Build Your Own AI Chatbot Like ChatGPT – Complete Guide

Launch an EC2 instance on Ubuntu, configure security groups, and assign an elastic IP, then register a domain with a registrar like GoDaddy and connect via DNS for worldwide accessibility.

Launch an Ubuntu EC2 instance on AWS to host a chatbot, selecting Ubuntu 24.04 LTS, creating a key pair, configuring security groups, and assigning storage and an elastic IP.

Allocate an elastic IP address under network and security and attach it to your chatbot VM to give it a static IP.

Purchase a domain for your AI chatbot using GoDaddy or another domain registrar, and check availability. Review INR pricing and proceed to checkout to secure the domain.

Learn to point your domain to an EC2 instance by updating the DNS A record in your registrar, including adding a www CNAME, using the elastic IP 52.86.117.139.

Access a vm via ssh with a public ip and private key, update packages, install Python 3.12 and pip, then set up a virtual environment with nginx for the chatbot.

Create a Python 3.12 virtual environment named chatbot env, activate it with source chatbot env/bin/activate, and upgrade pip for compatibility and security.

Explore the Streamlit frontend and chatbot_logic backend to enable text generation, image creation, and text-to-speech, backed by Amazon Bedrock and Google Text-to-Speech, with memory via link chain.

Explore how the backend script provides core AI functionality for a chatbot, including LM based responses, text to text, image and speech generation, memory, and context management via Amazon Bedrock.

Explore a Streamlit-based front-end script, chatbot_ui.py, that powers a multi-modal AI chatbot with text-to-text, text-to-image, text-to-speech, and speech-to-text features, connected to a back-end and preserving chat history.

Explore Amazon Bedrock, a managed aws service that lets you build generative ai apps by accessing foundation models via a simple api for text generation, chatbots, and data analysis.

Navigate the AWS Bedrock console to request access to foundation models from AI21 Labs, Anthropic, Cohere, and Llama, and learn how to handle approvals or support tickets.

Learn to clone a chatbot repo into a vm with git, install git if needed, clone the repository, and inspect the front end and back end scripts.

Install streamlit, boto3, gtt, request, openai, and anthropic; create a headless streamlit config for port 8505 and run the app in background with redirected errors to a log file.

Set up nginx as a reverse proxy for a streamlit-based ai chatbot on EC2, routing your domain to the chatbot on port 8501, then test and reload nginx.

Secure your chatbot website with https using certbot and Let's Encrypt. Install certbot and nginx plugin, generate and install a free ssl certificate, and configure nginx for https.

Troubleshoot ssl issues by ensuring ports 80 and 443 are open, nginx is running and listening, and the domain points to the elastic IP before rechecking the bot.

Access the chatbot ai by checking vm configuration, verifying Streamlit and nginx are running, and review nginx access and error logs to troubleshoot.

Troubleshoot chatbot access by updating the nginx configuration to listen on port 80, reload nginx, and verify Streamlit is served behind the nginx web server.

Identify and fix a module not found error by aligning the frontend import chatbot_backend with the backend script chatbot_logic, restart Streamlit, and anticipate installing the length chain package.

Fix the no module named langchain error by installing the missing package with pip. The video also covers AWS credentials and Bedrock permissions on EC2.

Configure the AWS CLI for chatbot integration by creating an IAM user, generating access keys, running AWS configure, and restarting the Streamlit app to resolve validation errors.

Install the transformers package to enable text-to-text generation, fix import errors with pip commands, and address version warnings. The lesson also demonstrates text-to-image and text-to-speech features.

Discover how to run a chatbot that remembers prior conversations, references earlier chats to answer questions, and guides you in building your own chatbot like ChatGPT.

Celebrate completing the course by building and deploying your own AI chatbot like ChatGPT using Python scripts, AWS EC2, nginx, and Streamlit.