/Bn-Chatbot

Primary LanguageJupyter Notebook

Certainly! Below is a consolidated readme.md file that includes information from various sections:

# Chatbot using Seq2Seq LSTM Models

This repository contains code for building a Chatbot using Seq2Seq LSTM models in Keras.

## Introduction

In this project, we leverage the power of Seq2Seq LSTM models to create a functional Chatbot capable of answering user questions. This approach can be applied to various domains, such as e-commerce or educational websites.

## Getting Started

### Prerequisites

Make sure you have the following dependencies installed:

- TensorFlow
- Keras
- bnlp_toolkit
- stopwordsiso
- wikipedia

Install them using the following command:

```bash
pip install -r requirements.txt

Data Preparation

Download the chatterbot/english dataset and extract it into the data directory.

# Example download command
wget https://github.com/shubham0204/Dataset_Archives/blob/master/chatbot_nlp.zip?raw=true -O chatbot_nlp.zip
unzip chatbot_nlp.zip

Training the Model

Run the Jupyter notebook ChatBot_With_Seq2Seq.ipynb to train the Seq2Seq LSTM model. The model will be saved as model.h5.

jupyter notebook ChatBot_With_Seq2Seq.ipynb

Inference and Chatting with the Chatbot

After training, you can interact with the Chatbot by running the provided inference code. Enter a question, and the Chatbot will generate an appropriate response.

python chatbot_inference.py

Additional Notes

  • The chatbot_inference.py script allows interactive chatting with the trained Chatbot.
  • The bengalianalyzer library is used for Bangla text analysis in certain parts of the code.

License

This project is licensed under the MIT License - see the LICENSE file for details.


Feel free to adjust and modify the content based on your specific project details and preferences.