How did you build this chatbot?

I developed this chatbot using OpenAI's GPT-3.5 Turbo and GPT-4. The chatbot is designed to handle a wide range of queries and is fine-tuned to cater to specific industry needs, providing accurate and contextual responses. The system architecture includes Flask as the web framework, enabling seamless integration between the backend and the front-end, which is crafted using Vanilla JavaScript and Tailwind CSS for styling. For data visualization, I incorporated Chart.js, enhancing user engagement through interactive charts. The chatbot's conversational data is stored in a Postgres database, managed with SQLAlchemy for ORM, ensuring efficient data handling and progressive model improvement purposes, strictly adhering to privacy guidelines. Additionally, the front-end features customized particleJS effects, creating a dynamic and visually appealing interface. This combination of technologies supports personalized responses by training the model on my specific skills, tastes, and experiences, alongside custom logic to accurately address user queries. Deployment of the application is managed through Heroku, offering a scalable and efficient cloud platform solution, facilitating quick updates and global accessibility. To monitor user interactions and gather insights for further optimization, I integrated Microsoft Clarity for in-depth user behavior analysis and Google Analytics for comprehensive web analytics, enabling data-driven decisions to enhance user experience and engagement.