This project implements a simple chatbot using Keras with a pre-trained model. The chatbot is trained on a dataset of intents and responses. The model predicts the intent of user input and generates an appropriate response.
This chatbot script uses a neural network model (chatbot_model.h5), a tokenizer (Tokenizer.p), and a label encoder (LabelEncoder.p) to understand user input and generate responses. The model is trained on a dataset of intents and responses provided in the intents.json file.
- Python 3.x
- Required Python packages: Keras, pandas, numpy
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Clone the repository:
https://github.com/shukur-alom/AI-ChatBot.git -
Install the required dependencies:
pip install -r requirements.txt
- Run the chatbot script:
python main.py
- Enter your messages when prompted, and the chatbot will generate responses based on the trained model.
- The model is a neural network loaded from chatbot_model.h5.
- Tokenization is performed using the tokenizer saved in Tokenizer.p.
- Intent labels are encoded and decoded using the label encoder saved in LabelEncoder.p.
- The intents and responses are defined in the intents.json file.