# 🏢 Apartment Allocation System

This is a FastAPI-based service that manages apartment allocation from predefined Excel blocks. It reads customer records, randomly assigns apartments, tracks remaining records, and allows for data resets and exports.

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## 📦 Features

- Randomly fetch and remove customer records from Excel files.
- Track allocations and remaining records.
- Export assigned records as a downloadable Excel file.
- Reset data to the original state.
- CORS enabled for cross-origin access.

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## 🧾 Directory Overview

├── main.py # FastAPI application
├── Resources/ # Active Excel files (Block_A/B/C.xlsx)
├── Original_Folder/ # Backup of the original Excel files
├── Final_output.xlsx # Collected assigned records (auto-created)
├── Preprocessing.ipynb # Jupyter notebook for manual data preparation
└── README.md # This documentation

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## 🚀 API Endpoints

### `GET /records`
- Fetches one random record from a non-empty block (A, B, or C).
- Removes the record from the source file and adds it to `Final_output.xlsx`.

### `GET /download`
- Download the `Final_output.xlsx` file.

### `GET /reset`
- Deletes `Final_output.xlsx`.
- Restores original block files from `Original_Folder` into `Resources`.

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## 📊 Data Format Requirements

Each Excel file must include the following **required columns**:
- `Customer Name`
- `Number of Rooms`
- `Type of Apartment`

Optional column:
- `FlatID` — if present, used to generate the full `Flat_ID`.

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## ⚙️ Setup Instructions

1. Install dependencies:
```bash
pip install fastapi uvicorn pandas openpyxl

2. Run the application:

uvicorn main:app --reload

3. Open your browser and visit:

http://127.0.0.1:8000/docs

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🧹 Preprocessing

The Preprocessing.ipynb file contains logic for cleaning or preparing Excel data before use. This may involve:

• Formatting column names
• Generating missing IDs
• Removing invalid rows

Run this notebook before placing files in Resources/.

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☁️ Deployment on AWS EC2 Instance

Follow these steps to deploy this API on an AWS EC2 server:

1. Launch EC2 Instance

• Use Ubuntu (recommended).
• Open ports 22, 80, and 8000 in the Security Group.

2. Connect to EC2 via SSH

ssh -i your-key.pem ubuntu@your-ec2-public-ip

3. Install Python and Pip

sudo apt update
sudo apt install python3-pip python3-venv -y

4. Clone or Upload Your Project

git clone https://github.com/your-repo-url.git
cd your-project-folder

Or use scp to upload files:

scp -i your-key.pem -r ./local-folder ubuntu@your-ec2-public-ip:/home/ubuntu/

5. Install Dependencies in a Virtual Environment

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Or manually:

pip install fastapi uvicorn pandas openpyxl

6. Run the App

uvicorn main:app --host 0.0.0.0 --port 8000

7. Access API

Go to:

http://your-ec2-public-ip:8000/docs

For production, consider using Gunicorn + Nginx or PM2 for process management.

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