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Nikhil Pradhan
Nikhil Pradhan

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NatureQuest-AI

#devchallenge #hf26challenge

Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass Submission 🌿

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

🌿 NatureQuest-AI: Mindful Everyday Nature Discovery

What I Built

We live in an age of pervasive screen fatigue. Notifications, endless feeds, and fluorescent-lit workspaces trap us in perpetual digital overdrive. Many people believe that "connecting with nature" requires booking an expensive vacation, packing heavy hiking gear, or driving hours to a national park. As a result, we overlook the rich, thriving micro-ecosystems happening right in front of us every single day—in the cracks of our sidewalks, on our apartment balconies, around street trees, and in neighborhood parks.

NatureQuest-AI was built to change that. It is an intelligent, privacy-first nature companion that transforms ordinary routines—a 10-minute balcony coffee break, a 15-minute walk around the block, or a quiet pause on a park bench—into grounded, mindful micro-expeditions.

Key Features & How It Gets People Outdoors

  • Tailored Micro-Expeditions in Under 30 Minutes: Instead of passive reading or generic Wikipedia summaries, NatureQuest-AI generates structured, sensory observation quests customized to your exact setting (Balcony/Window, Urban Street, Backyard, Park, Forest Trail) and available time (5–30 minutes).
  • Sensory & Scientific Observation Guides: Quests guide you through concrete physical exploration:
    • Leaf Architecture & Venation Detective: Examining microscopic circulatory systems of living leaves to uncover how plants transport water.
    • Solar Sundial & Shadow Drift: Marking subtle shadow drift across natural surfaces to witness the kinetic rotation of planet Earth in real time.
    • Acoustic Soundscape Mapping: Closing your eyes and mapping near vs. distant bird frequencies and wind currents.
    • Pavement Crack Micro-Ecology: Investigating resilient mosses, lichen colonies, and ant foraging highways in urban concrete.
  • Strictly Non-Destructive Conservation Ethic: Every generated quest contains explicit Leave-No-Trace principles and safety guidance, reminding explorers to observe without disturbing living organisms or damaging fragile habitats.
  • Personal Field Journal (Full CRUD): Capture what you saw, heard, and smelled. Record reflection notes, weather context, and custom tags. You can edit, search, and export your discoveries to clean Markdown (.md) or structured JSON (.json) with a single click.
  • Honest, Truth-Based Progress Tracking: No synthetic streaks, dopamine traps, or vanity game points. NatureQuest-AI visualizes authentic progress based strictly on your verified journaled sessions, highlighting habitats explored and observation minutes logged.

Demo 

  git clone https://github.com/palak2122-ag/NatureQuest-AI.git
  cd NatureQuest-AI
  pip install -r requirements.txt
  streamlit run app.py
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(Zero configuration needed—the app launches immediately with full offline capabilities!)

🎥 Walkthrough Preview:

  1. Home Overview: Quick-launch curated quests, review your time spent outdoors, and inspect your latest journal entries.
  2. AI Nature Discovery: Configure your surroundings, select your available time and observational focus (Botany, Wildlife, Geology, Sky/Atmosphere, Mindfulness), and generate an interactive quest complete with a session timer.
  3. Explore Library: Browse 20+ varied nature archetypes filterable by environment, difficulty, and duration.
  4. Field Journal: Record your thoughts and sensory observations directly into your private nature diary.
  5. Insights & Milestones: Track your genuine observation hours and celebrate authentic outdoor exploration milestones.

Code

You can find the full source code and documentation on GitHub:

GitHub logo palak2122-ag / NatureQuest-AI

An open-source AI app that generates personalized outdoor nature missions.

NatureQuest-AI 🌿

Discover Nature in Your Everyday Surroundings
An AI-powered, mindful nature discovery and micro-ecology platform that transforms everyday strolls, balconies, and urban streets into immersive outdoor discoveries.

Python Streamlit Gemini Ollama Privacy Tests

🌟 Key Features

  1. AI Nature Discovery Engine (Multi-Tier)

    • Google Gemini Integration: Cloud inference generating structured observation guides tailored to precise duration, micro-habitat, and language.
    • Local Gemma via Ollama: Optional, fully private offline neural inference on your local machine.
    • Curated Offline Recommendation Engine: 100% reliable fallback with 20+ rich archetypes, smart scoring, and multilingual support (English, Hindi, Hinglish). Never blocked by missing API keys or internet downtime.

  2. Honest Source Attribution & Safety

    • Plans explicitly display their source: Gemini AI, Local Gemma via Ollama, or Offline recommendation engine.
    • Built-in conservation rules: strictly non-destructive observation guidelines that protect urban wildlife and plant life.

  3. Curated Explore Library

    • 20+ diverse, scientifically grounded quests (Leaf Venation Architecture, Solar Shadow Drift, Acoustic Soundscape…
View on GitHub

Repository Highlights

  • 100% Python-Powered: Built with Streamlit, Pydantic v2, and clean modular architecture.
  • Extensive Test Suite: 38 automated unit tests covering storage concurrency, Pydantic schema validation, duplicate prevention, and AI provider fallback probes.
  • Accessible Design System: Custom nature-inspired palette (Warm Ivory #FBF9F5, Deep Forest Green #1B3B2B, Sage #5C8374, and Plus Jakarta Sans typography).

How I Built It

NatureQuest-AI was engineered from the ground up around open-weight models, local inference, and a transparent multi-tier AI architecture.

                           ┌────────────────────────────┐
                           │   NatureQuest-AI App       │
                           │   (Streamlit + UI System)  │
                           └──────────────┬─────────────┘
                                          │
                            Provider Probe & Health Check
                                          │
              ┌───────────────────────────┼──────────────────────────┐
              ▼                           ▼                          ▼
    ┌───────────────────┐       ┌───────────────────┐      ┌───────────────────┐
    │   Google Gemini   │       │    Local Gemma    │      │  Curated Offline  │
    │   (Cloud API)     │       │   (Ollama Open)   │      │ Recommendation    │
    │  gemini-1.5-flash │       │     gemma2:2b     │      │      Engine       │
    └───────────────────┘       └───────────────────┘      └───────────────────┘
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1. Open-Weight Local Inference with Gemma 2 & Ollama

When stepping outdoors, internet connectivity can be spotty or completely unavailable on park trails and nature walks. To ensure genuine accessibility and privacy, NatureQuest-AI natively supports Gemma 2 (2B / 9B) via Ollama.

  • Local & On-Device: Connects to http://localhost:11434 to run Gemma locally without sending a single byte of observation or prompt data over the internet.
  • Strict Schema Enforcement: Prompts are constrained to return strictly validated JSON matching our NaturePlan schema, parsed and verified through Pydantic v2.

2. Multi-Tier Provider Manager with Transparent Attribution

In NatureQuest-AI, we believe in radical transparency:

  • If a quest is generated by Google Gemini (1.5 Flash), the UI clearly displays ✨ Source: Google Gemini AI.
  • If generated by Local Gemma, it shows ⚡ Source: Local Gemma via Ollama.
  • If generated without any AI connection, it proudly displays 📘 Source: Offline recommendation engine.

3. Curated Offline Recommendation Engine (Zero-Failure Guarantee)

Technology should never be the barrier that stops someone from touching grass. If you don't have an API key or don't have Ollama installed, NatureQuest-AI automatically falls back to an internal knowledge engine featuring 20+ rich micro-ecology archetypes with smart habitat matching and multilingual adaptation (English, Hindi, Hinglish).

4. Local-First & Atomic Data Persistence

All personal notes, field observations, and completed quests are saved locally in data/ using atomic file writes and thread locks. Your private reflections never leave your machine.

Why Does Open Innovation Matter?

The very philosophy of "Touch Grass" is rooted in grounding, personal sovereignty, and stepping away from monopolistic walled gardens.

Here is why open innovation and open-weight models are vital for this project:

  1. Nature Doesn't Have Wi-Fi (Offline Resilience):
    The best moments of nature observation happen away from cellular towers—in deep woods, shaded canyons, or quiet park groves. Closed cloud APIs become useless the moment your signal drops. Open-weight models like Gemma 2 running locally on consumer hardware allow anyone to take intelligent, context-aware observation companions directly into the wilderness.

  2. Absolute Data Sovereignty for Personal Reflections:
    A field journal is an intimate record of your thoughts, quiet moments, and mental health. Users shouldn't have to surrender their private reflections or location patterns to closed corporate servers to enjoy AI assistance. Open-source AI makes local-first privacy an everyday reality.

  3. No Financial Paywalls for Outdoor Wellness:
    Connecting with the natural world should be free and accessible to everyone. Closed commercial APIs impose ongoing per-token costs and subscription walls. Open models give students, educators, community centers, and curious individuals unrestricted access to AI-powered discovery without credit cards.

  4. Transparent, Community-Auditable Prompts:
    With open innovation, our prompt templates, conservation guardrails, and observation mechanics are fully visible and community-auditable. There are no hidden biases or opaque filters dictating how people experience their physical environment.

My Agent Session

During the development of NatureQuest-AI, pair programming and agentic workflows were leveraged to build the component library, establish Pydantic v2 data models, write 38 automated test cases, and solve real-world UI edge cases (such as preventing Markdown indentation code block triggers in Streamlit when rendering rich HTML quest cards).

Prize Categories

  • Primary Track: Touch Grass — Designed from the ground up to disconnect users from digital feeds and immerse them in everyday outdoor micro-ecology.
  • Open-Source AI / Open Weights — Native integration with Google's open-weight Gemma 2 running locally and privately via Ollama.

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