Build Your Own AI App Using Local LLMs + FastAPI + Vibe coding

In this video, we build a complete AI chat application that connects directly to a local LLM model running on your PC. No OpenAI API. No subscriptions. No internet dependency. 100% private AI. ⚡ We’ll use: ✅ FastAPI Backend ✅ Vibe Coding Workflow ✅ Local LLM Models with Ollama ✅ Real-time AI Chat Responses ✅ Offline AI Processing ✅ Beginner-Friendly Setup By the end of this tutorial, you’ll have your own ChatGPT-style AI assistant running locally on your machine. This is the future of private AI development and offline AI applications. 🔥 Step by Step workflow: Step 1: Install Python: https://www.python.org/ Step 2: FastAPI Download : Python -m pip install fastapi uvicorn requests Step 3: Install Local Model:    • Install Local LLM in 3 Steps (Download Oll...   Step 4: Building AI chat UI: Create a simple, clean chat interface in HTML, CSS, and JavaScript. Requirements: A chat container with messages appearing in bubbles (user on the right, AI on the left) A text input box and a Send button at the bottom Automatically scroll to the latest message Use fetch() to send POST requests to: http://127.0.0.1:8000/chat Send JSON in this format: { "prompt": "USER_MESSAGE_HERE" } Display the AI response in the chat window Make the UI responsive and centered Use only vanilla HTML, CSS, and JavaScript (no frameworks) Put everything in a single index.html file Step 5: Create a FastAPI backend in Python named main.py for a local AI chat application. Requirements: Use FastAPI Use Uvicorn to run the server on port 8000 Use Pydantic BaseModel to define a ChatRequest with a single field: prompt: str Add CORS middleware that ONLY allows: http://127.0.0.1:5500 http://localhost:5500 http://127.0.0.1:8000 http://localhost:8000 Create a POST endpoint at /chat The endpoint should: Receive JSON: { "prompt": "USER_MESSAGE" } Send this prompt to Ollama running at model llama3.2:1b: http://127.0.0.1:11434/api/generate Use the Requests library to forward the prompt Return the AI response text back to the frontend Handle errors safely and return a friendly message if Ollama is not running Keep the code simple and beginner-friendly Step 6: Run FastAPI: uvicorn main:app --reload --port 8000 Step 7: Run Local Server: python -m http.server 5500 Step 8: Running LLMs locally Step 9: Real-time streaming responses: http://127.0.0.1:5500/index.html ⏱ Chapters 00:50 Vibe Coding 01:25 Technical Workflow 02:05 Project Folder Structure 02:35 Step 1: Install Python 03:02 Step 2: FastAPI Download 03:50 Step 3: Install Local Model 04:53 Step 4: Building AI chat UI 05:22 Step 8: Create a FastAPI backend 5:45 Step 6: Run FastAPI 6:17 Step 7: Run Local Server 6:45 Step 8: Running LLMs locally 7:13 Step 9: Test App If you enjoyed the video, make sure to LIKE, SUBSCRIBE, and COMMENT what you want to build next. #AI #FastAPI #LocalAI #LLM #Python #Ollama #ArtificialIntelligence #MachineLearning #OfflineAI #VibeCoding #OpenSourceAI #ChatGPT #Llama3 #AIChatApp