Embedding Your GPT in a Website

๐Ÿง‘โ€๐Ÿ’ป Question: I want to integrate a (custom) GPT model into my website to enhance its interactive features with AI-driven capabilities. How do I achieve this while ensuring the AI functions effectively within the web environment and aligns with my site’s design and functionality? How do I ensure real-time responses, contextually relevant interactions, and an intuitive user interface for this AI integration?

When facing these questions, I considered these three methods (and personally implemented the first one):

Method 1: WordPress Plugin

A GPT is mostly a context prompt with context data (=embeddings), Avatar image, and potential actions. If you donโ€™t badly need actions, i.e., external API calls such as weather data, and you run WordPress, I recommend the Meow Apps plugin (not affiliated).

Here’s a screenshot:

In the โ€˜Chatbotsโ€™ section, you can set the Context. Simply copy and paste the โ€˜Instructionsโ€™ prompt from the GPTs builder here.

In the โ€˜Chatbots > Visual Settingsโ€™ section, you can set the name and Avatar of your GPT so it feels the same.

In the โ€˜Chatbots > Embeddingsโ€™ section you can set the environment and add external data (e.g., using Pinecone).

Iโ€™m super happy with the plugin and can generally recommend it. You can see it live on any of our (bottom right) with what you may call a โ€˜custom GPTโ€™.

Here are some alternative solutions for embedding a GPT model on a website, provided by the community in November 2023:

Method 2: Using Assistant API with an iFrame

Implement an iframe on your webpage to embed the GPT.

Integrate a chat widget in the base.html file, using JavaScript to navigate the conversation based on the activated page.

Set up a Flask backend to handle API requests and send responsesโ€‹. Web developer Zorawar Purohit provided this code for the Flask backend:

# backend.py

from flask import Flask, request, jsonify
from flask_cors import CORS
import openai

app = Flask(__name__)
CORS(app)  # Enable CORS

openai.api_key = 'your-api-key'  # Replace with your OpenAI API key

@app.route('/ask', methods=['POST'])
def ask():
    data = request.json
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=data['prompt'],
        max_tokens=150
    )
    return jsonify(response)

if __name__ == '__main__':
    app.run(port=5000)  # Run the Flask app on port 5000

โ€‹Include an HTML page setup with iframe, form, and JavaScript function to send messages to the AI and display responsesโ€‹ (source):

<!-- index.html -->

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>AI Assistant</title>
    <script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
    <style>
        iframe {
            width: 100%;
            height: 500px;
            border: none;
        }
    </style>
</head>
<body>
    <iframe id="ai_frame" sandbox="allow-scripts allow-same-origin"></iframe>

    <form onsubmit="sendMessageToAI(); return false;">
        <input type="text" id="prompt" name="prompt" required>
        <input type="submit" value="Ask AI">
    </form>

    <script>
        function sendMessageToAI() {
            var prompt = document.getElementById('prompt').value;
            $.ajax({
                url: 'http://localhost:5000/ask',  // Point to the backend endpoint
                type: 'POST',
                contentType: 'application/json',
                data: JSON.stringify({ prompt: prompt }),
                success: function(response) {
                    // Write the AI's response to the iframe
                    var iframeDocument = document.getElementById('ai_frame').contentDocument;
                    iframeDocument.open();
                    iframeDocument.write('<p>' + response.choices[0].text + '</p>');
                    iframeDocument.close();
                },
                error: function(xhr, status, error) {
                    // Handle error
                    console.error("Error: " + status + " - " + error);
                }
            });
        }
    </script>
</body>
</html>

Method 3: Creating an Assistant Instead of a GPT

Opt for creating an Assistant (using the Assistant API) instead of directly embedding a GPT model.

Consider having a chatbox integration built for you or waiting for a plugin builder (e.g., Meow) to add this functionality to their plugin.

Note that the Assistant API might interact differently than GPT models, potentially requiring more instructions and tweakingโ€‹.

Also, note that there may be additional solutions at the time you’re reading this – the space is rapidly evolving. ๐Ÿš€

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๐Ÿ”ฅ Recommended: OpenAI Wants Us to Create GPTs, i.e., Miniature AI Agent Helpers