Rating (7.5/10): ThematicBot is an AI-based chatbot developed using TensorFlow, spaCy, and KMeans clustering. It uses tokenization and embedding to process text input, predicting responses. The interface is managed using the Tkinter library, enhancing user experience by dynamically improving relevance and depth of responses based on user input.
ThematicBot is an AI-based chatbot developed using machine learning models and natural language processing to generate contextually appropriate responses. The project integrates TensorFlow for neural network modeling, spaCy for natural language processing, and KMeans clustering for response categories. The system uses tokenization and embedding to process text input, which is fed into a neural network for response prediction. The Tkinter library manages the chatbot interface, providing a GUI for user interaction. The aim is to enhance user experience by dynamically improving response relevance and depth based on user input.
ThematicBot is an AI-powered chatbot that uses machine learning models and natural language processing (NLP) techniques to generate contextually appropriate responses. It uses TensorFlow, a deep learning framework, for neural network modeling, which predicts responses based on input data. SpaCy for NLP handles tokenization, part-of-speech tagging, named entity recognition, and other linguistic analyses. ThematicBot categorizes responses using KMeans clustering, breaking down input text into tokens and embedding them into dense vectors. The neural network processes tokenized input and predicts the most suitable response, learning from training data and adapting to user interactions. The chatbot provides a graphical user interface (GUI) using the Tkinter library for user interaction. ThematicBot aims to enhance user experience by dynamically improving response relevance and depth. As users provide input, the chatbot adapts its responses over time. ThematicBot encourages continuous refinement and optimization based on user feedback and real-world interactions.
- HTTPS - https://github.com/Statute8234/ThematicBot.git
- CLONE - Statute8234/ThematicBot.git
To use scripts on GitHub, follow these steps:
- Initialize a new repository: Create a new repository on GitHub where your scripts and related files will be stored.
- Clone the repository to your local machine: Clone the repository to your local machine, creating a local copy of your GitHub repository.
- Add scripts to the cloned directory on your local machine, such as Python scripts for machine learning models and text processing.
- Stage changes: Use
git addto stage new files or changes in existing files for commit, and usegit committo save staged changes to the local repository. - Push changes to GitHub: Upload local commits to your GitHub repository, updating your online repository with any changes made locally.
- Pull changes: Update your local repository with the latest changes from GitHub if you're collaborating with others or have made changes to the GitHub repository through the GitHub website.
- Branch (optional): Create branches for new features or experiments without affecting the main project, and merge them back into the main branch when ready.
- Review changes and merge pull requests: Use pull requests to review and discuss changes before merging them into the main branch, maintaining code quality and preventing conflicts.
The project integrates advanced technologies to create an interactive AI chatbot, utilizing Natural Language Processing (NLP) and clustering for improved response mechanisms. However, the reliance on predefined responses and codebase maintenance complexity may pose challenges in scaling and updating. The system could benefit from advanced NLP models for improved interaction quality and contextual understanding.