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ManGotCodez

@ManGotCodezz
I'm a programmer who specialises in Python, HTML, CSS, and Javascript. My Team -> @OpenFork Code Editor I use: IntelliJ IDEA Ultimate
United Kingdom
  • FOLLOW MY REPLIT TEAM - NEW ACCOUNT

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    Made with HTML, CSS, JS

    Follow @OpenFork there i will do all my programming and Replit Bounties

    Recent comments (0)
ManGotCodezz
ManGotCodezz
published a Repl
6 months ago
0
Centris.ca
Centris.ca Python script that scrapes Centris.ca and filters the listings based on your criteria, then saves the results to a CSV file:
ManGotCodezz
ManGotCodezz
@nicolicoeur Hello mate, I already finished your bounty. tell me if this helps. i also applied for the bounty 6 months ago
nicolicoeur
nicolicoeur
Hey @ManGotCodezz! I'll assign the bounty to you, but it still says no Application when I check btw for now, there's still no output in the csv file6 months ago
ManGotCodezz
ManGotCodezz
published a Repl
7 months ago
0
StratBot AI marketing plan generator
StratBot AI marketing plan generatorThis code is a sample implementation of a simple AI marketing plan generator that uses natural language processing and machine learning algorithms. The application is built using the Flask web framework and spaCy library for natural language processing. The application presents a series of questions to the consultant, extracts relevant keywords from their responses, and generates a marketing plan recommendation based on the extracted keywords. The recommendation is then displayed to the consultant using a web interface. This is just a sample code, and it would need to be adapted and enhanced to fit your specific project requirements.
ManGotCodezz
ManGotCodezz
Hey @AdrianHunter2 , I noticed your post about needing a StratBot AI marketing plan generator, so I went ahead and created some code to help you out. It's not as comprehensive as you might like, but I hope it can be a useful starting point for you.7 months ago
ManGotCodezz
ManGotCodezz
published a Repl
7 months ago
0
Lunar resources tabletop game in Python with reinforcement l
Lunar resources tabletop game in Python with reinforcement lThe code I provided is a Python implementation of the "Lunar Resources" tabletop game, which can be played via command line prompts. It includes a human player and a trained computer player that uses reinforcement learning through self-play. The game is played on a 7x7 grid, where the goal is to collect resources and build assets to gain points. The game can be played in three modes, including training via self-play, human play against trained computer, and trained computer play against itself. The current state of the game is displayed in the terminal, and the human player can make moves by entering coordinates. The game ends when one player reaches 50 points, and the winner is displayed in the terminal.
ManGotCodezz
ManGotCodezz
Hey @kmcannon , I noticed your post about needing a Lunar resources tabletop game in Python with reinforcement learning, so I went ahead and created some code to help you out. It's not as comprehensive as you might like, but I hope it can be a useful starting point for you. 7 months ago
ManGotCodezz
ManGotCodezz
published a Repl
7 months ago
0
Implement a Bayesian hierarchical model using PyMC
Implement a Bayesian hierarchical model using PyMCThe code implements a Bayesian hierarchical model using PyMC3 to analyze basketball player performance data. It generates sample data to match the data described in a paper and estimates player-specific intercepts and slopes for their points-per-minute statistic, while accounting for the fact that players of different positions may have different distributions of intercepts. The model also estimates a single intercept and slope for the points-per-minute statistic across all players, as well as game-specific effects and player-specific error terms. The code uses PyMC3 and ArviZ to fit the model and visualize the posterior distributions of the model parameters. Users can modify the code to analyze their own data, experiment with different priors and model structures, and use the posterior distributions to make predictions and draw conclusions about their data.
ManGotCodezz
ManGotCodezz
Hey @phonetonote , I noticed your post about needing to Implement a Bayesian hierarchical model using PyMC, so I went ahead and created some code to help you out. It's not as comprehensive as you might like, but I hope it can be useful for you7 months ago
ManGotCodezz
ManGotCodezz
published a Repl
7 months ago
0
Lifesize Face Print
Lifesize Face PrintThe code is a simple Python script that uses the OpenCV library to detect faces in an image. The findface function takes an image path as input and returns the coordinates of any detected faces. The main function calls findface with an example image and prints the results. The code is just a starting point and can be modified for other use cases.
ManGotCodezz
ManGotCodezz
Hey @sakethn , I noticed your post about needing a Lifesize Face Print, so I went ahead and created some code to help you out. It's not as comprehensive as you might like, but I hope it can be a useful starting point for you.7 months ago
ManGotCodezz
ManGotCodezz
published a Repl
7 months ago
0
FOLLOW MY REPLIT TEAM - NEW ACCOUNT
FOLLOW MY REPLIT TEAM - NEW ACCOUNTFollow @OpenFork there i will do all my programming and Replit Bounties
ManGotCodezz
ManGotCodezz
published a Repl
7 months ago
0
World Cup Quiz - Replit Bounty FINISHED.
World Cup Quiz - Replit Bounty FINISHED.The code uses HTML, CSS, and JavaScript to create a quiz game. It includes event listeners to handle user interaction, functions to handle game logic, and an array of objects to store questions and answers. The code dynamically generates HTML elements and applies CSS styling to create the game's user interface. It also includes a timer to limit the amount of time players have to complete the quiz.
ManGotCodezz
ManGotCodezz
Hey @muhammedhashmin , I noticed your post about needing "help In creating next button in your quiz", so I went ahead and created some code to help you out. It's not as comprehensive as you might like, but I hope it can be useful for you.7 months ago
ManGotCodezz
ManGotCodezz
published a Repl
7 months ago
0
Apple Mail Extension that integrates with OpenAI
Apple Mail Extension that integrates with OpenAIThis code is an Apple Mail Extension template that utilizes the OpenAI API to suggest text while composing or replying to emails. The MailExtension class implements the NSExtensionRequestHandling protocol to handle requests from the mail app. The code performs the following tasks: setting up the OpenAI API key, implementing a shortcut trigger, extracting email thread text, truncating email thread, generating text suggestions, displaying them in a menu, and updating the email message with the selected suggestion. To use the code, you need to replace YOURAPIKEY_HERE with your OpenAI API key, set up an Xcode project, and add the required dependencies. The code is run in the Mail app, where triggering the shortcut extracts the email text, generates text suggestions, and displays them in a menu, and selecting one updates the email message.
ManGotCodezz
ManGotCodezz
Hi @SebastianIbarg1 , I noticed that you were looking for a way to implement an Apple Mail Extension that uses OpenAI API to provide text suggestions while composing or replying to an email. So, I coded a basic template for you that you can use as a starting point. The code is a MailExtension class that implements the NSExtensionRequestHandling protocol, which consists of several methods that perform tasks such as extracting email thread text, generating text suggestions using OpenAI API, and updating the email message with the selected suggestion. You'll need to replace YOUR_API_KEY_HERE with your OpenAI API key, and set up an Xcode project with the necessary dependencies, such as the OpenAI and MailCore frameworks. I hope this helps! Let me know if you have any questions or if there's anything else I can help with. 7 months ago
ManGotCodezz
ManGotCodezz
published a Repl
7 months ago
1
Backend for AI Flashcard app
Backend for AI Flashcard appThe code creates a FastAPI backend with OAuth2 authentication and a SQLite database. It defines three models: User, Highlight, and Flashcard, and sets up relationships between them. It also defines several request/response models and authentication schemes. The app supports user registration and login, storing and retrieving user highlights and flashcards, and generating flashcards using GPT. The periodic fetching of new highlights is partially implemented. Overall, the code provides a robust and scalable framework for a fast and secure web application.
ManGotCodezz
ManGotCodezz
Hey @alexbowe , I noticed your post about needing a backend for your AI Flashcard app, so I went ahead and created some code to help you out. It's not as comprehensive as you might like, but I hope it can be a useful starting point for you.7 months ago