355 skills found · Page 7 of 12
jgravelle / GroqumentsGroquments is a simple demonstration project showcasing how easily PocketGroq can help developers integrate Groq's powerful AI capabilities into their Python projects. This project provides a basic implementation of an AI-powered document field mapping tool.
tegnike / Codebase ExplainerA Python script that leverages AI to analyze project codebases, generating file structures and descriptions
muhammedsaadi99 / Context PackerA Python script that bundles your project's source code into a single text file. Perfect for providing full context to AI assistants like ChatGPT, Gemini, and Claude.
DaveCoDev / Not Again AIA Python package designed to once and for all collect all the little things that come up over and over again in AI projects.
G0razd / Lalal.AI DownloaderThis project provides a Python script for downloading audio segments from Lalal.ai and merging them into a single audio file.
samballington / CodeWiseCodeWise is an AI-powered coding copilot that indexes your entire project (code, configs, docs), understands its structure through AST analysis, and answers questions with a hybrid vector + keyword search engine. It runs in a four-container stack—Next.js UI, FastAPI backend, Python indexer, and secure file server
KaranRajiwade / Invoice Processing SystemA Python-based system that automates invoice processing from emails and PDFs. It extracts key details, uses AI for supplier insights, stores data in SQLite, and provides a REST API. The project supports Docker deployment for easy scalability. 🚀 Let me know if you want a more concise or detailed version! 🔥
abdulsalam-s-ghaleb / Web Based Application E Commerce System With Object Recognition Using Neural Network AIFinal year project: E-Commerce system with object recognition using Neural Network (AI) using Python, Django, JavaScript, MySQL, HTML, CSS, Bootstrap. The system gives the ability to the user to register and login to the system as well as search the products by typing or image at a search bar of website. In addition, the sellers can access the system to add the new products, edit and delete with small dashboard
MasterAffan / OptiFitOptiFit is an AI-powered fitness application that analyzes exercise form through video processing, provides personalized workout guidance via AI chat, and tracks fitness progress across multiple platforms using Flutter and Python Flask. Learn more about the project
rishabkumar7 / Cr Demo PythonA demo project integrating Twilio Voice and Open AI using Python and FastAPI.
abhi-programmer / Jarvis Voice AssistantTHIS PYTHON AI PROJECT IS THAT HOW TO MAKE A VIRTUAL ASSISTANT LIKE GOOGLE ASSISTANT.THIS PROJECT IS PROPERLY VOICE ACTIVATED DESKTOP ASSISTANT.IN THIS PROJECT I AM USING BUNCH OF SOME IMPORTANT MODULES TO RECOGNIZE OUR VOICE AND ACCORDING TO OUR VOICE ASSISTANT WILL OPEN TASK WHICH WE WANT.
ShiZhuming / StyleTransferA real-time image style transfer project based on AdaIn and VGG network , python flask as frontend and HTML5 as backend , project of AI Introduction, a course of Peking Unicersity
jerryjames2001 / Edu VisionEduVision is a powerful AI-based study platform built for students and academic project seekers. It digitizes handwritten notes using OCR (Azure), summarizes them with AI, and generates quiz questions to aid in exam preparation. Ideal for final-year projects, EduVision includes a full-featured frontend (React + Tailwind), intelligent Python/Node.j
mthd98 / Project Algorithm For A Dog Identification AppProject Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
Perpetue237 / Agentsculptoragentsculptor is an experimental AI-powered development agent designed to analyze, refactor, and extend Python projects automatically. It uses an OpenAI-like planner–executor loop on top of a vLLM backend, combining project context analysis, structured tool calls, and iterative refinement. It has only been tested with gpt-oss-120b via vLLM.
annikalang / Pre Trained Image Classifier To Identify Dog BreedsFirst project of AI-programming with Python Nanodegree by Udacity
ocatak / TrustworthyaiTrustworthy AI: From Theory to Practice book. Explore the intersection of ethics and technology with 'Trustworthy AI: From Theory to Practice.' This comprehensive guide delves into creating AI models that prioritize privacy, security, and robustness. Featuring practical examples in Python, it covers uncertainty quantification, adversarial ML
TIO-IKIM / PathoPatcherPathoPatcher is a Python project designed for accelerating Whole Slide Image Preprocessing, employing AI-based preprocessing techniques with features like annotation handling, color normalization, and configurable parameters
LasithaAmarasinghe / Hand Gesture Math SolverThis project demonstrates a real-time hand gesture recognition system using Python, OpenCV, and Gemini AI by Google.
DevGlitch / BotwizerSocial media AI bot using computer vision to imitate human behaviors. Final project for Harvard Advance Python CSCI E-29 Fall 2020. Received Gold Student Choice Award.
