MicroSprout: Spend Less Time on Screens, More Time in the Soil

Mohamed Adib Shaikh — Sun, 11 Oct 2026 15:00:32 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

MicroSprout is an anti-screen, microclimate garden planner designed around a simple principle: spend 60 seconds looking at your screen, then spend the next 30 minutes with your hands in the soil.

Many digital gardening apps keep users glued to dashboards, notifications, and forums. MicroSprout takes hyper-local space and climate parameters—sunlight exposure, container setups, soil condition, and region—and uses an open-weight AI model to immediately generate:

Current sowing candidates suited to the space.
Soil and bed preparation notes.
An immediate, concrete 15-minute physical outdoor task.

The workflow concludes with a direct command to put the device down and step outside.

Demo

Code

import torch
import gradio as gr
from transformers import pipeline

# Fully open, ungated model (no login or HF_TOKEN required)
pipe = pipeline(
    "text-generation",
    model="Qwen/Qwen2.5-1.5B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

def plan_garden(region, sunlight, garden_type, soil_condition):
    system_instruction = (
        "You are an offline Microclimate Garden Advisor designed for the 'Touch Grass' challenge. "
        "Your mission is to get gardeners outside immediately. "
        "Analyze the user's localized conditions and output exactly:\n"
        "1. Current Sowing Candidates (2-3 plants ready to sow right now)\n"
        "2. Soil & Bed Prep (2 concise bullet points)\n"
        "3. Today's 15-Minute Outdoor Task (a clear actionable step in the dirt)\n"
        "End with: 'Now close your laptop, grab your trowel, and step outside!'"
    )

    user_query = (
        f"Location/Climate: {region}\n"
        f"Sunlight: {sunlight}\n"
        f"Space: {garden_type}\n"
        f"Soil/Moisture: {soil_condition}"
    )

    messages = [
        {"role": "system", "content": system_instruction},
        {"role": "user", "content": user_query}
    ]

    response = pipe(messages, max_new_tokens=300, temperature=0.6)
    return response[0]["generated_text"][-1]["content"]

# Gradio Interface
with gr.Blocks(theme=gr.themes.Soft(), title="MicroSprout Planner") as demo:
    gr.Markdown("# 🌱 MicroSprout: Microclimate Garden Assistant")
    gr.Markdown(
        "Enter your space parameters to get your planting tasks for today. "
        "The goal: spend 60 seconds on this screen, then get your hands in the dirt."
    )

    with gr.Row():
        region_input = gr.Textbox(
            label="Location / Region", 
            placeholder="e.g., Pune / Mumbai plateau, USDA Zone 9b, or coastal mild"
        )
        sun_input = gr.Dropdown(
            choices=["Full Sun (6+ hrs)", "Partial Shade (3-6 hrs)", "Full Shade (<3 hrs)"], 
            value="Full Sun (6+ hrs)", 
            label="Sunlight Exposure"
        )

    with gr.Row():
        type_input = gr.Dropdown(
            choices=["Balcony Pots & Containers", "Raised Beds", "Direct In-Ground", "Window Sill"], 
            value="Raised Beds", 
            label="Growing Setup"
        )
        soil_input = gr.Textbox(
            label="Soil / Moisture Condition", 
            placeholder="e.g., Dry clay, well-draining potting mix, damp loam"
        )

    action_btn = gr.Button("Get Today's Planting Plan", variant="primary")
    output_display = gr.Markdown(label="Outdoor Action Plan")

    action_btn.click(
        fn=plan_garden, 
        inputs=[region_input, sun_input, type_input, soil_input], 
        outputs=output_display
    )

demo.launch(share=True)

How I Built It

MicroSprout uses the open-weight Qwen 2.5 1.5B Instruct model via Hugging Face transformers and PyTorch, running in an accelerated Kaggle GPU environment:

Efficient Inference: Loaded with bfloat16 precision, enabling sub-2-second generation latency without heavy compute requirements.
Structured Instruction Architecture: Rather than allowing open-ended conversational filler, the prompt constrains the model to provide actionable planting recommendations and an immediate outdoor chore.
Fast Interactive Interface: Built using Gradio, providing a clean web interface and a shareable URL directly from a notebook environment.

Why Does Open Innovation Matter?

Gardening and outdoor exploration shouldn't be gated behind closed, subscription-based cloud APIs:

Edge & Offline Capability: Community plots, balconies, and rural gardens often suffer from spotty or non-existent cellular coverage. Compact open-weight models can be packaged to run locally on low-cost edge devices or laptops with zero internet connection.
Data Privacy: Users should not have to upload personal coordinates, property dimensions, or private layouts to corporate cloud backends.
Zero Operating Overhead: Open-source weights allow local clubs, schools, and community gardens to run custom localized assistants permanently without incurring recurring SaaS API fees.

Prize Categories

SafePlate: Cooking Safe, Allergen-Free Meals for My Friend with Lightweight Open AI

Mohamed Adib Shaikh — Sun, 11 Oct 2026 14:48:39 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

SafePlate is an open-source meal companion designed for a close friend dealing with severe dietary restrictions (lactose and nut allergies).

Cooking together or finding quick recipes often involves anxiety over cross-contamination or hidden allergens in commercial recipe blogs. SafePlate takes my friend's exact restrictions along with whatever raw ingredients we have in the pantry, and generates a personalized, allergen-verified recipe in seconds.

Demo

Code

import torch
import gradio as gr
from transformers import pipeline

 ## Load lightweight open-weight model (Ungated, no login required)
pipe = pipeline(
    "text-generation",
    model="Qwen/Qwen2.5-1.5B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

def plan_meal_for_friend(friend_name, allergies, pantry_items):
    system_instruction = (
        "You are an empathetic, precise meal planner designed for the 'Build for a Friend' challenge. "
        "Your top priority is food safety and simplicity. "
        "Output format:\n"
        "1. Dish Name (Safe & custom-made for the friend)\n"
        "2. Safety Verification (explicitly confirm why the dangerous allergens are omitted)\n"
        "3. 3-Step Simple Recipe (clear instructions under 20 minutes)\n"
        "End with a short, warm note to serve to the friend."
    )

    user_query = (
        f"Friend's Name: {friend_name}\n"
        f"Strict Allergies/Dislikes: {allergies}\n"
        f"Available Ingredients: {pantry_items}"
    )

    messages = [
        {"role": "system", "content": system_instruction},
        {"role": "user", "content": user_query}
    ]

    response = pipe(messages, max_new_tokens=300, temperature=0.6)
    return response[0]["generated_text"][-1]["content"]

## Build Gradio UI
with gr.Blocks(theme=gr.themes.Soft(), title="SafePlate: Cook for a Friend") as demo:
    gr.Markdown("# 🍲 SafePlate: Build for a Friend")
    gr.Markdown("Create safe, personalized home-cooked meals tailored around your friend's strict food allergies.")

    with gr.Row():
        name_input = gr.Textbox(label="Friend's Name", placeholder="e.g., Rahul / Sarah")
        allergies_input = gr.Textbox(label="Strict Allergies / Restrictions", placeholder="e.g., Peanuts, Dairy, Gluten")

    pantry_input = gr.Textbox(label="Available Pantry Ingredients", placeholder="e.g., Rice, chicken, carrots, olive oil, garlic, soy sauce")
    action_btn = gr.Button("Generate Safe Meal Plan", variant="primary")
    output_display = gr.Markdown(label="Custom Safe Recipe")

    action_btn.click(
        fn=plan_meal_for_friend, 
        inputs=[name_input, allergies_input, pantry_input], 
        outputs=output_display
    )

demo.launch(share=True)

How I Built It

SafePlate runs on the open-weight Qwen 2.5 1.5B Instruct model via Hugging Face transformers and PyTorch, running in an accelerated Kaggle GPU environment:

Lightweight Edge Inference: Utilizing bfloat16 precision, the model performs fast inference in under 2 seconds without external paid APIs.
Deterministic Safety Prompting: The prompt constrains the model to verify allergen absence explicitly before outputting the 3-step cooking method.
Interactive UI: Served through Gradio with an ephemeral live URL.

Why Does Open Innovation Matter?

Health & Data Privacy: Personal dietary sensitivities, medical allergies, and household pantry habits shouldn't be logged or monetized by proprietary SaaS platforms.
Offline Kitchen Utility: Recipes are needed in home kitchens and cabins where internet connections can be unreliable. Compact open-weight models can run offline directly on cheap hardware.
Community Customization: Open weights allow anyone to adapt the model for regional diets and specialized culinary traditions without recurring API bills.

DEV Community: Mohamed Adib Shaikh

MicroSprout: Spend Less Time on Screens, More Time in the Soil

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

Prize Categories

SafePlate: Cooking Safe, Allergen-Free Meals for My Friend with Lightweight Open AI

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

Prize Categories