Juni Lee’s Post

🔊 "Transforming Text into Speech with Python and Gemini API: Check Out My Latest Project!" 🚀 Excited to unveil my latest Python project, where I've integrated Gemini API to convert text responses into speech! 💬 Check out the project here: https://lnkd.in/g-EQXtub Don't forget to create your free Gemini API key https://lnkd.in/gPMttDS7 Remember, safeguard your API key for secure project sharing! #AIIntegration #Gemini #GenerativeAI #TextToSpeech #API

𝗙𝗿𝗲𝗲 𝗮𝗽𝗽 𝗶𝗱𝗲𝗮 (half ready for you) Find the shortest, shade maximizing path between two points, so people who enjoy walking don't get sunburnt. You can (most likely) get building elevation data from Google Maps or Google Earth, and you can do shade projection given the time of day (Sun elevation) and the geographical latitude and longitude. You can use that as a heuristic for an A* pathfinder, and there you have the perfect walking app. Here's the ChatGPT result (in Python) for shade projection and the A* heuristic: https://lnkd.in/d96iAnn3 𝗕𝗼𝗻𝘂𝘀 𝗽𝗼𝗶𝗻𝘁𝘀: Lobby to your city Mayor and preferred candidate on the future election to plant those 1million promised trees in the city, to shademax your sidewalk area, and not in some forest 50km away from where you live #scheduledviapubler

 A Content-Based Movie Recommendation System! 🎬 I’ve developed a Streamlit app using the MovieLens dataset to provide personalized movie recommendations. What makes this app unique? 🔹 Data Cleaning with Mode Rating: I modified the dataset to include the mode rating of users for each movie, ensuring no missing values and improving prediction accuracy. 🔹 Content-Based Filtering: The app uses content-based filtering to recommend movies based on your preferences. 🔹 Cosine Similarity for Recommendations: I’ve implemented cosine similarity to match movies based on user ratings, improving recommendation relevance. 🔹 KNN for Rating Prediction: I used KNN (K-Nearest Neighbors) to predict unseen movie ratings and further enhance personalized suggestions. 🔹 FuzzyWuzzy Matching: To refine movie title matching and ensure accurate recommendations, I’ve integrated FuzzyWuzzy for string matching. Nitish Singh Sir Krish Naik Sir, would love to hear your thoughts on this project. Your feedback would be invaluable as I continue to refine this project. #MachineLearning #RecommendationSystem #DataScience #AI #Python

Just finished the course “Build a JavaScript AI App with React and the OpenAI API” 

Hi everyone, 🚀 Excited to share my latest project: the Face Swap Flask App! 🎭 🎯 Aim: To seamlessly swap faces from a source image to a given destination image. 🔍 How It Works: Face Detection: Leveraged OpenCV and dlib to detect human faces in both images. Landmark Extraction: A pretrained model helped to extract facial landmarks from these detected faces. Face Swapping Magic: Using these landmarks, I swapped the faces while preserving expressions and features. 🌐 Web App Creation: Built the app using Flask, making it accessible via a user-friendly web interface. 👉 Check out the demo and try it yourself! 📸🔄 🔗 Check out the project on GitHub and share your feedback! https://lnkd.in/dsPcS9Ev #AI #ComputerVision #WebDevelopment #Python #Flask #FaceSwap Feel free to connect and share your thoughts! Let’s keep innovating together. 🤝🔥

#Day41 #75HardGenAI #GenerativeAI 🤖 Build Retrieval Augmented Generation (RAG) based System with Pinecone Vector DB and Large Language Models (LLM) 🧠 🔗 Link - https://lnkd.in/dvcKUTT2 In this video I Explained about: - Building Retrieval Augmented Generation (RAG) based Application using Pinecone Vector Database and Hugging Face Large Language Models (LLM) - Why use RAG in LLM? - What is RAG and Types of RAG Frameworks? - How to Create Index in Pinecone Vector Database? - How to insert Data in Pinecone Vector DB? - How to Fetch and Query Data from Pinecone DB? - How to Combine Pinecone DB and LLM to Building RAG System? - How Retriver works in RAG based LLM Models? - Showcasing How LLM use Pincone Vector DB to generate Response with RAG. 🗂️ In Next video - I will show how to use Google Gemini LLM to create various AI-Powered Applications. 🗂️ To get the Source Code, GitHub - https://lnkd.in/diehTu44 Watch previous day videos now to get started with Generative AI and Large Language Modelling 🦍 🤖 Day 1 to Day 40 - https://bit.ly/4bJwZla 🔖 Book your call with me at topmate.io and learn about Python, Data Science, ML/DL, Large Language Models (LLM) or Interview Preparation 🤖. 📲 Book your call at - https://lnkd.in/d3r6wAMz Simranjeet Singh ❤️ Follow Now 🐺

🚀 Exciting News! 🚀 I’ve successfully implemented my CNN model in a mobile app, leveraging a Flask server for real-time image classification of Cat And Dog! 📱🧠 Here’s how it works: - The app sends an image to the Flask server via an API request. - The Flask server, a lightweight and powerful web framework in Python, handles the request, processes the image, and passes it through the CNN model. - The model then generates a prediction, which is sent back to the app as a response! 🔄 Flask’s simplicity and flexibility made it easy to deploy the model and integrate it with the app, making the entire process smooth and efficient. It’s been an amazing learning experience combining mobile development and AI. Stay tuned for more updates! ✨ #MobileApp #Flask #MachineLearning #CNN #DeepLearning #AI

Just finished setting up this Darwin RAG ChatBot for 'Proyecto Gondwana', which is running smoothly on a MacBook with only 16GB of RAM. What's even better is that it can speak Spanish. This LangChain-based chatbot retrieves information from certain PDFs (Darwin's diaries) using Hugging Face Embeddings and FAISS for embeddings and efficient similarity search in vector storage. I chose Vicuna (7B | 4-bit) as the language model because it performs quite well for chat running locally on this common hardware, and, with just a specific prompt, it speaks Spanish effortlessly. It's a desktop app, and for quick implementation, I used Flet for the UI. The background image was done by Federico Villalba from Estudio Groovin. - #flet #python #RAG #AI #chatbot

🚀 Ready to ensure your LLM app runs smoothly? Check out our latest post by Daniel Baptista Dias on trace-based testing for LLM apps! 🔍 We’ve created a step-by-step guide to help you identify and fix performance bottlenecks with Tracetest. Here’s what you’ll find in the post: 🔹 What is trace-based testing and how it applies to LLM apps 🔹 A detailed step-by-step tutorial to troubleshoot and resolve issues 🔹 Best practices for using Tracetest with large language models 🔹 How to improve LLM app performance by leveraging distributed tracing 🛠️ Dive into the tutorial now: https://lnkd.in/e9d8uHM3

Just finished the course “Build a JavaScript AI App with React and the OpenAI API” by Morten Rand-Hendriksen! The course provided me with the opportunity to get hands-on experience by guiding me through the development of an AI-driven application using React. This experience enhanced my technical skills and highlighted the vast capabilities of AI, showing me how to incorporate the powerful OpenAI API in web applications.