title	PropertyVision BI x RAG
emoji	🏢
colorFrom	blue
colorTo	indigo
sdk	docker
app_port	7860

PropertyVision BI x RAG

Executive-grade real-estate decision intelligence for Ho Chi Minh City and Hanoi. BI dashboards, price prediction, what-if simulation, GIS/planning views, and a retrieval-first AI assistant for leadership reporting.

Quick Links

• What You Get
• Quick Start
• Environment Variables
• Hugging Face Space
• Metro Impact Data
• Useful Backend Endpoints
• Documentation

What You Get

• 📊 Executive dashboard with market KPIs and trend views
• 🧩 Multi-dimensional slice-dice analysis
• 📈 Price prediction and ROI simulation
• 🗺️ Planning/GIS map with opportunity and risk views
• 🤖 RAG-based assistant grounded in market, planning, legal, and metro context
• 📝 Export-friendly periodic report view for leadership updates

At a Glance

Item	Value
Release	v1.0.0
Main stack	FastAPI + React + Vite
AI layer	Hosted Qwen + retrieval-first RAG
Markets covered	Ho Chi Minh City, Hanoi
Metro scope	Bến Thành, Tham Lương, HCMC TOD, Hanoi TOD
Primary dataset	datasets/clean_dataset.csv

Architecture

flowchart LR
    U[User] --> F[React + Vite Frontend]
    F --> B[FastAPI Backend]

    HF[Hugging Face Dataset<br/>SpringWang08/hanoi-hcmc-real-estate] --> D[datasets/clean_dataset.csv]
    D --> M[Runtime Data Mart<br/>SQLite + Pandas]
    M --> B

    B --> A[Analytics / Prediction / Simulation]
    B --> G[GIS / Planning / Metro impact]
    B --> R[RAG Retriever]
    R --> Q[Hosted Qwen]
    Q --> O[Executive response]

    A --> F
    G --> F
    O --> F

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Repository Layout

PropertyVision/
├── backend/             FastAPI app, analytics, RAG, metro/planning data
├── frontend/            React + Vite UI
├── datasets/            Processed dataset, dataset notes, cached reference data
├── docs/                Diagrams, baseline notes, demo scripts, UI spec
├── data/                SQLite runtime artifacts
├── README.md           Project overview and setup
└── requirements.txt    Python dependencies

Data Model

The application works with a processed dataset and runtime-generated analytical layers:

• datasets/clean_dataset.csv is the main processed dataset
• data/*.db is created at runtime for facts, planning zones, legal notes, and metro impact profiles
• the backend also builds a cached street-level reference for richer RAG answers

Automatic dataset behavior

On first backend start, the app will try to:

1. download the processed dataset from Hugging Face
2. store it locally as datasets/clean_dataset.csv
3. fall back to the local file if it already exists
4. fall back to raw reference data in datasets/raw/ if needed

This means a fresh clone can usually start without manual data copying.

Dataset links:

• https://huggingface.co/datasets/SpringWang08/hanoi-hcmc-real-estate
• https://huggingface.co/datasets/tinixai/vietnam-real-estates

Quick Start

1. Clone

git clone https://github.com/QuangVoAI/PropertyVision.git
cd PropertyVision

2. Set up the backend

macOS / Linux:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn backend.main:app --reload

Windows PowerShell:

python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt
uvicorn backend.main:app --reload

Backend URL:

http://localhost:8000

3. Set up the frontend

cd frontend
npm install
npm run dev

Frontend URL:

http://localhost:5173

Hugging Face Space

This repository already includes a Dockerfile, so you can upload it to Hugging Face Spaces as a Docker Space with minimal extra work.

• The backend serves the built frontend from frontend/dist
• The app runs on port 7860 in Spaces
• Use the root README.md as the Space landing page

If you want a shorter Vietnamese guide for the same project, see README.vi.md.

Environment Variables

The app works in retrieval-only mode without a hosted LLM token, but you can enable hosted generation for richer analysis.

Recommended variables:

HF_TOKEN=your_hugging_face_token
PROPERTYVISION_HF_QWEN_MODEL=Qwen/Qwen2.5-1.5B-Instruct
PROPERTYVISION_HF_INFERENCE_PROVIDER=auto
PROPERTYVISION_USE_HOSTED_QWEN=true

Optional .env file at the project root is supported.

Notes

• If no hosted model is available, the app still runs with retrieval-backed analysis.
• If you want faster local debugging with less AI overhead, keep the hosted model disabled.

Core Features

1. 📌 Tổng quan điều hành

• KPI trọng yếu
• xu hướng điều hành dài hạn
• kiểm tra giả định tăng trưởng
• khuyến nghị dành cho ban điều hành

2. 🏙️ Thông tin thị trường

• so sánh khu vực
• mặt bằng giá
• phân tích phân khúc
• insight theo thành phố / quận / loại tài sản

3. 🔎 Phân tích đa chiều

• slice-dice theo khu vực và phân khúc
• bảng phân đoạn tiềm năng cao
• xem danh sách địa chỉ theo từng record

4. 📊 Mô phỏng đầu tư

• giá trị tương lai
• lợi nhuận vốn
• ROI tích lũy
• thời gian hoàn vốn
• khuyến nghị mua thêm / giữ / bán bớt

5. 🗺️ Bản đồ quy hoạch

• opportunity score
• risk level
• bộ lọc theo ROI, score và rủi ro
• dữ liệu quy hoạch, legal, và metro impact

6. 🤖 Trợ lý phân tích

• hỏi đáp theo ngữ cảnh RAG
• nguồn trích dẫn rõ ràng
• khuyến nghị ngắn gọn theo giọng điều hành

7. 📝 Báo cáo định kỳ

• bản tóm tắt kiểu executive report
• hỗ trợ in ra PDF từ trình duyệt

Metro Impact Data

The backend now includes a dedicated metro-impact layer for real estate analysis:

• Ho Chi Minh City metro line 1
• Ben Thanh central station
• Tham Luong station / metro line 2 gateway
• Hanoi TOD and urban rail corridor references

This layer is available through the RAG pipeline and the data-ops view so the assistant can answer questions like:

• “Metro ảnh hưởng giá nhà như thế nào?”
• “Bến Thành và Tham Lương tác động ra sao?”
• “Hà Nội và TP.HCM khác nhau thế nào quanh ga metro?”

There is also an API endpoint:

GET /api/metro/impact

Refreshing Data

If you change planning, legal, metro, or market sources, refresh the runtime layers:

POST /api/etl/run
POST /api/rag/reindex

You can also use the Theo dõi dữ liệu page in the UI to do this.

Useful Backend Endpoints

• GET /api/metadata
• POST /api/analytics
• POST /api/slice-dice
• POST /api/predict
• POST /api/what-if
• GET /api/map/districts
• GET /api/planning/zones
• GET /api/metro/impact
• POST /api/rag/reindex
• GET /api/etl/status
• GET /api/ai/status

Notes For Contributors

• The repository is designed so that a new clone can run end-to-end with minimal manual setup.
• Avoid committing generated runtime files from data/ and downloaded dataset artifacts unless intentional.
• If you update the dataset or knowledge base, reindex RAG so the assistant reflects the latest state.

📚 Documentation

• Project Diagrams
• Technical Baseline
• Demo Script
• UI Design Spec

🎁 Release Notes

• Official v1.0.0 release for executive-grade real-estate intelligence.
• Adds a cleaner onboarding README so new contributors can clone and run faster.
• Includes metro-impact data and RAG coverage for Ho Chi Minh City and Hanoi.
• Keeps the AI experience retrieval-first, with hosted Qwen available when configured.

🪪 License / Data Use

This project aggregates public, processed, and derivative analytical data for BI and demonstration purposes. Please review the source terms of any upstream data before redistribution or commercial use.