Final Semester Project: Generative AI themed Chatbot

Author: Deva Manikanta (BCA Program)

This project showcases a generative AI themed chatbot application built using Chatterbot, Flask, and a dynamic HTML/CSS interface. It leverages the Gemini API to enhance the chatbot's responsiveness and interactivity.

Project Overview

• Chatbot Engine: I have used Chatterbot framework, where it provides the core functionalities for building the conversational AI.
• Web Framework: For the design of web interfaces I have used Flask framework which facilitates the creation of a web-based interfaces with user interaction and the chatbot.
• API Integration: To add more spice to the my Vortex, I also added the Gemini API, where this integration empowered the chatbot to access and process external information, enriching its responses.
• User Interface: I have created an interactive HTML and CSS interface that provides a user-friendly platform for engaging with the chatbot.

Key Features

• Conversational AI: The chatbot interacts with users in a natural, text-based manner.
• API Integration: Access to external information expands the chatbot's knowledge base (limited to SEP-2021) and potential responses.
• Interactive Interface: The HTML/CSS interface enables users to conveniently interact with the chatbot.

Documentations That I have reffered:

• Chatterbot library (https://pypi.org/project/ChatterBot/)
• Flask library (https://pypi.org/project/Flask/)
• Additional libraries as needed (e.g., Gemini API client library)

Installation:

1. Clone or download the project repository.
2. Install required Python libraries using pip install -r requirements.txt (if a requirements.txt file exists).

Run the Application:

1. Navigate to the project directory in your terminal.
2. Start the Flask development server: flask run
3. Access the application in your web browser by visiting http://127.0.0.1:5000/ (or the specified port)

How It Works

• The Chatterbot framework handles the core conversation logic, including pattern matching and response generation.
• Flask powers the web server, enabling users to interact with the chatbot through the interface.
• The Gemini API integration allows the chatbot to retrieve information from external sources and incorporate it into its responses.
• The HTML and CSS interface presents a user-friendly platform for interacting with the chatbot via text input and output.

Further Development

• Train the chatbot on more extensive datasets to enhance its conversational abilities.
• Integrate additional APIs for a wider range of information access.
• Implement natural language processing (NLP) techniques for more sophisticated language understanding.
• Improve the user interface's design and functionality.