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| title: ALDDS | |
| emoji: 💻 | |
| colorFrom: indigo | |
| colorTo: red | |
| sdk: gradio | |
| sdk_version: 6.14.0 | |
| python_version: '3.13' | |
| app_file: app.py | |
| pinned: false | |
| # ⚖️ Automated Legal Document Digitization System (ALDDS) | |
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| 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. | |
| ## 🚀 Pipeline & Architecture | |
| ```text | |
| Image Upload → Cloudinary Hosting → Tesseract OCR → NVIDIA LLMs → MongoDB → WhatsApp Alert | |
| ``` | |
| 1. **OCR Extraction**: Uses Tesseract to extract raw text from image uploads. | |
| 2. **AI Parsing**: Leverages advanced NVIDIA models (`qwen/qwen3-coder-480b-a35b-instruct`, Llama 3) to parse the unstructured OCR text into a strict 10-field JSON schema. | |
| 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 | |
| - **Multi-Model Fallback**: The app cycles through a priority list of NVIDIA LLMs ensuring maximum uptime and reliability during the parsing phase. | |
| - **👮 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. | |
| --- | |
| ## 🔑 Environment Setup | |
| To run ALDDS locally or in the cloud, you need the following API keys configured in a `.env` file (or as Secrets on Hugging Face). | |
| ```env | |
| # Cloudinary (Image Hosting) | |
| CLOUDINARY_CLOUD_NAME=your_cloud_name | |
| CLOUDINARY_API_KEY=your_api_key | |
| CLOUDINARY_API_SECRET=your_api_secret | |
| # NVIDIA API (LLM Parsing) | |
| NVIDIA_API_KEY=your_nvidia_api_key | |
| # 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 | | |
| | `NVIDIA_API_KEY` | [NVIDIA Build](https://build.nvidia.com/) → API Catalog → Get API Key | | |
| | `MONGODB_URI` | [MongoDB Atlas](https://www.mongodb.com/cloud/atlas) → Database → Connect | | |
| --- | |
| ## 💻 Local Installation Guide | |
| ### 1. Install Tesseract OCR (System Binary) | |
| Python's `pytesseract` requires the underlying Tesseract engine to be installed on your OS. | |
| **Windows** | |
| 1. Download installer from: https://github.com/UB-Mannheim/tesseract/wiki | |
| 2. Run installer (default path: `C:\Program Files\Tesseract-OCR\`) | |
| 3. The app is hardcoded to look for this path on Windows. | |
| **Linux (Debian/Ubuntu)** | |
| ```bash | |
| sudo apt-get update | |
| sudo apt-get install tesseract-ocr | |
| ``` | |
| **macOS** | |
| ```bash | |
| brew install tesseract | |
| ``` | |
| ### 2. Run the App | |
| ```bash | |
| # Clone repository | |
| git clone https://huggingface.co/spaces/LovnishVerma/ALDDS | |
| cd ALDDS | |
| # Install Dependencies | |
| pip install -r requirements.txt | |
| # Run the Server | |
| python app.py | |
| ``` | |
| The interface will launch at **http://127.0.0.1:7860**. | |
| --- | |
| ## ☁️ Hugging Face Spaces Deployment | |
| If you are deploying ALDDS to Hugging Face Spaces, follow these critical steps: | |
| 1. **Set Up Secrets**: Go to your Space **Settings** -> **Variables and secrets**. Add all variables from your `.env` file as **Secrets**. | |
| 2. **System Dependencies**: Hugging Face runs Debian Linux. The repository includes a `packages.txt` file telling Hugging Face to install `tesseract-ocr` and `libtesseract-dev` during the Docker build. | |
| 3. **CRLF Warning**: Ensure that `packages.txt` is saved with Unix (`LF`) line endings. If it has Windows (`CRLF`) line endings, the Hugging Face Docker build will fail with a `Package not found` error. | |
| 4. **Restart**: Always click **Restart Space** after adding or modifying 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 found, 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.* | |