Build a WhatsApp Business API Chatbot with Python Flask
Table of Contents
- Introduction
- Configuring the Sandbox Environment
- Configuring Dialogflow
- Two-Way Communication with WhatsApp API
- Available Templates for Sending WhatsApp Messages
- Configuring the Sandbox Environment for Twilio
- Configuring Dialogflow for WhatsApp Integration
- Creating a Service Account for Dialogflow API
- Setting Up the Python Project
- Implementing the Get Message API
- Integrating Twilio for Sending Messages
- Deploying the Project on AWS using Zappa
- Testing the Integration in the Twilio Sandbox
- Conclusion
- Resources
Introduction
In this tutorial, we will be exploring how to integrate Whatsapp Business API with Python using the Twilio sandbox environment. We will cover the steps to configure the sandbox environment, set up Dialogflow for natural language processing, and implement the two-way communication with the WhatsApp API. Additionally, we will discuss the available templates for sending messages and guide You on how to configure Twilio and Dialogflow for WhatsApp integration. Finally, we will deploy the project on AWS using Zappa and test the integration in the Twilio sandbox. Let's get started!
Configuring the Sandbox Environment
To begin, we need to set up the sandbox environment provided by Twilio. Since we don't have a WhatsApp Business account, we will rely on the Twilio sandbox environment, which offers limited usage and integration. The sandbox environment provides a phone number registered with WhatsApp, which allows us to receive and send messages. We will walk through the process of configuring the sandbox environment using Twilio's programmable SMS and WhatsApp APIs.
Configuring Dialogflow
Next, we will configure Dialogflow as our natural language processing (NLP) engine. Dialogflow, a framework developed by Google, will analyze and detect user intents from their conversation and respond accordingly with preconfigured responses. We will Create a new agent in Dialogflow, set up the necessary intents, and define appropriate responses. Dialogflow will be integrated into our Python application, serving as the main NLP engine for our WhatsApp chatbot.
Two-Way Communication with WhatsApp API
WhatsApp APIs work asynchronously, allowing two-way communication between our application and the user. When a user sends a message to our application, whether it is a reply to one of our outbound messages or they initiate the communication themselves, our application has a 24-hour window to send messages back to the user without using a template. However, for messages sent outside this window, we must use approved templates. We will explore the different categories of templates, how to get them approved by WhatsApp, and how to handle two-way communication effectively.
Available Templates for Sending WhatsApp Messages
To send messages to users outside the 24-hour session window, we must use approved templates. Templates vary Based on the specific category they fall into, such as account updates, alerts, appointments, auto-reply, issue resolution, and payment updates. These templates must be approved by WhatsApp to ensure that they Align with the template approval process. We will provide examples of how these templates are structured and explain the approval process for different businesses.
Configuring the Sandbox Environment for Twilio
In this section, we will dive deeper into the configuration process of the Twilio sandbox environment. We will guide you through accessing Twilio's dashboard, specifically the programmable SMS section, and demonstrate how to obtain the sandbox's mobile number and codes. These codes will be used to register your mobile number on the sandbox environment, enabling communication between your application and the sandbox infrastructure.
Configuring Dialogflow for WhatsApp Integration
Once the sandbox environment is set up, we will focus on configuring Dialogflow for WhatsApp integration. We will log into Dialogflow and create a new agent specifically for our YouTube WhatsApp integration project. The agent will handle user queries related to order summaries and other account-related information. We will define training phrases, intents, and set up appropriate responses. This will enable Dialogflow to understand user input and respond accordingly.
Creating a Service Account for Dialogflow API
For Dialogflow to be properly integrated into our Python project, we need to create a service account in Google Cloud. The service account will serve as a means of authentication when accessing the Dialogflow API through REST APIs in Python. We will guide you through creating the service account, obtaining a private key in JSON format, and storing it securely in your Python project.
Setting Up the Python Project
Before delving into the implementation of the Get Message API and integrating Twilio for sending messages, we need to set up a basic Python project. We will create a virtual environment, install the necessary dependencies (such as Flask for REST API creation and Dialogflow for integration), and set up the development environment. Furthermore, we will explain the importance of creating a requirements.txt file and how it helps in deploying the project on AWS using Zappa.
Implementing the Get Message API
With the Python project set up, we can now implement the Get Message API. This API will handle incoming messages from users and process them using Dialogflow for natural language understanding. We will configure the necessary imports, variables, and parameters for Dialogflow integration. We will also provide a step-by-step explanation of the code, covering various components such as session setup, query inputs, and the detect intent method from the Dialogflow library.
Integrating Twilio for Sending Messages
To enable our chatbot to send messages back to the user, we need to integrate Twilio's API into our project. We will explain how to construct the API URL using the Twilio documentation and include the necessary parameters such as the message, recipient's mobile number, and authorization details. We will also demonstrate how to obtain the account SID and auth token from Twilio for authentication. This integration will allow our chatbot to respond to user queries effectively.
Deploying the Project on AWS using Zappa
Once our Python project is fully implemented and tested locally, we will proceed to deploy it on AWS using Zappa. We will guide you through the deployment process, explaining the necessary configurations and choosing a suitable stage name and AWS settings. Zappa will Package our virtual environment, install dependencies, create an S3 bucket, and deploy our project as an AWS Lambda function. We will provide step-by-step instructions on deploying the project and obtaining the API Gateway URL.
Testing the Integration in the Twilio Sandbox
To ensure that our integration is functioning correctly, we will test it in the Twilio sandbox environment. We will send a WhatsApp message to our Twilio sandbox number and observe the response from our chatbot. We will demonstrate different scenarios, including querying order status and receiving appropriate responses. This final test will confirm the successful integration of all components.
Conclusion
In conclusion, we have successfully explored the integration of WhatsApp Business API with Python using the Twilio sandbox environment. We have covered all the necessary steps, from configuring the sandbox environment and Dialogflow to implementing two-way communication and deploying the project on AWS. With this knowledge, you can build powerful WhatsApp chatbots and provide seamless interactions with your users. Be sure to refer to the resources section for further information and documentation.
Resources