TrailSense: Touch Grass Safely

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

What I Built

Getting outside shouldn't mean staring at a 2,000-word chatbot wall of text while standing at a windy trailhead or crouching next to an unfamiliar wild mushroom. When you're outdoors, the screen should be the shortest part of the experience—just enough to verify your gear, check a hazard, respect the ecosystem, and keep moving.

TrailSense: Touch Grass Safely is a multimodal outdoor field guide, trail pre-flight safety analyzer, and flora/fauna hazard identification web and mobile app designed with an earthy "Moss & Granite" aesthetic (#1C3F2E Deep Forest Emerald, #2D5A43 Hunter Green, #E3B341 Sunlit Lichen Gold, #C86D51 Terracotta Amber, and #F9F8F3 Warm Parchment).

It is built for day hikers, backpackers, trail runners, dog owners, and amateur naturalists around two focused field workflows:

1. 🏔️ Workflow 1 — Analyze Route / Trail Ahead (Pre-Flight Check):

   • Snap or drop a photo of a trailhead sign, alpine talus pass, or unbridged river crossing.
   • TrailSense identifies the terrain class (e.g., Loose Alpine Scree & Wet Glaciated Granite), flags visible hazards with HIGH / MODERATE / LOW severity pills and tactical mitigations, recommends specific footwear, and generates an Interactive Essential Gear Checklist.
   • As you check off items in your pack (trekking poles, microspikes, drybags, offline GPX map), the Dynamic Radial Safety Meter (0–100% Trail Readiness Score) updates in real time.

2. 🍄 Workflow 2 — Identify Plant / Critter (Bio-Safety & Leave No Trace):

   • Snap or upload a macro photo of a mushroom, trailside shrub, wildflower, insect, or reptile.
   • TrailSense outputs a tactile Polaroid-Style Specimen Card with pinned badges for Common & Scientific (Binomial) Name, Confidence Level (High / Moderate / Low - Requires closer examination), and a Safety Verdict (SAFE TO TOUCH, CAUTION, DANGEROUS, TOXIC, DEADLY).
   • It pairs a 2–3 sentence natural history & ecological profile with a strict Leave No Trace & Handling Directive (e.g., why picking a single Trillium ovatum blossom starves its perennial underground rhizome, or how to decontaminate trekking poles and dog fur after brushing Toxicodendron diversilobum / Western Poison Oak).

3. 🌤️ Backcountry Quick-Toggles & Zero-Signal Field Kit:

   • Floating Weather Sync Pill: Displays wind speed, UV index, and a live ticking Sunset Countdown Timer across trailhead elevations.
   • Offline-Ready Field Kit (localStorage Cache): Pin any Polaroid route or specimen card before entering a cell dead-zone so your checklists and hazard protocols remain accessible with zero signal.

Demo

🚀 Live App on Render: TrailSense: Touch Grass Safely — Moss & Granite Field Guide

What You Can Try Live Right Now:

• One-Click Optical Presets (Zero Setup Required): Click 🟡 Alpine Scree Couloir or 🔴 Glacial River Ford in the Route Ahead dropzone, or click 🔴 Fly Agaric, 🔴 Poison Oak, or 🟢 Trillium in the Plant / Critter dropzone to see procedural field illustrations, Polaroid specimen cards, and hazard breakdowns immediately.
• Interactive Readiness Gauge: Check and uncheck pack items in the Interactive Field Readiness Kit and watch the SVG radial Dynamic Safety Meter recalculate your Trail Readiness Score from Terracotta Amber (#C86D51) to Sunlit Gold (#E3B341) to Hunter Green (#2D5A43).
• Live Camera & Custom Photos: Open the live WebRTC Trail Camera / Macro Camera (facingMode: "environment") or drop your own outdoor photos to run multimodal inference across Gemma 4 (gemma-4-31b-it, gemma-4-26b-a4b-it) and Gemini 3.8 Flash (gemini-3.8-flash).

Code

sanjana806 / Trailsense

🧭 TrailSense AI Field Guide

Touch Grass Safely & Responsibly — A multimodal outdoor field guide, trail pre-flight safety analyzer, and flora/fauna hazard identification web application powered by the Gemini Interactions API (@google/genai >= 2.28.0), Gemini 3.8 Flash, Gemini 3.5 Flash-Lite, and open-weight Gemma 4 (gemma-4-31b-it / gemma-4-26b-a4b-it).

🚀 Live Demo (Deployed on Render): TrailSense: Touch Grass Safely — Moss & Granite Field Guide

---

✨ Features

1. Two Specialized Outdoor Workflows:

   • 🏔️ Workflow 1: Trail & Terrain Pre-Flight Check
     • Classifies terrain (Loose Alpine Scree, Wet Glaciated Granite, Bog/Marsh, etc.)
     • Detects visible hazards with HIGH / MODERATE / LOW severity badges and tactical mitigations
     • Generates an Interactive Essential Pack & Footwear Checklist (persisted in localStorage with a live packing progress bar and custom gear additions)
     • Evaluates navigation risk, weather turnaround watchpoints, and cell dead-zone protocols
   • 🍄 Workflow 2: Flora & Fauna Identification…

View on GitHub

🔗 GitHub Repository: https://github.com/sanjana806/Trailsense (MIT Licensed)

How I Built It

TrailSense is built around open-weight AI models (gemma-4-31b-it and gemma-4-26b-a4b-it), a two-stage multimodal routing pipeline, and a strict JSON Schema contract powered by the @google/genai SDK (client.interactions.create).

flowchart LR
    A["📷 Optical Input\n(Dual Dropzones / WebRTC Camera / Presets)"] --> B["⚡ Stage 1 Pre-Flight Router\ngemini-3.5-flash-lite"]
    B -->|trail_terrain| C["🏔️ Workflow 1: Route & Terrain\ngemma-4-31b-it / gemini-3.8-flash"]
    B -->|flora_fauna| D["🍄 Workflow 2: Organism Safety\ngemma-4-31b-it / gemini-3.8-flash"]
    C --> E["📊 Structured JSON Schema\nPolaroid Card + Radial Safety Meter + Gear Checklist"]
    D --> E
    E --> F["📓 Offline Backcountry Cache\n& Stateful Field Radio Q&A"]

1. Open-Weight Gemma 4 + Two-Stage Multimodal Routing (src/analyzer.js)

In the field, latency and battery matter—and so does never giving a false-positive "safe" verdict on a toxic lookalike. Inspired by Swift's recent DEV benchmark on Flash-Lite routing ("Being overly cautious costs a few cents. Being over confident costs our users' trust... So: when in doubt, defer"), TrailSense implements a two-stage pipeline:

• Stage 1 (Sub-Second Modality Triage): When a hiker snaps a photo in auto-mode, gemini-3.5-flash-lite classifies the optical input into trail_terrain (Workflow 1) vs. flora_fauna (Workflow 2) using WORKFLOW_ROUTER_SCHEMA.
• Stage 2 (Structured Field Assessment with Open-Weight Gemma 4 & Gemini 3.8): The primary analysis runs on Gemma 4 31B Dense (gemma-4-31b-it), Gemma 4 26B MoE (gemma-4-26b-a4b-it), or Gemini 3.8 Flash (gemini-3.8-flash), enforcing TRAILSENSE_RESPONSE_SCHEMA via response_format:

const interaction = await client.interactions.create({
  model: selectedModel, // "gemma-4-31b-it", "gemma-4-26b-a4b-it", or "gemini-3.8-flash"
  system_instruction: systemPrompt || TRAILSENSE_SYSTEM_PROMPT,
  input: [
    { type: "image", mime_type: mimeType, data: imageBase64 },
    { type: "text", text: `${workflowDirective}${notesDirective}` },
  ],
  response_format: [
    {
      type: "text",
      mime_type: "application/json",
      schema: TRAILSENSE_RESPONSE_SCHEMA,
    },
  ],
});

2. Tactile "Moss & Granite" Instrument UI (public/app.js & public/styles.css)

Following Sylwia Laskowska's principle and Jana's code-native visual design approach that AI should power deterministic, tactile UI widgets rather than raw chat walls, the frontend turns the structured JSON output into:

• A Polaroid Specimen & Route Card with masking-tape accents and pinned taxonomic/hazard badges.
• An SVG Radial Safety Meter whose stroke offset (stroke-dashoffset) reacts dynamically as you check off items in your pack.
• Stateful Field Radio Follow-Ups that chain previous_interaction_id across turns so hikers can ask quick follow-up questions ("Is this ford safe with a dog at 4 PM?").

Why Does Open Innovation Matter?

When you are 12 miles from the nearest paved road in a mountain basin, closed, black-box AI subscriptions break down in three critical ways that open innovation solves:

1. Swappable Open-Weight Models (gemma-4-31b-it & gemma-4-26b-a4b-it) & Edge Portability: Because TrailSense is architected around open-weight Gemma 4 models alongside an open JSON Schema contract (src/prompts.js) and an open-source MIT codebase, hikers and search-and-rescue clubs aren't locked into a single proprietary cloud vendor. You can run inference via open-weight Gemma 4 endpoints today, or quantize and run Gemma weights locally on a backcountry device or trailhead Raspberry Pi kiosk with zero internet connectivity.
2. Auditable Safety Guardrails (No Hidden Prompt Drift): In wild mushroom and poisonous plant identification, a silent prompt change in a closed app can be dangerous. TrailSense makes its entire TRAILSENSE_SYSTEM_PROMPT and TRAILSENSE_RESPONSE_SCHEMA inspectable and editable right inside the UI (📜 Prompt). Our safety rule is explicit and verifiable in code: never state 100% toxicity clearance on wild mushrooms or foraged plants, and always downgrade ambiguous specimens to [Low - Requires closer examination] and [🟡 CAUTION].
3. Privacy for Backcountry Locations & Rare Species: Rare orchids, slow-growing wildflowers like Trillium ovatum, and sensitive wildlife habitats suffer from poaching when coordinates are harvested by closed commercial platforms. An open-source, self-hostable field guide with localStorage offline caching keeps your photos, field notes, and trail logs under your own control.

My Agent Session

I built TrailSense pair-programming with Antigravity (powered by Gemini 3.8 Flash), iterating from the core two-workflow field safety system prompt to the full-stack Express + @google/genai backend, the Moss & Granite UI, GitHub push, and Render deployment:

• Step 1: Designed the dual-workflow schema (trail_terrain & flora_fauna) and implemented the two-stage router + open-weight Gemma 4 (gemma-4-31b-it / gemma-4-26b-a4b-it) & Gemini 3.8 Flash analyzer in src/analyzer.js.
• Step 2: Built the Moss & Granite UI with topographic SVG contour backdrops, Dual Upload Dropzones, the Polaroid Specimen Card, the live SVG Radial Safety Meter, and Floating Weather/Offline Quick-Toggles.
• Step 3: Added offline fallback heuristics so every preset, custom upload, and Field Radio follow-up works out-of-the-box on Render even before a user supplies their own API key.

Prize Categories

• Best Use of Gemma: TrailSense integrates Google's open-weight Gemma 4 models (gemma-4-31b-it Dense and gemma-4-26b-a4b-it MoE) directly into its multimodal field analysis and stateful Field Radio Q&A pipeline, allowing users to switch seamlessly to open-weight Gemma 4 inference from the header bar.
• Best Use of Render: TrailSense is deployed as a live, full-stack Node.js web service on Render at https://trailsense.onrender.com/, serving both the zero-build Moss & Granite frontend and the /api/analyze, /api/presets/:id, and /api/followup backend endpoints.