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

🌿 What I Built

Most AI applications try to maximize the amount of time users spend interacting with AI.

WildLens does the exact opposite.

WildLens is an AI-powered nature exploration companion that encourages people to go outdoors by turning ordinary walks into small discovery missions. The core guiding principle of WildLens is simple:

"Use AI to help people explore the real world, then get them off the screen."

"The goal isn't more screen time. The goal is more world time."

How it works:

1. Snap & Analyze: Take a single photo of a leaf, flower, bird, tree, insect, or rock found outdoors.
2. Open-Source Vision AI Result: Receive a fast identification ("This looks like..."), confidence rating, short description, nature fact, region prevalence, and safety disclaimers for mushrooms/wild items.
3. Touch Grass Challenge: Get a real-world sensory observation challenge tailored to your discovery (e.g. "Look around and find two other leaves that have a different shape" or "Spend two minutes listening quietly for another bird call").
4. Phone Down Mode: Switch into a minimal screen-free interface with a calming 5-minute timer and Web Audio API forest synthesizer that explicitly tells you: "📵 Now put your phone away and explore."
5. Return Check-In & Journal: When you return from exploring, share a quick reflection and log your real-world experience into a personal, non-addictive Nature Journal.

📽️ Demo

Here is the intended flow when taking WildLens outdoors:

Open WildLens ➔ Go Outside ➔ Find Something ➔ Take 1 Photo ➔ AI Analyzes ➔ Receive Touch Grass Challenge ➔ Put Phone Away ➔ Explore ➔ Return Check-In 

Main Screenshots & Features

• Look Up. Look Around. Hero landing with 1-click sample presets for instant camera-less testing.
• Open Vision AI Analysis Card: Detailed botanical/zoological overview with safety caution disclaimers whenever confidence is low or caution items (fungi, toxic foliage, wild berries) are detected.
• Phone Down Mode: Screen-free 5-minute countdown timer with built-in ambient breeze and songbird audio.
• Return Check-In Modal: Quick selection pills (Saw something, Heard something, Found something new) + reflection notes + celebratory nature confetti.
• Nature Journal & Stats: Category filtering (Leaf, Flower, Tree, Bird, Insect, Rock, Plant, Fungus) and mindful outdoor metrics (no infinite feeds, likes, or streak traps!).

💻 Code

🌱 AI helps you discover something outside — then tells you to put the phone down 

Open WildLens ➔ Go Outside ➔ Find Something Interesting ➔ Take 1 Photo ➔ AI Analyzes ➔ Receive Touch Grass Challenge ➔ Put Phone Away ➔ Explore ➔ Return Check-In 

Repository: github.com/keerthisree-25/wildLens

🛠️ How I Built It

WildLens is built as a complete full-stack web application:

• Frontend: React, Vite, Tailwind CSS, Lucide Icons, Canvas Confetti, and Web Audio API for ambient nature sounds.
• Backend: Node.js, Express, SQLite (better-sqlite3), and Multer for image uploads.
• Open-Source Vision AI Engine: A modular multi-provider vision engine built from the ground up to prioritize open-weight models:
  • Ollama Local Inference: Supports running llava, qwen2.5-vl, moondream, or florence2 directly on a laptop or device via OLLAMA_HOST=http://localhost:11434.
  • Hugging Face Inference API: Integrates with open-weight models like Salesforce/blip-image-captioning or Qwen/Qwen2.5-VL-7B-Instruct.
  • Embedded Heuristics Fallback: An out-of-the-box zero-dependency fallback engine that analyzes visual features, color profiles, and botanical heuristics so developers can run and test the app immediately without needing API keys or hardware setups.

The vision provider is developer-configurable via environment variables:

VISION_MODEL=llava VISION_PROVIDER=auto 

💡 Why Does Open Innovation Matter?

Open innovation is essential for WildLens and nature exploration for four main reasons:

1. Offline Backcountry Trail Readiness: By running open-weight models like LLaVA or Moondream via Ollama, WildLens can function on a laptop or mobile device deep in national parks and hiking trails without cell signal or internet connectivity.
2. Complete Data Privacy: Your location and outdoor exploration photos stay on your device or private server, keeping your personal nature observations off centralized proprietary AI servers.
3. Developer Freedom & Model Swappability: Developers can easily swap vision models (LLaVA, Qwen-VL, Moondream, Florence-2) or plug in fine-tuned local botanical/zoological models as computer vision advances.
4. Community Prompts & Observation Logic: The observation task generator and safety evaluation rules are open source, allowing the community to improve species identification prompts and safety guardrails.

🤖 My Agent Session

Here is the complete agent session transcript showing how WildLens was conceptualized, built, tested, and verified:

Initializing server, database schema, vision AI provider, touch grass challenge generator, and responsive UI components.

Successfully created WildLens application with full-stack Node.js backend, SQLite database, React Vite Tailwind frontend, and modular open-weight vision AI integration.

🏆 Prize Categories

• Overall Hacktoberfest Open-Source AI Challenge: Week 1 Winner (#hf26challenge)