Chatbot-Using-Deep-Learning

We use Keras, Tensorflow and NLTK to recognize the user's intent and generate the appropriate response.

Getting Started (Console Chat):

Create a virtual environment and install the requirements: pip install -r requirements.txt

Train the model:

• Run all the code cells in the notebook 'trainer.ipynb'.

Run the application: python chat.py

Intents Dataset:

1. Greeting: greetings to the user.

2. GoodBye: goodbyes to the user.

3. ThankYou: appreciation messages to the user.

4. Success: response to a successful action relayed by the user.

5. About: replies to messages about the chatbot.

6. Name: replies to messages about the chatbot's name.

7. Help: responses to a user's help request or request for more information on an issue.

8. Assistance: replies to a user's request for assistance.

9. CreateAccount: helps the user with creating an account.

10. Login: messages helping the user login.

11. Password: assistance with resetting a forgetten password.

12. EmailChange: helps a user with email address changes.

13. EmailSupport: gives the user an email for customer support.

14. Payout: provides information about vendor payouts.

15. Complaint: gives th user a place to complain.