Welcome to another exciting blog post! Today, we'll delve into the world of Maestro and the Gemini 1.5 models. If you're unfamiliar with Maestro, it’s a powerful tool that allows you to create applications effortlessly using simple prompts. Maestro employs a fascinating approach where an orchestrator agent breaks down your prompt into multiple steps, which are then executed by sub-agents, refined, and merged together. This method works exceptionally well, simplifying the process of creating applications.
However, Maestro faces some challenges, particularly with context limits and API pricing. Running multiple requests to the API can become expensive, and context limits can hinder the creation of larger applications. But fear not! We have a solution that addresses these issues while maintaining a similar model: Gemini 1.5 Flash and Gemini 1.5 Pro.
Why Gemini 1.5?
Gemini 1.5 Flash, developed by Google, is one of the cheapest and fastest models available. It ranks among the leading models on various leaderboards, making it a strong contender for our needs. Additionally, it offers a 1 million token context window, which is ideal for larger applications. The pricing is also reasonable, making it a cost-effective alternative.
Today, we will experiment with the Gemini 1.5 Flash model to see if it can deliver results comparable to other high-end models. We'll also test the Gemini 1.5 Pro model to evaluate its performance. Using these models with Maestro should be straightforward since the Maestro framework includes an Any API file that we can use to connect with Gemini.
Getting Started with Maestro and Gemini
Let's get started by setting up Maestro and connecting it to the Gemini models.
Clone the Maestro Repository First, clone the Maestro repository to your computer:
git clone https://github.com/Doriandarko/maestro
cd maestro2. Configure the Any API File
Open the maestro-anyapi.py file in your text editor. You need to set your Gemini API key in this file. Locate the line where the API key should be set and update it as shown below:
import os
os.environ["GEMINI_API_KEY"] = "YOUR GEMINI API KEY"Replace "YOUR GEMINI API KEY" with the actual API key you obtained from Gemini AI Studio.
3. Install Required Dependencies
Run the following command to install the necessary dependencies:
pip install -r requirements.txtpip install -r requirements.txt
4. Run the Any API File
Now, run the Any API file to start using Maestro with Gemini:
python maestro-anyapi.pyYou will be prompted to enter what you want to create. Let's start by asking it to create a snake game in HTML, CSS, and JavaScript.
5. Generate a Snake Game
Enter the prompt to create a snake game. Maestro will generate the tasks and assign them to sub-agents. Wait a few minutes for the tasks to be completed.
What do you want to create? Snake game in HTML, CSS, and JSOnce done, you'll see the generated code neatly arranged in one folder.
Testing the Gemini 1.5 Models
Now, let's test the Gemini 1.5 Flash and Pro models by creating different applications.
Creating a Modern Minimal Landing Page
Generate a Landing Page Enter the prompt to create a modern and minimal landing page. Maestro will generate the tasks and assign them to sub-agents. Wait for the tasks to be completed.
What do you want to create? Modern and minimal landing pageOnce done, you'll see the generated code arranged neatly in a folder. Run it to check the output.
Creating a To-Do App
Generate a To-Do App Enter the prompt to create a modern minimal to-do app. Maestro will generate the tasks and assign them to sub-agents. Wait for the tasks to be completed.
What do you want to create? Modern minimal to-do appOnce done, you'll see the generated code arranged neatly in a folder. Run it to check the output.
Comparing Gemini Flash and Pro Models
Let's compare the performance of the Gemini 1.5 Flash and Pro models by creating the same applications with both models.
Switch to Gemini Pro Model Open the Any API file and change the Flash model to the Pro model. Save the file and rerun the Any API file.
# Change this line in maestro-anyapi.py model_name = "gemini_pro_model"2. Generate Applications with Pro Model
Repeat the steps to create a snake game and a to-do app using the Pro model. Compare the generated outputs with those from the Flash model.
Conclusion
In our experiments, both the Gemini 1.5 Flash and Pro models performed admirably. The Flash model, being cost-effective and efficient, is a great choice for creating applications with Maestro. The Pro model, while slightly more advanced, did not show a significant difference in performance for basic tasks.
Overall, the Gemini 1.5 Flash model is highly recommended for its affordability and efficiency. You can also mix and match models to achieve even better results, depending on your specific needs.
Let me know in the comments if you have any questions or if you'd like to see more experiments with different models. If you found this post helpful, consider supporting the channel through the super thanks option below. Give this post a thumbs up and subscribe for more content. See you in the next post!
Happy coding! 🚀
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