What I Built

I built Trailwise, an AI-powered hiking companion designed to help people trade screen time for time outdoors.

Currently focused on hiking destinations across Maharashtra, Trailwise helps users discover trails, check current weather, understand estimated risk levels, and find trail details such as difficulty, distance, and elevation. It also generates a hiking gear checklist using AI and lets users save their experiences in photo albums.

Code

Prathamesh2007-o7 / trailwise

Trailwise

Prathamesh Guram · 2026-10-10T10:54:11Z

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass What I Built I built Trailwise, an AI-powered hiking companion designed to help people trade screen time for time outdoors. Currently focused on hiking destinations across Maharashtra, Trailwise helps users discover trails, check current weather, understand estimated risk levels, and find trail details such as difficulty, distance, and elevation. It also generates a hiking gear checklist using AI and lets users save their experiences in photo albums. Demo Code Prathamesh2007-o7 / trailwise Trailwise Find your next good climb. Trailwise is a hiking companion web app that helps users discover trails across Maharashtra, check weather conditions, assess potential risks, prepare for hikes, and save memories through photo albums. Features Trail Discovery — Explore hiking locations with difficulty, distance, elevation, and descriptions. Live Weather — Check current weather conditions for a trail. Risk Assessment — View an estimated risk score before your hike. AI Gear Checklist — Get hiking preparation recommendations powered by Gemma 3 (4B). Photo Albums — Upload and organize memories from your hikes. Tech Stack Frontend: HTML, CSS, JavaScript Backend: Python, Flask AI Model: Gemma 3 (4B) via Ollama Weather: Open-Meteo API Photo Storage: Local uploads/ directory Getting Started Prerequisites Python 3 Ollama Installation Clone the repository git clone < repository-url > cd < repository-folder > Enter fullscreen mode Exit fullscreen mode Install Python dependencies pip install -r requirements.txt Enter fullscreen mode Exit fullscreen mode Download the AI model ollama pull gemma3:4b Enter fullscreen mode Exit fullscreen mode Start … View on GitHub How I Built It Trailwise is built with a Flask backend and an HTML, CSS, and JavaScript frontend. I integrated Gemma 3 (4B) through Ollama to power the AI-generated hiking gear checklist. The application combines trail information with weather data from Open-Meteo to help users prepare for their hikes. Uploaded photos are stored locally in an uploads/ directory. Using an open-weight model locally makes AI an integral part of the application while keeping the model deployment under my control. Why Does Open Innovation Matter? Open innovation makes it possible to experiment, build, and learn without depending entirely on closed AI APIs. Using Gemma 3 through Ollama gives Trailwise a locally runnable AI component, greater control over deployment, and the freedom to adapt the experience to outdoor planning needs. It also makes the project easier for other developers to explore, modify, and extend. I want Trailwise to be more than a finished application. I want it to be something the open-source community can improve with better trail data, smarter preparation tools, and more ways to encourage people to get outdoors. Prize Categories Best Use of Gemma — Uses Gemma 3 (4B) through Ollama to generate AI-powered hiking gear checklists tailored to outdoor adventures.

Find your next good climb.

Trailwise is a hiking companion web app that helps users discover trails across Maharashtra, check weather conditions, assess potential risks, prepare for hikes, and save memories through photo albums.

Features

Trail Discovery — Explore hiking locations with difficulty, distance, elevation, and descriptions.
Live Weather — Check current weather conditions for a trail.
Risk Assessment — View an estimated risk score before your hike.
AI Gear Checklist — Get hiking preparation recommendations powered by Gemma 3 (4B).
Photo Albums — Upload and organize memories from your hikes.

Tech Stack

Frontend: HTML, CSS, JavaScript
Backend: Python, Flask
AI Model: Gemma 3 (4B) via Ollama
Weather: Open-Meteo API
Photo Storage: Local uploads/ directory

Getting Started

Prerequisites

Python 3
Ollama

Installation

Clone the repository
```
git clone <repository-url>
cd <repository-folder>
```
Install Python dependencies
```
pip install -r requirements.txt
```
Download the AI model
```
ollama pull gemma3:4b
```
Start…

View on GitHub

How I Built It

Trailwise is built with a Flask backend and an HTML, CSS, and JavaScript frontend.

I integrated Gemma 3 (4B) through Ollama to power the AI-generated hiking gear checklist. The application combines trail information with weather data from Open-Meteo to help users prepare for their hikes. Uploaded photos are stored locally in an uploads/ directory.

Using an open-weight model locally makes AI an integral part of the application while keeping the model deployment under my control.

Why Does Open Innovation Matter?

Open innovation makes it possible to experiment, build, and learn without depending entirely on closed AI APIs.

Using Gemma 3 through Ollama gives Trailwise a locally runnable AI component, greater control over deployment, and the freedom to adapt the experience to outdoor planning needs. It also makes the project easier for other developers to explore, modify, and extend.

I want Trailwise to be more than a finished application. I want it to be something the open-source community can improve with better trail data, smarter preparation tools, and more ways to encourage people to get outdoors.

DEV Community

TrailWise