Alright, folks, buckle up! Today I’m diving deep into a little project I’ve been tinkering with – I’m calling it “AI Menu.” It’s basically an attempt to use AI to suggest dinner ideas based on what I’ve got lurking in the fridge. Sounds simple, right? Well, let’s see how it went down.
It all started with me staring into my fridge, AGAIN. You know the feeling? Plenty of ingredients, but zero inspiration. I thought, “Hey, I’m a techy kinda guy, there’s gotta be a way to automate this torture.” So, the idea of “AI Menu” was born.
First things first, I needed to choose an AI model. After a bit of digging, I settled on OpenAI’s GPT-3. It’s powerful and I’ve messed around with it before, so the learning curve wasn’t too steep. I figured I could prompt it with my ingredients and it would spit out some recipe ideas.
Next up, coding. I decided to use Python because it’s my go-to language for AI stuff. I started by setting up a simple script that takes a list of ingredients as input, formats them into a prompt for GPT-3, and then sends the prompt to OpenAI’s API. The code looked something like this (simplified, of course):
- Import the OpenAI library.
- Define a function that takes a list of ingredients.
- Format those ingredients into a prompt: “Suggest a recipe using [ingredient1], [ingredient2], and [ingredient3].”
- Send the prompt to the OpenAI API.
- Parse the response and return the suggested recipe.
Then came the fun part – testing! I threw in “chicken,” “broccoli,” and “rice.” The first few attempts were…interesting. GPT-3 suggested things like “Chicken and Broccoli Ice Cream Surprise.” Yeah, no thanks. I quickly realized I needed to refine my prompt. I added phrases like “healthy,” “easy to make,” and “avoid desserts.”
That helped a lot! I started getting suggestions like “Chicken and Broccoli Stir-fry” and “Baked Chicken with Broccoli and Rice.” Much better! But the recipes were still a bit vague. They lacked details like cooking times, temperatures, and specific instructions.
So, back to the code. I modified the prompt again, adding “Provide a detailed recipe with cooking times and instructions.” This time, the responses were significantly more useful. I got actual recipes I could follow, with step-by-step instructions and estimated cooking times.
But there were still some hiccups. Sometimes, GPT-3 would suggest ingredients I didn’t list. Or it would come up with recipes that were way too complicated for a weeknight dinner. I realized I needed to add some error handling and filtering to the script.
I added a check to make sure the suggested recipe only used ingredients I provided. And I added a “complexity” filter to weed out recipes with too many steps or exotic ingredients. This involved some extra coding and a bit of trial and error, but eventually, I got it working pretty well.
Now, I have a little Python script that takes my fridge inventory and spits out decent dinner ideas. It’s not perfect, but it’s a huge improvement over staring blankly into the fridge every night. Plus, it’s been a fun little project to learn more about AI and Python.
Lessons learned:
- Prompt engineering is key. The better your prompt, the better the results.
- Error handling is crucial. AI models aren’t always perfect, so you need to be prepared for unexpected outputs.
- Start simple and iterate. Don’t try to build the perfect solution right away. Start with a basic version and then add features and improvements over time.
Where do I go from here? I’m thinking about building a simple web interface for my “AI Menu.” That way, I can easily enter my ingredients and get recipe suggestions on my phone. Maybe I’ll even add a feature to save my favorite recipes for later. Who knows? The possibilities are endless!
So, that’s my “AI Menu” project in a nutshell. It’s a work in progress, but it’s already making my life a little easier (and my dinners a little more interesting). Hope you found this little walkthrough helpful. Now, if you’ll excuse me, I’m off to try out a new recipe suggested by my AI overlord!