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| 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} | |
| } | |
| ``` | |