Smart Chatbot Application Using Google Gemini and Python Flask

This project is a web-based chatbot application that integrates Google's Gemini generative AI model using the Gemini API. The chatbot provides intelligent, context-aware responses to user queries in real time. Developed using Python with the Flask framework, the application features a clean and responsive user interface built with HTML, CSS , and JavaScript (jQuery). The core functionality allows users to send messages through a chat interface, which are then processed by the Flask backend. The backend communicates with the Gemini API to generate AI-driven responses based on user input. These responses are then returned and displayed in the frontend dynamically using AJAX, enabling smooth, real-time interaction without page reloads. The system is designed with modularity and simplicity in mind, making it suitable for educational, support, or personal assistant applications. This project demonstrates how advanced language models can be effectively integrated into lightweight web applications to create engaging and intelligent user experiences.

Authors

Publication Details

Journal
International Scientific Journal of Engineering and Management
Published
2025-07-19
DOI
https://doi.org/10.55041/isjem04811
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Smart Chatbot Application Using Google Gemini and Python Flask

Mankala Satish, ANDAVARAPU SAISREE
International Scientific Journal of Engineering and Management
AI in Service Interactions
article

Smart Chatbot Application Using Google Gemini and Python Flask

Mankala Satish, ANDAVARAPU SAISREE
article en

Abstract

This project is a web-based chatbot application that integrates Google's Gemini generative AI model using the Gemini API. The chatbot provides intelligent, context-aware responses to user queries in real time. Developed using Python with the Flask framework, the application features a clean and responsive user interface built with HTML, CSS , and JavaScript (jQuery). The core functionality allows users to send messages through a chat interface, which are then processed by the Flask backend. The backend communicates with the Gemini API to generate AI-driven responses based on user input. These responses are then returned and displayed in the frontend dynamically using AJAX, enabling smooth, real-time interaction without page reloads. The system is designed with modularity and simplicity in mind, making it suitable for educational, support, or personal assistant applications. This project demonstrates how advanced language models can be effectively integrated into lightweight web applications to create engaging and intelligent user experiences.

International Scientific Journal of Engineering and ManagementVol. 04(07)
Industry, innovation and infrastructure
Openalex Percentile: Top 90%
AI in Service Interactions
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