This project demonstrates the development of an interactive AI chatbot trained on the Cornell Movie Dialogs dataset. The chatbot uses Natural Language Processing (NLP) techniques to engage in dynamic conversations by learning from movie scripts.
The chatbot utilizes a combination of NLP methods such as tokenization, stemming, and word embeddings to understand and respond to user input. It is designed to hold simple, engaging conversations based on a vast corpus of movie dialogues.
Key features include:
- Data Preprocessing: Tokenization, lemmatization, and word embeddings for text processing.
- Chatbot Architecture: A simple yet effective deep learning-based response generation model.
- Interactive User Interface: Real-time conversation with the chatbot using a GUI.
- Customizable: Easy to extend with new functionalities, datasets, or deep learning models.
- Pretrained Model: Build a basic model that understands and responds to user inputs.
- Response Generation: Generate contextually relevant responses based on the Cornell Movie Dialogs dataset.
- Real-time Chat: Engage in interactive conversation via a GUI interface with the chatbot.
- Extensible Framework: Ready for adding advanced NLP techniques like sequence-to-sequence models, attention mechanisms, or deep learning architectures.
- Resource hungry: You need to have a lot of resources to train and run this model locally on your PC (Minimum I5 10th gen, 8GB RAM & 4GB GPU)
To run this project locally, follow these steps:
git clone https://github.com/Khanz9664/chatbot.git
cd chatbot