This repository contains code for building a chatbot using TensorFlow and Keras. The chatbot is designed to respond to user queries and prompts with contextually relevant answers.
This chatbot project utilizes deep learning techniques implemented with TensorFlow and Keras to create a conversational agent capable of understanding and generating natural language responses. The model architecture follows a sequence-to-sequence framework with an encoder-decoder architecture using LSTM layers.
- Natural Language Understanding: The chatbot can interpret user queries and prompts using natural language processing techniques.
- Contextual Responses: The chatbot generates contextually relevant responses based on the input it receives.
- Training and Inference: The model can be trained on custom datasets and used for real-time inference to interact with users.
To run the chatbot locally, follow these steps:
- Clone this repository to your local machine.
- Install the required dependencies by running
pip install -r requirements.txt. - Run the
tensorflow-ai.pyscript to train the model and start the chat interface.
Once the chatbot is installed and running, you can interact with it by typing queries or prompts into the command line interface. The chatbot will respond with generated text based on the input it receives.
The data.py file contains a dataset of input-output pairs used for training the chatbot. Each pair consists of a user query and the corresponding response generated by the chatbot. The dataset covers a wide range of topics to ensure the chatbot's responses are diverse and contextually appropriate.
The model architecture consists of an encoder-decoder framework with LSTM layers. The encoder processes the input sequence, while the decoder generates the output sequence based on the encoded input. The model is trained using a sequence-to-sequence approach with teacher forcing and sparse categorical cross-entropy loss.
Contributions to this project are welcome! Feel free to open issues for bugs or feature requests, or submit pull requests with improvements to the codebase.
This project is licensed under the MIT License.
