Create a Powerful WhatsApp Chatbot with Python and Twilio

Table of Contents

1. Introduction
2. Setting up the Accounts
3. Registering the WhatsApp Number
4. Creating the Flask Script
5. Launching the EC2 instance
6. Connecting to the EC2 instance
7. Updating the NGROK URL
8. Testing the WhatsApp Chatbot
9. Running the Application Continuously
10. Conclusion

Introduction

In this tutorial, we will learn how to Create a WhatsApp chatbot using Chat GPT. A WhatsApp chatbot allows users to have live conversations with the chatbot through their WhatsApp number. The chatbot is powered by Chat GPT, an advanced language model developed by OpenAI. By the end of this tutorial, You will be able to deploy your own WhatsApp chatbot and use it to Interact with your friends. We'll walk through each step of the process and provide detailed instructions on setting up the necessary accounts, creating the Flask script, launching an EC2 instance, and testing the chatbot.

Pros:

• Allows Live Chat with users through WhatsApp
• Powered by Chat GPT, an advanced language model
• Easy to set up and deploy

Cons:

• Requires accounts on multiple platforms
• Requires technical knowledge to set up and configure

1. Setting up the Accounts

To get started, you will need to set up accounts on the following platforms:

• OpenAI: Create a developer account on platform.openai.com.
• AWS: Create an AWS account on aws.amazon.com.
• Twilio: Create an account on twilio.com.
• NGROK: Create a free account on ngrok.com.

Make sure to note down the necessary API keys and credentials for each account as they will be required in the later steps.

2. Registering the WhatsApp Number

After setting up the accounts, you will need to register your WhatsApp number in the Twilio console. To do this, follow these steps:

1. Go to the Twilio console and navigate to Messaging.
2. Select "Send a WhatsApp message".
3. Send a WhatsApp message to the provided number with the code "join Continue".
4. Your mobile number will be registered as a WhatsApp sandbox.

3. Creating the Flask Script

Now it's time to create the Flask script that will serve as the API for the chatbot. Follow these steps to create the script:

1. Install the necessary libraries: Flask, OpenAI, and twilio.
2. Import the required libraries and set up the Flask app.
3. Create a function to generate the chatbot's response using Chat GPT.
4. Set up the endpoint for incoming questions.
5. Retrieve the question from the request body and generate the answer using the chatbot function.
6. Send the response back to the user via WhatsApp.
7. Run the Flask app on a specific host and port.

Make sure to provide your OpenAI API Key as an environment variable or import it securely.

4. Launching the EC2 instance

Next, we will launch an EC2 instance on AWS to host our Flask application. Follow these steps:

1. Go to the AWS console and select EC2.
2. Click on "Launch Instance" and select an instance Type (e.g., T2 Micro).
3. Create or select an existing key pair for SSH access.
4. Configure the instance details and enable HTTP and HTTPS traffic.
5. Launch the instance and connect to it using SSH.

Ensure that you update the security groups to allow inbound traffic on port 5000, which is the port our Flask application will be running on.

5. Connecting to the EC2 instance

Once connected to the EC2 instance, you will need to set up the required dependencies and start the Flask application. Follow these steps:

1. Update the Package lists with apt update.
2. Install Python 3 and pip with apt install python3 python3-pip.
3. Create a requirements.txt file and install the necessary packages.
4. Install NGROK to expose the Flask application to the internet.
5. Set up NGROK and configure your NGROK API key.
6. Start the Flask application and note down the NGROK URL.

Ensure that you configure the NGROK API key and set it as an environment variable to secure it.

6. Updating the NGROK URL

To update the NGROK URL in the sandbox settings of your WhatsApp number, follow these steps:

1. Go to the Twilio console and navigate to the sandbox settings.
2. Copy the NGROK URL and paste it into the WhatsApp sandbox settings.
3. Save the changes.

Make sure to wait a few seconds for the changes to take effect.

7. Testing the WhatsApp Chatbot

With everything set up, you can now test the WhatsApp chatbot by sending messages to the registered number. Ensure that you have shared the WhatsApp number with your friends so they can also interact with the chatbot. Send various questions and check if the chatbot responds correctly. Monitor the Flask console to see the incoming questions and generated responses.

8. Running the Application Continuously

To ensure that the chatbot is available 24/7, you will need to run the Flask application continuously. Follow these steps:

1. Use the nohup command to run the Flask application in the background.
2. Use NGROK to run the application on the same NGROK URL.
3. Close the terminal and verify that the chatbot is still responsive.

This step is essential as it allows the chatbot to run continuously without interruption. Make sure to terminate the EC2 instance after you are done with the experiment to avoid unnecessary costs.

9. Conclusion

In this tutorial, we have learned how to create a WhatsApp chatbot using Chat GPT. We covered each step in Detail, from setting up the necessary accounts to launching the chatbot on an EC2 instance. We also tested the chatbot and ensured that it ran continuously. By following these steps, you can deploy your own WhatsApp chatbot and use it to interact with your friends.

Highlights

• Set up accounts on OpenAI, AWS, Twilio, and NGROK.
• Register your WhatsApp number in the Twilio sandbox.
• Create a Flask script to serve as the API for the chatbot.
• Launch an EC2 instance on AWS and connect to it via SSH.
• Update the NGROK URL in the Twilio sandbox settings.
• Test the chatbot by sending messages to the WhatsApp number.
• Run the Flask application continuously using nohup and NGROK.
• Terminate the EC2 instance to avoid unnecessary costs.

FAQs

Q: Can I use a different language model instead of Chat GPT? A: Yes, you can use other language models supported by OpenAI or even train your own custom model.

Q: How secure is the API key for the chatbot? A: It is essential to keep the API key secure and not share it publicly. Consider using environment variables to store sensitive information.

Q: Can I customize the responses of the chatbot? A: Yes, you can modify the Flask script to add custom logic and handle specific questions or commands.

Q: Is the chatbot limited to text responses only? A: Yes, the chatbot is designed to generate text responses based on the given questions or prompts.

Q: Can I deploy the chatbot on a different platform other than AWS? A: Yes, you can deploy the Flask application on any platform that supports Python and meets the necessary requirements.

Q: How do I terminate the EC2 instance and stop the chatbot? A: To terminate the EC2 instance, go to the AWS EC2 console, select the instance, and choose "Terminate".