Day 8 of Decoding Data Science's 8-Day Building AI App Challenge: Final Submission is complete. ✅

8 days ago I had a blank Python file and an idea. Today I have a deployed, working AI application that real people can open in a browser right now. ✨

🥗 Cookable is an AI powered recipe assistant built around a problem most of us have faced. You open your fridge, you have ingredients, you have no idea what to make, and you end up ordering food anyway.

📝 You type in whatever is in your fridge, set your preferences, and the app returns 5 cookbook quality recipes with exact heat levels, timings, and visual cues. Not summaries. Actual instructions you can cook from.

Here is what Cookable can do:

🍳 Generate 5 detailed recipes from whatever ingredients you have and pantry staples

🥙 Filter by cuisine, time available, meal type, serving size, and dietary restrictions

🛒 Get 10 smart ingredient suggestions with reasons to buy them

🧈 Generate 2 bonus recipes when you pick up new ingredients

🕘 One click history pills to reload any past search instantly

🌱 Clean earthy green and cream UI built entirely with custom CSS in Streamlit

Here is what the 8 days actually looked like:

🫑 Day 1: Ideation. The idea came from standing in front of my fridge at midnight with eggs, a carrot, and half a block of cheese, and ordering food anyway.

🥦 Day 2: Environment setup. Groq API connected, Streamlit running locally, first test prompt returning a response.

🥒 Day 3: The hardest day. Writing prompts that return consistent structured JSON with cookbook quality steps every single time took far more iteration than expected. Small wording changes had a bigger impact on output quality than anything else.

🫛 Day 4: Evaluation and hardening. 20 test cases, three failure modes found and fixed, rate limit handling added, input validation tightened.

🍏 Day 5: Full UI. Custom CSS design system built entirely in Streamlit. Green and cream palette, hero image, history pills, bonus recipe section, sticky footer.

🥬 Day 6: Security. System and user message split, prompt injection guard, retry logic, JSON fallback parser, README cleanup.

🌿 Day 7: Deployment. Cookable went live on Streamlit Community Cloud. Smoke tests passed. The app works the same in a browser anywhere in the world as it does on my laptop.

🍃 Day 8: This post.

What I learned is that the hard part is never the code. It is the small decisions: how you word a prompt, how you handle a failure gracefully, how you make something feel polished when you have no time. Those decisions compound over 8 days into something you are genuinely proud of.

Thank you to Decoding Data Science for running a challenge that is open ended, practical, and forces you to ship. That combination is rare and genuinely valuable.

✨ Live app: cookable-bydurgaanand.streamlit.app

✨ GitHub: github.com/Durga-Anand-2006/cookable

@decodingdatascience @ddsbusinesscircle

#DecodingDataScience #BuildingAIApplicationChallenge #AIChallenge #BuildInPublic

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Day 8 of Decoding Data Science's 8-Day Building AI App Challenge: Final Submission is complete. ✅