Build a Chatbot with Gemini Pro Using Flask

AI chatbots have become essential for customer support, virtual assistants, and interactive web applications, requiring real-time conversational capabilities and context-aware responses. Building a web-based chatbot demonstrates how to integrate large language models into Flask applications, handle user sessions, and create dynamic interfaces that update asynchronously without page reloads.

In this project, we'll build a conversational AI chatbot using Gemini Pro and Flask that processes user queries and displays AI-generated responses in real time. We'll create a Flask web application with form handling for user input, integrate the Gemini API for natural language processing, and implement session management to maintain conversation history. The interface will feature asynchronous UI updates with loading indicators and a question history view for reviewing past interactions.

We'll start by setting up the Flask application structure and creating HTML templates for the chat interface. Next, we'll build routing endpoints to handle user queries, integrate Gemini Pro API calls for response generation, and implement AJAX requests for seamless UI updates. By the end, you'll have a working AI-powered chatbot demonstrating Flask backend development, Gemini API integration, session-based chat history, asynchronous form submission, and responsive web design applicable to any conversational AI or interactive web application project.