May 22
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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: https://www.youtube.com/watch?v=zD0A-9qghdo 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