---
title: GharScan
emoji: 🏗️
colorFrom: gray
colorTo: red
sdk: gradio
sdk_version: "6.14.0"
app_file: app.py
pinned: true
license: mit
short_description: AI Building Defect Inspector for India
tags:
  - computer-vision
  - building-inspection
  - defect-detection
  - india
  - minicpm-v
  - openbmb
  - gradio
  - backyard-ai
  - build-small-hackathon
  - track:backyard
  - sponsor:modal
  - achievement:offgrid
  - achievement:welltuned
  - achievement:offbrand
  - achievement:llama
  - achievement:sharing
  - achievement:fieldnotes
models:
  - ritvik360/gharscan-qwen2vl-lora
  - ritvik360/gharscan-qwen2vl-gguf
datasets:
  - ritvik360/gharscan-defect-dataset
  - ritvik360/gharscan-agent-traces
---

# 🏗️ GharScan — AI Building Defect Inspector for India

**Track:** Backyard AI · Build Small Hackathon 2026

> *"My neighbor Aunty Puja, a retired teacher in a 1982 DDA flat in Delhi,
> had three masons give her three different quotes for a crack she didn't
> understand. GharScan told her in 12 seconds: settlement crack, Severity 2/5,
> not structural, seal before monsoon — ₹400–600, local mason."*

---

## What It Does

Point your phone camera at any defect in your home — a crack, a damp patch,
rust stains, salt deposits — and get an instant expert-grade triage report:

- **Defect type** from an 8-class Indian residential taxonomy
- **Severity score** (1–5, color-coded) with structural risk flag
- **Immediate action** in plain language (English or Hindi)
- **Cost estimate in INR** from a 2026 Delhi/NCR market rate matrix
- **Who to call**: painter / mason / waterproofing contractor / civil engineer
- **Monsoon risk** flag (critical for Indian users pre-June)

## The Problem

Over 62% of India's urban housing was built before 1990. When a homeowner
sees a wall crack, they have three options: pay ₹2,000–8,000 for a civil
engineer, ask a mason who has a conflict of interest, or Google it and get
scared. GharScan is the affordable first answer.

## Technical Architecture

| Layer | Technology |
|---|---|
| Base model | Qwen2-VL-2B-Instruct (2.07B params) |
| Fine-tuning | LoRA r=16, 7 modules, Modal A100-80GB, 3 epochs |
| Dataset | 8,860 deduplicated images → 17,720 VQA records |
| Deduplication | CLIP ViT-B/32, cosine sim >0.95 threshold |
| Inference | HuggingFace ZeroGPU · gr.Blocks · no external API calls |
| Cost estimation | Deterministic INR lookup table (not model-generated) |
| Offline runtime | llama.cpp GGUF Q4_K_M · 941MB |
| Agent tracing | Auto-uploaded to ritvik360/gharscan-agent-traces |

## How to Use on Mobile

1. Open this Space on your phone browser
2. Tap **Take Photo** — your rear camera opens directly
3. Photograph the defect (crack, stain, damage)
4. Wait ~12 seconds for analysis
5. Read your inspection report

## REQ Compliance Checklist

| Requirement | Status | Evidence |
|---|---|---|
| REQ-01: ≤32B parameters | ✅ | Qwen2-VL-2B = **2.07B params** |
| REQ-02: Gradio Space in org | ✅ | [GharScan Space](https://huggingface.co/spaces/build-small-hackathon/GharScan) |
| REQ-03: Demo video | ✅ | [Demo Walkthrough](https://youtu.be/nKJNsk5MbcU?si=WSo-ktht_-7LvWI4) |
| REQ-04: Social media post | ✅ | [Social Post](https://x.com/ritvik_aiml/status/2066581507297472931?s=20) |
| REQ-05: ZeroGPU limit | ✅ | 1 ZeroGPU Space used |
| REQ-06: README tags | ✅ | YAML above includes all tracks + badges |

## Bonus Quest Badges Claimed

| Badge | Status | Proof |
|---|---|---|
| 🔌 **Off the Grid** — no cloud APIs | ✅ | ZeroGPU only, inference.py has zero external API calls |
| 🎯 **Well-Tuned** — published fine-tune | ✅ | [ritvik360/gharscan-qwen2vl-lora](https://huggingface.co/ritvik360/gharscan-qwen2vl-lora) |
| 🎨 **Off-Brand** — custom UI beyond defaults | ✅ | Dark concrete-grey theme, custom CSS, HTML report cards |
| 🦙 **Llama Champion** — GGUF + llama.cpp | ✅ | [ritvik360/gharscan-qwen2vl-gguf](https://huggingface.co/ritvik360/gharscan-qwen2vl-gguf) · Q4_K_M 941MB |
| 📡 **Sharing is Caring** — agent traces | ✅ | [ritvik360/gharscan-agent-traces](https://huggingface.co/datasets/ritvik360/gharscan-agent-traces) |
| 📓 **Field Notes** — blog post | ✅ | [Read on HuggingFace Blog](https://huggingface.co/blog/build-small-hackathon/gharscan-article) |

**All 6 badges claimed → Bonus Quest Champion eligible**

## Repositories

| Resource | Link |
|---|---|
| 🤖 Fine-tuned LoRA | [ritvik360/gharscan-qwen2vl-lora](https://huggingface.co/ritvik360/gharscan-qwen2vl-lora) |
| 📦 GGUF (Q4_K_M, 941MB) | [ritvik360/gharscan-qwen2vl-gguf](https://huggingface.co/ritvik360/gharscan-qwen2vl-gguf) |
| 📊 Training dataset | [ritvik360/gharscan-defect-dataset](https://huggingface.co/datasets/ritvik360/gharscan-defect-dataset) |
| 🔍 Agent traces | [ritvik360/gharscan-agent-traces](https://huggingface.co/datasets/ritvik360/gharscan-agent-traces) |
| 📝 Blog post | [huggingface.co/blog/ritvik360/gharscan](https://huggingface.co/blog/build-small-hackathon/gharscan-article) |

## Citation

If you use the GharScan dataset or model:
```
@misc{gharscan2026,
  title  = {GharScan: AI Building Defect Inspector for Indian Residential Properties},
  author = {ritvik360},
  year   = {2026},
  url    = {https://huggingface.co/spaces/build-small-hackathon/gharscan}
}
```