Introduction to Chatbots - Build Your First AI Assistant

Define chatbots and compare rule-based versus AI-powered types, outline use cases, and explain how they enable 24/7 support and handle thousands of users.

Explore the core use cases of chatbots across customer support, e-commerce, healthcare, education, banking, and entertainment, showing how bots answer FAQs, track orders, find products, and support learning.

Learn the essential prerequisites for building a chatbot: Streamlit overview, setting up Python and a code editor (VS Code recommended), and creating a hello world app with internet access.

Master Streamlit basics to build a chatbot front end with just a few lines of Python. Explore installation, API reference, and interactive widgets for data science apps.

Set up your development environment by creating a virtual environment, installing Streamlit via the command line, activating the environment, and verifying the installation with a hello world app.

Build a simple hello chatbot app with Streamlit by creating think hello.py, setting a title and caption, adding a text input, and displaying user input plus a bot response.

Explore chatbot architecture and its workflow, detailing how frontend, backend, and NLP engine use models like gpt-4, google gemini, and llama to generate smart responses.

Explore the end-to-end chatbot workflow from user input to model-generated responses, including frontend, backend routing, and model APIs like OpenAI, Gemini, or llama.

Demonstrates building your first chatbot with the Gemini API in a two-part demo, from a basic app to an advanced version using Streamlit, Python, and session state.

Replace hard-coded credentials by adding a sidebar with logo, model selection, and Gemini API key input. Allow users to enter their API key and choose a model at session start.

Demonstrates building a multipage chatbot app with Streamlit by creating a pages folder, a home page, and a dedicated chatbot page for scalable features.

demonstrates building a file-based q&a bot in streamlit that answers questions from uploaded txt or md files using the Gemini API key, with file upload and article text in session.

Learn to build a CSV bot that answers questions about uploaded CSV data by loading it into a pandas data frame and using a data analyst prompt.

Create a text-to-image imagine bot using Gemini API for image generation, with a streamlit interface and a dedicated API key in Google Cloud to render and download the generated images.

Demonstrates running a local model with Ollama to protect data privacy, avoiding external APIs, and building a Streamlit chat interface that uses Gemma for local inference.

Deploy your chatbots to a live web app using Streamlit community cloud by preparing dependencies in a requirements.txt, linking GitHub, and running Streamlit run think_bot.py.

Develop and deploy diverse AI chatbots—from file Q&A to CSV-based and image generation, with llama integrations—using Streamlit, and share real-world demos via Streamlit Community Cloud.