
• Gorm
• Docker Compose

Give a ⭐️ if this project helped you!

• 🔭 I’m currently working on Frontend skills 🚀
• 🎓 I recently completed Codecool Hungary Full-Stack developer bootcamp.
• 👯 I’m looking to collaborate with other Developers 😉
• 🥅 2022 Goals: Learn scss, less css, typescript, front-end frameworks.
• 💬 Ask me about anything, I am happy to help 😉
• 🧗 I try to go beyond and push the bounds.
• ⚡ Fun fact: I love old technology 🙌

💻

🌐

⚙

🎨

TODO

If you have any feedback, please visit the issues page

TODO

• Logo – Gopherize.me

Contributions are always welcome!

See contributing.md for ways to get started.

Please adhere to this project’s code of conduct.

This project implements a neural network model for a binary classification problem using Python and NumPy. The model is trained, validated, and tested on a cleaned heart disease dataset. Key features include gradient descent optimization, early stopping for improved generalization, and evaluation using metrics like accuracy, precision, recall, and F1 score.

The goal of this project is to demonstrate the development of a simple neural network with one hidden layer to classify data into two categories (binary classification). The project focuses on:

• Dividing the dataset into training, validation, and test sets.
• Training the model using gradient descent.
• Employing early stopping to prevent overfitting using validation loss.
• Evaluating the model on unseen test data using standard metrics.

The dataset used is a preprocessed and cleaned heart disease dataset, containing numerical features and a binary target variable (0 or 1).

• Input Features: 13 features representing various health attributes.
• Output Target: Binary classification (0 = No disease, 1 = Disease).
• Data Split:
  • Training Set: 70%
  • Validation Set: 15%
  • Test Set: 15%

The neural network has the following structure:

1. Input Layer: Accepts 13 input features.
2. Hidden Layer: Contains 38 neurons with the sigmoid activation function.
3. Output Layer: 1 neuron with the sigmoid activation function for binary classification.

• Gradient Descent: Optimized weights and biases using gradient descent.
• Early Stopping: Monitored validation loss to stop training when performance stopped improving, with a patience of 2000 epochs.
• Evaluation Metrics: Accuracy, precision, recall, and F1 score calculated on the test set.

After training the model with early stopping, the following results were achieved on the test set:

These results indicate that the model performs well, with a balanced trade-off between precision and recall.

1. Training: Implemented forward and backward propagation using NumPy.
2. Early Stopping: Monitored validation loss and saved the best weights during training.
3. Testing: Evaluated the model on unseen test data and reported comprehensive metrics.

The project uses the following libraries:

• NumPy: For mathematical computations.
• Pandas: For data handling.
• scikit-learn: For preprocessing and splitting datasets.

Install dependencies via:

pip install numpy pandas scikit-learn