Build a WhatsApp AI Chatbot with ChatGPT & APIs
Learn how to connect ChatGPT with WhatsApp to build an AI-powered chatbot for customer service. Explore both code-based and no-code integration methods that follow WhatsApp’s official API guidelines.
How to Build a Custom WhatsApp AI Chatbot with the WA-MExpress API
Modern customers expect instant answers, 24/7. But how can you provide real-time, intelligent support without overwhelming your team? The answer is a custom AI chatbot that lives on WhatsApp.
While our no-code AI Agent feature is perfect for most businesses, developers often need more power and flexibility. This guide will walk you through the core concepts of using the WA-MExpress platform to build a fully custom WhatsApp chatbot connected to any AI model, like OpenAI's GPT-4.
The Two Core Components
A custom chatbot has two main jobs: listening for incoming messages and sending replies. WA-MExpress provides powerful, developer-friendly tools for both.
1. The "Ears": Receiving Messages with Webhooks
You can't build a bot if you don't know when a user messages you. A Webhook is the solution. When a user sends a message to your WhatsApp number, WA-MExpress instantly sends a notification with the message content to a URL you provide. This is the real-time trigger for your application.
2. The "Mouth": Sending Replies with the REST API
Once your AI has generated a response, you need a way to send it back to the user on WhatsApp. Our REST API provides a simple, powerful endpoint to send text, media, and template messages programmatically. This is how your bot talks.
The 5 Steps to Building Your Bot
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Set Up Your Server Endpoint
Create a simple web application using a framework like Node.js (with Express) or Python (with Flask). This application needs a public URL that can receive `POST` requests. This URL will become your webhook receiver. -
Configure Your WA-MExpress Webhook
Log in to your WA-MExpress dashboard, navigate to your device settings, and paste your application's public URL into the webhook field. Now, all incoming messages will be sent directly to your server. -
Process the Incoming Message
In your application's code, parse the JSON data that WA-MExpress sends. Extract the `sender` phone number and the `conversation` text. This is the information you'll pass to your AI. -
Generate an AI Response
Send the user's message text to your preferred AI model's API (e.g., OpenAI, Anthropic, or a self-hosted model). The AI will process the text and return a generated response. -
Send the Reply via the WA-MExpress API
Take the response from your AI and use it as the `message` parameter in a call to the WA-MExpress `create-message` API endpoint. Use the `sender` number from the webhook as the `to` parameter. This completes the loop and sends the reply to the user.
Example Code (Python + Flask)
Here’s a simplified Python example showing how these pieces fit together.
from flask import Flask, request, jsonify
import requests
import openai
app = Flask(__name__)
# Your WA-MExpress API Credentials
MExpress_APP_KEY = "YOUR_APP_KEY"
MExpress_AUTH_KEY = "YOUR_AUTH_KEY"
MExpress_API_URL = "https://wa.mexpress.pk/api/create-message"
# Your OpenAI API Key
openai.api_key = "YOUR_OPENAI_API_KEY"
def get_ai_response(user_message):
# This is where you call the ChatGPT API
response = openai.Completion.create(engine="text-davinci-003", prompt=user_message, max_tokens=150)
return response.choices[0].text.strip()
def send_whatsapp_reply(recipient, message):
payload = {
'appkey': MExpress_APP_KEY,
'authkey': MExpress_AUTH_KEY,
'to': recipient,
'message': message
}
requests.post(MExpress_API_URL, data=payload)
@app.route('/whatsapp-webhook', methods=['POST'])
def whatsapp_webhook():
data = request.json
sender_number = data.get('sender')
user_message = data.get('payload', {}).get('conversation')
if sender_number and user_message:
# 1. Get AI response
ai_reply = get_ai_response(user_message)
# 2. Send reply via WA-MExpress API
send_whatsapp_reply(sender_number, ai_reply)
return jsonify({"status": "success"}), 200
if __name__ == '__main__':
app.run(debug=True)
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