---
title: ALDDS
emoji: 💻
colorFrom: purple
colorTo: red
sdk: docker
pinned: false
---

# ⚖️ Automated Legal Document Digitization System (ALDDS)

![Python](https://img.shields.io/badge/Python-3.10-blue.svg)
![Gradio](https://img.shields.io/badge/Gradio-6.14.0-orange.svg)
![PaddleOCR](https://img.shields.io/badge/PaddleOCR-2.9.1-green.svg)
![Hugging Face](https://img.shields.io/badge/Deploy-Hugging%20Face-yellow.svg)

Upload a photo of a legal document (bailable warrant, summon, etc.) and seamlessly convert it into structured JSON data. ALDDS handles the OCR, AI parsing, secure storage, and live dispatcher notifications in a single streamlined pipeline—**100% locally and offline**.

## 🚀 Pipeline & Architecture

```text
Image Upload → Cloudinary Hosting → PaddleOCR → Local Qwen2.5-1.5B LLM → MongoDB → WhatsApp Alert
```

1. **OCR Extraction**: Uses **PaddleOCR** to extract raw text from image uploads with high accuracy on complex legal layouts.
2. **AI Parsing**: Leverages a quantized **Local Qwen2.5-1.5B GGUF** model via `llama-cpp-python` to parse the unstructured OCR text into a strict 10-field JSON schema. *Zero API costs, 100% data privacy.*
3. **Secure Storage**: Automatically commits the digital record to a MongoDB database.
4. **Live Dashboard**: A real-time, searchable Police Dashboard built directly into the UI.
5. **Instant Notifications**: Dispatchers can notify Investigating Officers (IOs) instantly via zero-cost WhatsApp `wa.me` links populated from an internal CSV database.

---

## 📋 Features

- **100% Offline Local AI**: Uses `llama-cpp-python` to run a highly optimized Qwen2.5-1.5B model directly on CPU. No external API keys (like OpenAI or NVIDIA) are needed, guaranteeing zero data leaks and no per-request costs.
- **HF Free Tier Optimized**: Specifically configured (non-streaming, 1.5B model, capped threads) to run quickly on Hugging Face's free 2 vCPU tier without timing out.
- **Auto-Healing OCR**: Includes an automated fix that detects and downgrades broken PaddlePaddle 3.x versions to the stable v2.6.2 runtime during startup, preventing CPU crashes.
- **👮 Live Police Dashboard**: A dedicated tab for station dispatchers to monitor incoming warrants. Includes a real-time MongoDB search filter (by Case No, IO Name, Station, etc.).
- **💬 WhatsApp Integration**: Zero-API-cost notifications. Selecting an officer from the dropdown dynamically pulls their phone number from `officers.csv` and opens a pre-filled WhatsApp window for the dispatcher to send manually.
- **Strict Hallucination Guard**: Custom Python post-processing runs regex checks to ensure the LLM didn't fabricate names, dates, or case numbers that aren't explicitly in the OCR text.

---

## 🔑 Environment Setup

To run ALDDS locally or in the cloud, you only need the following API keys configured in a `.env` file (or as Secrets on Hugging Face). **No LLM API key is required.**

```env
# Cloudinary (Image Hosting)
CLOUDINARY_CLOUD_NAME=your_cloud_name
CLOUDINARY_API_KEY=your_api_key
CLOUDINARY_API_SECRET=your_api_secret

# MongoDB (Database)
MONGODB_URI=mongodb+srv://username:password@cluster0.../?retryWrites=true&w=majority
```

| Variable | Where to get it |
|---|---|
| `CLOUDINARY_*` | [Cloudinary Console](https://console.cloudinary.com/) → Dashboard |
| `MONGODB_URI` | [MongoDB Atlas](https://www.mongodb.com/cloud/atlas) → Database → Connect |

---

## 💻 Local Installation Guide

ALDDS handles all OCR and AI natively in Python. You do **not** need to install Tesseract or any system-level C++ compilers.

```bash
# 1. Clone repository
git clone https://huggingface.co/spaces/LovnishVerma/ALDDS
cd ALDDS

# 2. Install Dependencies
pip install -r requirements.txt

# 3. Run the Server
python app.py
```
The interface will launch at **http://127.0.0.1:7860**.

> **Note:** The first launch automatically downloads the Qwen2.5-1.5B GGUF model (~1.1 GB) and the PaddleOCR weights. Subsequent launches will use the cached files.

---

## ☁️ Hugging Face Spaces Deployment

If you are deploying ALDDS to Hugging Face Spaces, follow these steps:

1. **Set Up Secrets**: Go to your Space **Settings** → **Variables and secrets**. Add `CLOUDINARY_CLOUD_NAME`, `CLOUDINARY_API_KEY`, `CLOUDINARY_API_SECRET`, and `MONGODB_URI`.
2. **Ensure `officers.csv`**: Upload your `officers.csv` file (with `Officer_Name` and `Phone_Number` columns) to the root of the repository.
3. **Restart**: Always click **Factory Rebuild** or **Restart Space** after adding Secrets so the new environment variables are loaded into the container.

---

## 📊 Extracted JSON Schema

The LLM is strictly prompted to extract the following fields. If a field cannot be explicitly found in the text, it defaults to `null`.

| JSON Key | Description |
|---|---|
| `Case_FIR_Number` | FIR or court case reference number |
| `Act_and_Sections` | Applicable IPC/CRPC legal acts and sections |
| `Type_of_Document` | Warrant, summon, notice, etc. |
| `Target_Police_Station` | Police station the document is addressed to |
| `IO_Name_and_Belt_No` | Investigating Officer's name and belt number |
| `IO_Mobile_Number` | IO's contact number |
| `Person_Name_To_Serve` | Name of the person to be served/arrested |
| `Person_Address` | Address of the target person |
| `Court_Name` | Issuing court name |
| `Hearing_Date` | Scheduled hearing or appearance date (DD-MM-YYYY) |

---

*Built to streamline station workflows, eliminate manual data entry, and instantly notify field officers—all while keeping sensitive legal data entirely offline and private.*