This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass
What I Built
Engineering and college students across Bhubaneswar (from campuses like SOA/ITER, KIIT, OUTR, AIIMS, and Silicon) often spend entire weekends inside air-conditioned hostel rooms scrolling feeds or grinding code blocks. Many migrant hostellers want to explore, but lack of local transit clarity, intense coastal humidity windows, and Odia language barriers keep them trapped indoors.
CampusBahar (PravasiTrail) is an intentional anti-screen exploration engine. Its mission is to be used for less than 30 seconds:
- Hostel-to-Trail Routing: Select your campus hub or auto-detect your location with GPS to discover 18 natural, forest, and ancient rock-cut destinations across Odisha.
- TabPFN Diurnal Climate Windows: Predicts heat, humidity, and UV solar comfort using a prior-data fitted tabular model so students step out only during safe dawn or dusk hours.
- 4-Way Transparent Transit Matrix: Real-time fare comparisons across Mo Bus routes, shared autos, bike taxis, and split cabs.
- Hands-Free Audio Walk (ElevenLabs): Converts the pocket guide into clear audio so students can put their phones away, put in their earphones, and navigate purely through sight and touch.
- Colloquial Street Odia: Provides authentic Odia script, phonetic transliteration, and street tips to negotiate with local auto drivers and bus conductors without stress.
Demo
- Live Application: https://campusbahar.vercel.app
- Production Backend API: https://campusbahar.onrender.com
- API Health Check: https://campusbahar.onrender.com/api/health
Code
The entire open-source repository (FastAPI backend + Memphis postmodern UI) is available on GitHub:
Pratyush-Panda-2006
/
CampusBahar
A zero-screen-time weekend exploration engine for Bhubaneswar students. Built with TabPFN tabular models and Groq Gemma 2 to check coastal heat windows, transit fares, and trail guides before stepping outside.
How I Built It
CampusBahar is built around a lightweight, privacy-focused open-weight AI architecture:
-
TabPFN Foundation Model (
tabpfn): We fitted TabPFN on diurnal coastal microclimate cycles (bbsr_weather_comfort.csv). Unlike heavy ML pipelines that require hyperparameter tuning or training iterations, TabPFN delivers instant zero-shot tabular inference directly on CPU, classifying hours intoOptimal,Moderate, orAvoidconditions based on temperature, relative humidity, and UV index. -
Gemma 2 (
gemma2-9b-itvia Groq): Synthesizes concise 4-point micro field cards (Transit Route, Sensory Touch-and-See Checkpoint, Driver Phrase in Odia, and Digital Detox Rules) tailored specifically for non-Odia students from Jharkhand, Bihar, and UP. -
Google Gemini API (
gemini-2.5-flash): Powers the bilingual Odia translation engine, handling colloquial street phrasing and phonetic transliteration to demystify transit communication. -
ElevenLabs Speech API (
eleven_turbo_v2_5): Generates lightweight audio walk narrations with the Rachel voice model, allowing students to disconnect from screens entirely once on the trail. - Cloud Infrastructure: The asynchronous FastAPI backend is deployed on a Render Web Service container in Singapore, and the frontend is hosted on Vercel with pure static edge rewrites.
Why Does Open Innovation Matter?
Open-source and open-weight models give developers the power to solve hyper-local civic and student challenges without being tied to expensive, closed enterprise platforms.
Using TabPFN and Gemma 2 means community tools like CampusBahar can run inference with minimal latency and predictable overhead. Open innovation allows models to be adapted to local micro-climates, regional bus networks, and campus realities, giving students an accessible, open-weight toolkit that encourages them to close their laptops and step outside into the real world.
Prize Categories
- Best Use of TabPFN
- Best Use of Gemma
- Best Use of ElevenLabs
- Best Use of Render
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