AI Based Food Nutrition Analyzer | Agentic AI Project | Euron скачать в хорошем качестве

AI Based Food Nutrition Analyzer | Agentic AI Project | Euron

Sign up with Euron today : https://euron.one/sign-up?ref=940C6863 Project Resource Link : https://euron.one/course/ai-based-foo... One Student One Subscription Euron Plus - https://euron.one/personal-plan/aa290... For any queries or counseling, feel free to call or WhatsApp us at: +919110665931 / +919019065931 Learn how to build a powerful AI Nutrition App using GPT-4, Llama Index, FastAPI, and Streamlit in this step-by-step Python video. Perfect for beginners and experienced developers alike, this comprehensive guide will kickstart your programming journey while helping you master essential coding concepts. In this video, you'll: Create an AI-driven app to analyze food nutrition and suggest smart, healthy pairings. Implement semantic search using Llama Index to answer nutrition questions from custom datasets. Build an interactive Streamlit dashboard to visualize macros like protein, carbs, and fat, while calculating health scores. Follow modular, production-ready code with clean API endpoints, logging, and environment variable handling. This project is ideal for those looking to dive into real-world applications of OpenAI GPT models and vector search technology, all while strengthening your Python skills. Whether you're learning to code or refreshing your knowledge, this tutorial is for you! Hit play and code along to create a fully functional AI-powered food nutrition assistant. Don't forget to Like, Subscribe, and hit the notification bell for more educational content from EuronTech. Let's build something amazing together! #llamaindex #naturallanguageprocessing #streamlittutorial #streamlit #streamlitdashboard CHAPTERS: 00:00 - Project Overview 01:08 - Creating a Conda Environment 04:07 - Setting Up Folder Structure 13:04 - Configuring the AI Model 17:14 - Loading Environment Variables 18:06 - Implementing Logging 19:36 - Creating Service Context 20:49 - Building Vector Store Index 28:15 - Asking Questions from Index 29:10 - Creating a Query Engine 34:55 - Getting Nutrition Information from OpenAI 40:50 - main.py Overview 43:09 - FastAPI Code Implementation 44:40 - FastAPI Endpoints Setup 47:08 - Running FastAPI Server 54:12 - Streamlit Code Overview 54:50 - Displaying Nutrition Information in UI 55:00 - Running the Streamlit App 56:00 - Testing the App with Different Foods 57:25 - Project Demo Instagram: https://www.instagram.com/euron_offic... WhatsApp :https://whatsapp.com/channel/0029Vaee... LinkedIn: https://www.linkedin.com/company/euro... Facebook:   / 61566117690191   Twitter :https://x.com/i/flow/login?redirect_a...