Creating Quick Applications with Aider and Generative AI for Coding

In the age of rapid digital transformation, the ability to create applications quickly and cost-effectively has become a game-changer. Aider and generative AI tools like chatGPT provide an unprecedented opportunity to streamline the development process, enabling you to go from idea to implementation in record time.

In this blog post, we’ll explore how I used Aider and generative AI to create a Streamlit-based image generator that leverages the Hugging Face API to produce stunning visuals — all for less than $0.50 in chatGPT costs. Here’s how it came together.

Streamlit Web Interface

What Is Aider and How Does It Help?

Aider is an AI-powered coding assistant designed to help developers write, debug, and optimize code. It works in tandem with chatGPT models, offering:

• Rapid prototyping: Generate functional code snippets in minutes.
• Debugging support: Identify and fix errors with ease.
• Documentation: Provide explanations or refactor existing code.

Combined with generative AI, Aider bridges the gap between conceptualization and execution, allowing developers to create applications faster than ever before.

The Application: Hugging Face API Image Generator

I built a simple yet powerful application using Streamlit, Hugging Face’s Inference API, and the Stable Diffusion model. The app enables users to generate AI-generated images by inputting a prompt and selecting a style. Below is a breakdown of the app’s functionality.

Hugginface provides detailed API documentation

How It Works

1. User Interface with Streamlit: A clean and intuitive UI allows users to enter a prompt and choose from different styles (e.g., Realistic, Artistic, Cartoon, Abstract).
2. Backend API Calls: The Hugging Face InferenceClient communicates with the Stable Diffusion model to generate images based on the user’s input.
3. Logging and Error Handling: Logging ensures transparency, while error handling provides clear feedback for troubleshooting.

Aider writes the code, you can watch it work

Code Walkthrough

Here’s the code for the application:

import streamlit as st
import logging
from huggingface_hub import InferenceClient

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Initialize the InferenceClient with the model and token
client = InferenceClient("stabilityai/stable-diffusion-xl-base-1.0", token="hf_YOUR_TOKEN_HERE")

# Set up the Streamlit interface
st.title("Huggingface API Image Generator")
st.write("Enter a prompt and select a style to generate an image.")

# Collect user input
prompt = st.text_input("Prompt", "A beautiful landscape")

# Collect user input for style
style = st.selectbox("Select Style", ["Realistic", "Artistic", "Cartoon", "Abstract"])

# Function to call the Huggingface API using InferenceClient
def generate_image(prompt, style):
    # Concatenate prompt and style
    styled_prompt = f"{prompt} in {style} style"
    try:
        logger.info("Generating image with styled prompt: %s", styled_prompt)
        result = client.text_to_image(styled_prompt)
        logger.info("Image generated successfully")
        return result
    except Exception as e:
        logger.error("An error occurred: %s", str(e))
        st.error(f"An error occurred: {str(e)}")
        return None

# Generate image on button click
if st.button("Generate Image"):
    result = generate_image(prompt, style)
    if result:
        if isinstance(result, bytes):
            st.image(result, caption="Generated Image")
        else:
            st.write("Response:", result)

Key Features

1. Streamlit UI: Simple and responsive design for user input.
2. Hugging Face Integration: Seamless API interaction for generating images.
3. Customizable Prompts and Styles: Enhance creativity with style options.
4. Efficient Costs: Achieves impressive results for less than $0.50.

~5 seconds after pressing Generate

Why Use Generative AI for Coding?

Generative AI tools like GPT accelerate development by:

• Reducing boilerplate: Automatically generating repetitive code.
• Providing insights: Offering suggestions for best practices.
• Lowering costs: Delivering high-quality applications without heavy investment.

In this project, the combined power of chatGPT and Aider enabled me to create a fully functional application quickly, saving time and resources.

Next Steps: Experiment and Innovate

This example is just the beginning. You can expand on this application by:

• Adding more styles: Explore other artistic options like surrealism or minimalism.
• Enhancing functionality: Incorporate additional AI models for text, sound, or video generation.
• Deploying the app: Use Streamlit Cloud or other hosting services to share your creation with the world.

Final Thoughts

Using Aider and generative AI tools like chatGPT is a game-changer for developers and hobbyists alike. Whether you’re building small prototypes or scaling up complex projects, these technologies empower you to bring your ideas to life with minimal effort and cost.

So, why wait? Dive into the world of generative AI and start creating your own applications today! Let your creativity run wild — just like the beautiful images this app generates.

Find this project on GitHub: SDXL-InferenceAPI

If you enjoyed this article or found it helpful, I’d be incredibly grateful for your support. You can follow me for more content like this or help fuel my creativity by buying me a coffee at buymeacoffee.com/polymathic3d.