Implement AI-Generated Images for Agents in Publish Flow

[Sararda1]

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Summary 💡

In the agent publishing workflow, it would be beneficial to add a feature that allows users to generate AI-based images for their agents directly within the UI. This feature can enhance the visual appeal of the agents, making them more engaging and providing a professional look to the user interface.

Currently, there is a button in the UI labeled “Generate an AI Image,” but it needs to be connected to functionality.
This feature can work by allowing the button to interact with a prompt that takes a predefined set of parameters from the agent's information. This includes details such as the agent name, description, or other relevant data.

To implement this, the backend can utilize the existing /v2/store/media.py script for storing the generated images. Each image should be associated with the agent’s name or a unique identifier.

Examples 🌈

1. Frontend: Button Interaction (UI)

You already have a button labeled "Generate an AI Image." Now, you need to set up its functionality to trigger a request to the backend.

HTML/JS Example for the Button:

<!-- Button to trigger AI image generation -->
<button id="generateImageBtn">Generate AI Image</button>

<script>
    document.getElementById("generateImageBtn").addEventListener("click", function() {
        // Collect agent data from UI (e.g., agent name, description, etc.)
        const agentData = {
            name: document.getElementById("agentName").value, // Example input field for name
            description: document.getElementById("agentDescription").value, // Example input field for description
        };

        // Call backend API to generate image
        fetch('/v2/store/media/generate_image', {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json'
            },
            body: JSON.stringify(agentData)
        })
        .then(response => response.json())
        .then(data => {
            // Display the generated image (assuming the backend returns a URL)
            const imageUrl = data.imageUrl;
            document.getElementById("agentImage").src = imageUrl; // Display the image in an img element
        })
        .catch(error => {
            console.error('Error generating image:', error);
        });
    });
</script>

2. Backend: API Endpoint (Flask Example)

The backend will receive the data from the frontend, process it, and generate the image using AI (you can use an AI image generation service like OpenAI's DALL·E, or a custom model). Then, it will store the image and return a URL to be displayed on the frontend.

Flask Example for the Backend:

from flask import Flask, request, jsonify
import uuid
import os
from some_image_generation_service import generate_image_from_text  # Example import for image generation

app = Flask(__name__)

# Assuming media.py handles storage and saving images
from v2.store import media

# Endpoint to generate the AI image
@app.route('/v2/store/media/generate_image', methods=['POST'])
def generate_image():
    # Get agent data from the request
    agent_data = request.get_json()
    agent_name = agent_data.get('name')
    agent_description = agent_data.get('description')

    # Use the data to generate an AI image
    image_prompt = f"An image representing {agent_name}: {agent_description}"
    
    # Use an AI service to generate the image
    image = generate_image_from_text(image_prompt)  # This could be an API call or model inference

    # Generate a unique identifier for the image
    image_id = str(uuid.uuid4())  # Use a unique ID or UUID for the image
    image_filename = f"{image_id}.png"
    image_path = os.path.join('static/images', image_filename)  # Save it in a folder, e.g., 'static/images/'

    # Save the image to a file
    image.save(image_path)

    # Save the image details to the database (optional)
    media.save_image_data(agent_name, image_filename, image_path)

    # Return the image URL to the frontend
    image_url = f"/static/images/{image_filename}"
    return jsonify({"imageUrl": image_url})

if __name__ == "__main__":
    app.run(debug=True)

3. Image Generation Logic (AI Integration)

In the generate_image_from_text function, you would connect to an image-generation API (like OpenAI's DALL·E, or a similar service) that can accept a prompt and return an image.

Here’s an example of how this might look with OpenAI’s DALL·E (or any other API):

Example Image Generation Function (OpenAI API):

import openai

openai.api_key = 'your_openai_api_key'

def generate_image_from_text(prompt):
    response = openai.Image.create(
        prompt=prompt,
        n=1,
        size="1024x1024"
    )
    image_url = response['data'][0]['url']
    # You would then download the image and save it locally or use it directly
    image_data = requests.get(image_url).content
    image = Image.open(BytesIO(image_data))
    return image

4. Storing the Image (media.py)

The /v2/store/media.py script should handle storing the image locally and possibly saving relevant metadata about the image to a database (such as the agent’s name and the image path).

import os
from PIL import Image

# Function to save image data to disk or cloud
def save_image_data(agent_name, image_filename, image_path):
    # Example: Save the image details to a database or storage
    # You can store agent's name and image details in the database
    pass

# Function to save the image locally
def save_image(image_data, image_path):
    image_data.save(image_path)

5. Displaying the Image (Frontend)

Once the image is generated and the URL is returned from the backend, you can display it in the UI.

HTML Example for Image Display:

<img id="agentImage" src="" alt="Agent Image" />

Final Workflow:

1. The user clicks the "Generate AI Image" button.
2. The frontend sends the agent’s name and description to the backend.
3. The backend generates the image using AI (e.g., DALL·E or a custom model).
4. The image is saved and a URL is returned to the frontend.
5. The frontend updates the UI to display the generated image.

Motivation 🔦

It’s great that you're working on something so innovative! Building a feature that combines user input with AI-generated visuals can truly enhance the user experience and make your application stand out. This kind of integration is a powerful way to engage users, showcase creativity, and elevate the professional look of your platform.

The road to implementing this feature may have its challenges, but each step you take brings you closer to a more dynamic and visually appealing product. Remember, every challenge you overcome is a step towards making your vision a reality. You’re combining technical skills with creativity, and that’s something special.

Stay focused, take it one step at a time, and trust that the work you're putting in now will result in something amazing that users will love! Keep pushing forward—you're capable of accomplishing this!

[Swiftyos]

Great ticket thank you @Sararda1

I added this functionality today see the merged PR - #9090

[Sararda1]

Great ticket thank you @Sararda1

I added this functionality today see the merged PR - #9090

Thank you for the quick update and for implementing the functionality. It’s great to see the changes reflected so promptly. I’ll review the merged PR (#9090) and provide feedback if necessary.

Appreciate the effort!