🏢 Sinhas GmbH — Property Data Management Portal

A full-stack property data management system with a multi-user upload portal and a real-time admin dashboard — powered by Flask, MongoDB Atlas, and Chart.js.

Live Demo · GitHub Repo

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📋 Table of Contents

• Overview
• Features
• Tech Stack
• Project Structure
• Getting Started
• Environment Variables
• API Reference
• Deployment (Render)
• Screenshots

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🌐 Overview

Sinhas GmbH is a web-based data management platform built for managing Swiss property (apartment) records. It provides two distinct interfaces:

1. User Portal (/) — A clean, professional SaaS-style interface for data entry teams to upload Excel/CSV files in bulk, run structured ETL jobs, or manually enter individual property records directly into MongoDB.

2. Admin Dashboard (/admin) — A real-time analytics and data management panel for administrators to view charts, browse all MongoDB collections, search/filter records, and perform inline edits.

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✨ Features

📤 User Portal

Feature	Description
Bulk Upload	Upload any .xlsx or .csv file; each sheet becomes a MongoDB collection
Occupancy ETL	Upload the master OCCUPANCY Excel file; auto-parses into structured cities, buildings, and apartments collections
Manual Entry	Form-based single record insertion with all key property fields matching MongoDB schema
Drag & Drop	Drag and drop files directly onto the upload zones

🖥️ Admin Dashboard

Feature	Description
KPI Cards	Real-time counts — Total Properties, Occupied, Available, Buildings
4 Live Charts	Occupancy donut, Properties by City bar, System Overview bar, Apartments pie — all via Chart.js
Dynamic Sidebar	Auto-fetches all MongoDB collections; click any to browse its data
Data Table	Paginated, searchable table for any collection with configurable row limit
Load All Data	One-click button to fetch all records from the active collection
Inline Edit	Click "Edit" on any row to open a field-level modal and save changes to MongoDB

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🛠 Tech Stack

Layer	Technology
Backend	Python 3.11, Flask 3.0
Database	MongoDB Atlas (PyMongo 4.6)
Data Processing	Pandas 2.2, OpenPyXL 3.1
Frontend	Vanilla HTML, CSS, JavaScript
Charts	Chart.js (CDN)
Server (Prod)	Gunicorn 21.2
Deployment	Render (via render.yaml)
Fonts	Google Fonts — Inter

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📁 Project Structure

sinhasgmbh/
│
├── app.py                  # Flask application & all API routes
├── index.html              # User Portal — upload & manual entry UI
├── admin.html              # Admin Dashboard — charts, tables, edit modal
│
├── requirements.txt        # Python dependencies
├── render.yaml             # Render deployment configuration
├── .env                    # Local environment variables (git-ignored)
├── .gitignore
│
├── migrate_keys.py         # DB migration script (sanitizes field names)
├── import_from_excel.py    # Standalone ETL script (CLI)
├── upload_apartments.py    # CLI uploader for apartment records
├── upload_flat.py          # CLI uploader for flat records
├── analyze_excel.py        # Analysis/inspection utility for sheets
├── test_mongo.py           # MongoDB connection test script
└── test_server.py          # Flask server endpoint test script

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🚀 Getting Started

Prerequisites

• Python 3.10+ installed
• A MongoDB Atlas account with a cluster
• Git

1. Clone the Repository

git clone https://github.com/deba2k5/sinhasrealty.git
cd sinhasrealty

2. Create & Activate a Virtual Environment

# Windows
python -m venv .venv
.venv\Scripts\activate

# macOS / Linux
python -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Set Up Environment Variables

Create a .env file in the project root:

MONGO_URI=mongodb+srv://<username>:<password>@<cluster>.mongodb.net/?appName=<app>

See Environment Variables for details.

5. Run Locally

python app.py

Open your browser at:

• User Portal: http://localhost:5000/
• Admin Dashboard: http://localhost:5000/admin

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🔑 Environment Variables

Variable	Required	Description
MONGO_URI	✅ Yes	Full MongoDB Atlas connection string with credentials

The app falls back to a default URI if MONGO_URI is not set, but it is strongly recommended to set it via .env for local development and via the hosting provider's environment settings for production.

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📡 API Reference

All API endpoints return JSON.

Upload Endpoints

Method	Endpoint	Description
POST	/upload	Bulk upload any Excel or CSV file to MongoDB
POST	/upload_occupancy	ETL upload — parses Occupancy Excel into Cities/Buildings/Apartments
POST	/add_city	Insert a single city record into the cities collection
POST	/add_property	Insert a single record into the database

Admin / Data Endpoints

Method	Endpoint	Description
GET	/admin	Serve the Admin Dashboard HTML page
GET	/api/collections	List all MongoDB collection names
GET	/api/data/<collection>	Paginated, searchable data from a collection
POST	/api/update/<collection>/<doc_id>	Update a single document by _id
GET	/api/stats	Aggregated stats for KPI cards and charts
GET	/download_csv?collection=<name>	Export any collection as a downloadable CSV

/api/data/<collection> Query Parameters

Param	Type	Default	Description
page	int	1	Page number
limit	int	50	Records per page (9999 = load all)
search	string	""	Case-insensitive search across all string fields

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☁️ Deployment (Render)

The project is pre-configured for Render via render.yaml.

Steps

1. Push to GitHub

2. Create a Render Account at render.com

3. New Web Service → Connect your GitHub repo deba2k5/sinhasrealty

4. Render will auto-detect render.yaml. Confirm these settings:

   • Environment: Python
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app

5. Add Environment Variable in the Render dashboard:

   • Key: MONGO_URI
   • Value: (your MongoDB Atlas connection string)

6. Click "Deploy" — your app will be live at:

   https://sinhasgmbh.onrender.com
   

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📊 Data Schema

Shared Records (migrated)

Field	Type	Description
Apartment Address	String	Street name of the apartment
City	String	City name
Floor	Number	Floor number
POSITION	String	Unit position (Left / Right / Center)
Apartment SQMT	Number	Apartment size in square meters
NO OF ROOMS	Number	Number of rooms (e.g. 3.5)
AWN NO	Number	Internal AWN reference number
INDIV / SHR	String	Occupancy type (Family / Sharing / Individual)
Status	String	Occupancy status (OCCUPIED / AVAILABLE)

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👤 Author

Debangshu — github.com/deba2k5

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📄 License

This project is proprietary software developed for Sinhas GmbH. All rights reserved.

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Built with ❤️ for Sinhas GmbH using Flask, MongoDB Atlas, and Chart.js