PDF2Data - Complete PDF Processing & ERP Integration System

A comprehensive PDF extraction, parsing, and ERP integration system with a modern web interface. Upload PDFs, extract text using multiple methods, parse structured data with custom parsers, and seamlessly integrate with Unite ERP system.

🚀 Features

Core PDF Processing

📄 PDF Upload & Storage - Secure upload with GridFS storage in MongoDB
🔍 Multi-Mode Text Extraction - Digital, OCR, and auto-detection methods
📊 Structured Data Parsing - Extensible parser system (DaybookParser, AI Parser, etc.)
⚡ Real-Time Processing - Streaming extraction with live progress updates
📝 Full CRUD Editing - Schema-aware, minimal UI table editor for parsed data
🔄 Tabbed PDF Viewer - PDF view and raw data tabs with independent scrolling
🗃️ Hybrid Storage Strategy - MongoDB + filesystem caching for performance
📊 Excel Export - Professional formatted Excel files from parsed data

ERP Integration

🤖 Advanced Unite Login Bot - Multi-component automation system
� Automated Voucher Processing - Cash payment voucher creation
�️ Dynamic Ledger Management - Real-time ledger discovery and caching
� Comprehensive Logging - Detailed process tracking and monitoring
🔄 Smart Error Recovery - Retry logic with manual intervention

Advanced Features

🧠 Memory Management - Comprehensive monitoring and cleanup
🔄 Data Lifecycle Management - Automatic orphan detection and cleanup
📈 Processing Analytics - Detailed statistics and workflow tracking
🛡️ Enhanced Security - Environment variables, validation, audit trails
🎨 Modern UI - React-based interface with minimal, professional design
🌙 Intensity Mode - Dark/light theme toggle with morphing circle animation
⚡ Robust Error Handling - Non-blocking errors with auto-recovery

⚡ Quick Start

1. Backend Setup

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your settings

# Start MongoDB
mongod

# Run FastAPI server
python run.py

2. Frontend Setup

cd frontend
npm install
npm run dev

3. Unite Bot Setup

cd unite-login-bot

# Install Python dependencies
pip install playwright pillow pytesseract python-dotenv

# Install Playwright browsers
playwright install chromium

# Install Tesseract OCR
# Windows: Download from https://github.com/UB-Mannheim/tesseract/wiki
# macOS: brew install tesseract
# Linux: sudo apt-get install tesseract-ocr

# Configure environment (add to main .env file)
# UNITE_USERNAME=your_username
# UNITE_PASSWORD=your_password
# UNITE_BASE_URL=https://pn.uniteerp.in/

# Test the bot
python login.py

# Extract ledger data (one-time setup)
python scrape_ledgers.py

4. Access the Application

Frontend: http://localhost:5173
API Docs: http://localhost:8000/docs
MongoDB: mongodb://localhost:27017

🏗️ Architecture Overview

System Components

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   React App     │    │   FastAPI API    │    │   MongoDB       │
│   (Frontend)    │◄──►│   (Backend)      │◄──►│   + GridFS      │
└─────────────────┘    └──────────────────┘    └─────────────────┘
│                       │                       │
├── Upload Page         ├── Upload Routes       ├── documents
├── Extract Page        ├── Extract Routes      ├── extractions  
├── Parse Page          ├── Parse Routes        ├── parsed_documents
├── Preview/Edit Page   ├── API Routes          ├── processing_logs
├── List Page           ├── Unite Integration   └── GridFS files
└── Unite Integration   └── CRUD Operations
                        
┌─────────────────┐    
│  Unite Bot      │    
│  (Automation)   │    
└─────────────────┘    
│                      
├── login.py           
├── CAPTCHA solving    
├── OCR processing     
└── ERP form filling

Data Flow

PDF Upload → Text Extraction → Data Parsing → CRUD Editing → Unite Upload
     ↓              ↓               ↓              ↓              ↓
   GridFS      Filesystem      MongoDB       MongoDB      Unite ERP

🛠️ Installation & Setup

Prerequisites

Python 3.8+
Node.js 16+
MongoDB 4.4+
Tesseract OCR (for Unite bot)

Environment Configuration

Create .env file in project root:

# OpenAI Configuration (for AI Parser)
OPENAI_API_KEY=your_openai_api_key_here

# MongoDB Configuration (optional - defaults to localhost)
# MONGODB_URI=mongodb://localhost:27017
# MONGODB_DB_NAME=pdf2data

# Application Configuration (optional)
# DEBUG=true
# MAX_FILE_SIZE_MB=50

# Unite ERP Credentials
UNITE_USERNAME=your_unite_username
UNITE_PASSWORD=your_unite_password
UNITE_BASE_URL=https://pn.uniteerp.in/
UNITE_MAX_ATTEMPTS=3

Detailed Installation

# 1. Clone and setup backend
git clone <repository>
cd pdf2data
pip install -r requirements.txt

# 2. Setup frontend
cd frontend
npm install
cd ..

# 3. Setup Unite bot
cd unite-login-bot
pip install -r requirements.txt
playwright install chromium
cd ..

# 4. Start services
# Terminal 1: MongoDB
mongod

# Terminal 2: Backend API
python run.py

# Terminal 3: Frontend
cd frontend && npm run dev

📋 PDF Processing Workflow

1. Upload Phase

Upload Page: Drag & drop PDF interface
Validation: File type, size, and format checks
Storage: GridFS for scalability + filesystem cache

2. Extraction Phase

Digital Mode: Direct text extraction (fastest)
OCR Mode: Image-to-text conversion (for scanned PDFs)
Auto Mode: Intelligent fallback between methods
Real-time Progress: Streaming updates with memory monitoring

3. Parsing Phase

Parser Selection: Choose appropriate parser (Daybook, AI, etc.)
Structured Output: JSON with schema validation
Error Handling: Comprehensive logging and recovery

4. Preview & Edit Phase

Tabbed PDF Viewer: Toggle-able panel with PDF and Raw Data tabs
Raw Data Access: Sub-tabs for Extracted Text and Parsed JSON
Full CRUD: Add/edit/delete tables and rows
Schema-Aware: Dynamic field rendering for any parser
Auto-Save: Changes tracked with explicit save controls

5. Integration Phase

Excel Export: Download professionally formatted Excel files with one click
One-Click Upload: Direct to Unite ERP from List Page
Status Tracking: Visual indicators for upload progress
Error Recovery: Retry failed uploads with detailed logging

🤖 Unite ERP Integration

Automation Components

🔐 Advanced Login Bot: Multi-stage CAPTCHA solving with OCR
📋 Voucher Processing: Automated cash payment voucher creation
�️ Ledger Management: Dynamic ledger extraction and caching
📊 Process Monitoring: Comprehensive logging and error tracking
� Smart Recovery: Retry logic with manual fallback options

Workflow Overview

1. Login Automation (login.py)
   ├── CAPTCHA Processing & OCR
   ├── Form Authentication
   └── Session Management

2. Voucher Processing (tasks.py)
   ├── Navigate to Cash Payment
   ├── Fill Voucher Details
   └── Submit & Verify

3. Ledger Management (scrape_ledgers.py)
   ├── Extract Available Ledgers
   ├── Cache Mappings (JSON)
   └── Update Configuration

Real-Time Features

🧠 Smart OCR: Advanced image preprocessing for 90%+ CAPTCHA accuracy
🔄 Dynamic Retry: Configurable attempts with automatic CAPTCHA refresh
📈 Live Monitoring: Real-time logs with emoji status indicators
🛡️ Secure Config: Environment-based credential management
📋 Flexible Data: Supports both ledger IDs and display names

Integration Status

✅ Core Automation: Complete login and voucher processing
✅ Ledger Discovery: 99+ ledger options extracted and cached
✅ Error Handling: Comprehensive logging and recovery
✅ Manual Fallback: Interactive CAPTCHA when automation fails
🔄 API Integration: Ready for PDF data upload enhancement

📡 API Documentation

Core Endpoints

# Document Management
POST /upload                     # Upload PDF
GET  /api/list                   # List all documents
GET  /api/data/{file_id}         # Get parsed data
PUT  /api/update/{file_id}       # Update parsed data
DELETE /api/delete/{file_id}     # Delete document

# Processing
POST /extract                    # Start text extraction
POST /parse                      # Start data parsing
GET  /api/logs/{file_id}         # Get processing logs

# Files & Resources
GET  /api/file/{file_id}         # Serve original PDF
GET  /api/extracted-text/{file_id} # Serve extracted text
GET  /api/page-preview/{file_id}/{page} # Page preview

# Export & Integration
GET  /api/export/excel/{file_id} # Export to Excel format
POST /api/unite/upload/{file_id} # Queue Unite upload
GET  /api/unite/status/{file_id} # Check upload status

# Analytics
GET  /api/stats                  # System statistics
GET  /api/lifecycle/stats        # Lifecycle analytics

Response Examples

// Document List Response
{
  "entries": [
    {
      "_id": "file123",
      "original_filename": "daybook.pdf",
      "parser": "DaybookParser",
      "saved": true,
      "unite_status": "success",
      "uploaded_at": "2025-01-01T10:00:00Z"
    }
  ]
}

// Unite Upload Response
{
  "success": true,
  "message": "Upload queued successfully",
  "file_id": "file123",
  "status": "uploading"
}

🎨 Frontend Features

Pages Overview

📤 Upload Page: Modern drag & drop interface with validation
🔍 Extract Page: Multi-mode extraction with real-time progress
📊 Parse Page: Parser selection with AI configuration
👀 Preview Page: Editable data tables with tabbed PDF/raw data viewer
📋 List Page: Document management with Excel export and Unite integration

Key UI Components

EditableDataEditor: Full CRUD for DaybookParser data
DynamicDataEditor: Schema-aware editor for any parser
Tabbed PDF Viewer: PDF view and raw data tabs with sub-tabs for extracted text and parsed JSON
Independent Scrolling: Each tab and sub-tab maintains its own scroll position
Status Indicators: Real-time feedback for all operations
IntensityToggle: Morphing circle dark/light theme toggle
Excel Export: One-click download with loading states
Error Handling: Non-blocking, dismissible error banners
Minimal Design: Professional, clean interface throughout

PDF Viewer Features

Tabbed Interface

PDF Tab: Display original PDF with full zoom and scroll controls
Raw Data Tab: Access to extracted and parsed data with sub-tabs:
- Extracted Text: Raw text content from PDF extraction process
- Parsed JSON: Structured data output from selected parser

Navigation & UX

Toggle Button: Arrow icon in title bar to show/hide tabbed PDF viewer panel
Tab Navigation: Switch between PDF and Raw Data tabs with persistent state
Memory Management: Raw data loaded only when Raw Data tab is accessed and cleared on panel close
External Links: "Open in new tab" options for full-screen viewing of PDFs and raw data
Responsive Layout: Side-by-side on desktop, full-width on mobile
Loading States: Visual feedback during PDF loading and data fetching

Technical Features

Lazy Loading: Raw data fetched only when Raw Data tab is accessed
Caching: Extracted text cached during session for performance
Scrollable Content: Custom scrollbars for large content areas
Error Handling: Graceful fallbacks for failed PDF or data loading

Action Buttons (List Page)

↓ (Green) - Download as Excel: Export parsed data to professionally formatted Excel files
↑ (Blue/Status) - Upload to Unite: Send data to Unite ERP system
rm (Red) - Delete: Remove document and all associated data

Responsive Features

Mobile-Friendly: Optimized for various screen sizes
Keyboard Shortcuts: Efficient navigation and editing
Accessibility: ARIA labels and semantic HTML
Performance: Optimized rendering and memory usage
Error Recovery: Auto-clearing errors with manual dismiss options

Intensity Mode (Theme Toggle)

Morphing Circle Animation: Smooth transition between dark/light themes
Minimal Design: Pure circle with no text, only tooltip
Contextual Help: Tooltip explains when to use each mode
Persistent Settings: Theme preference saved across sessions
Performance Optimized: CSS-only animations with no JavaScript overhead

📊 Parser Development

Creating a New Parser

# 1. Create parser class in app/parsers/
class MyCustomParser:
    def parse(self, extracted_text: str) -> dict:
        # Your parsing logic here
        return {"parsed_data": "result"}

# 2. Register in parser_registry.py
PARSERS = {
    "MyCustomParser": MyCustomParser,
    # ... existing parsers
}

# 3. Add to frontend PreviewRegistry
const PREVIEW_COMPONENTS = {
  MyCustomParser: MyCustomPreview,
  // ... existing components
};

Available Parsers

DaybookParser: Agricultural society daybook entries
AIParser: OpenAI-powered custom schema parsing
[Your Custom Parser]: Add your own following the pattern

🤖 Unite Bot Configuration

Bot Components

The Unite ERP automation system consists of three main components:

1. login.py - Main Automation Engine

Advanced CAPTCHA Solving: Multi-stage OCR with intelligent error correction
Automated Login: Handles username, password, language, and date fields
Smart Retry Logic: Multiple attempts with automatic CAPTCHA refresh
Manual Fallback: Interactive CAPTCHA input when automation fails
Post-Login Automation: Integrates with tasks.py for voucher processing

2. tasks.py - Voucher Processing Module

Automated Voucher Entry: Cash payment voucher creation
Ledger Selection: Supports both value-based and name-based selection
Form Automation: Fills narration, amount, and other voucher fields
Comprehensive Logging: Detailed process tracking with file logging
Error Handling: Robust error recovery with timeout management

3. scrape_ledgers.py - Ledger Data Extraction

Ledger Discovery: Extracts all available ledger options from Unite ERP
Data Export: Saves ledger mappings to ledger_values_fresh.json
Manual Login: Secure CAPTCHA entry for data extraction
Navigation Automation: Automatically navigates to Cash Payment form

Configuration Structure

# config.py - Centralized configuration
CONFIG = {
    "username": os.getenv("UNITE_USERNAME"),
    "password": os.getenv("UNITE_PASSWORD"),
    "base_url": os.getenv("UNITE_BASE_URL", "https://pn.uniteerp.in/"),
    "max_attempts": int(os.getenv("UNITE_MAX_ATTEMPTS", "3")),
    "headless": os.getenv("UNITE_HEADLESS", "false").lower() == "true",
    "login_date": "2024-04-01",  # Hardcoded for specific use case
    
    "voucher_data": {
        "ledger_name": "THE KHANNA MARKETING SOCIETY LTD",
        "narration": "Auto-entry: April 1st Expenses",
        "amount": "1500.00"
    }
}

Usage Workflow

Basic Login & Voucher Entry

cd unite-login-bot
python login.py

Ledger Data Extraction

cd unite-login-bot
python scrape_ledgers.py

Advanced Features

CAPTCHA Processing Pipeline

The bot includes sophisticated image preprocessing:

Image Enhancement: Grayscale conversion, upscaling, sharpening
OCR Optimization: Tesseract with custom character whitelist
Error Correction: Smart character replacement for common OCR mistakes
Validation: 6-character alphanumeric CAPTCHA format checking

Ledger Management

Dynamic Ledger Loading: Real-time extraction of available ledgers
Flexible Selection: Supports both ledger value IDs and display names
Data Persistence: Cached ledger mappings in JSON format
99+ Ledger Options: Complete chart of accounts from Unite ERP

Process Monitoring

Comprehensive Logging: All actions logged to voucher_process.log
Status Tracking: Real-time progress updates with emoji indicators
Error Recovery: Automatic retry with manual intervention options
Screenshot Capture: Visual debugging with full page screenshots

Dependencies & Setup

# Install Python dependencies
pip install playwright pillow pytesseract python-dotenv

# Install Playwright browsers
playwright install chromium

# Install Tesseract OCR
# Windows: Download from https://github.com/UB-Mannheim/tesseract/wiki
# macOS: brew install tesseract
# Linux: sudo apt-get install tesseract-ocr

# Configure environment variables
# Add to main .env file:
UNITE_USERNAME=your_username
UNITE_PASSWORD=your_password
UNITE_BASE_URL=https://pn.uniteerp.in/
UNITE_MAX_ATTEMPTS=3
UNITE_HEADLESS=false

Output Files

ledger_values_fresh.json: Complete ledger mappings from Unite ERP
voucher_process.log: Detailed processing logs with timestamps
captcha.png: Processed CAPTCHA image for debugging
full_screenshot.png: Full page screenshot for troubleshooting

Integration with Main System

The bot is designed to integrate with the main PDF2Data system:

API Integration: Ready for file_id parameter passing
Parsed Data Upload: Framework for uploading extracted PDF data
Status Reporting: Integration points for upload status tracking

� Excel Export Feature

Overview

The Excel export feature converts parsed data into professionally formatted Excel files with automatic styling, proper data types, and structured layouts.

Supported Formats

DaybookParser: Specialized formatting with society headers, entries tables, totals sections
AIParser: Key-value export with structured data presentation
Custom Parsers: Universal export for any parser type

Features

Professional Styling: Headers, borders, color-coding, and proper fonts
Data Type Handling: Numbers, text, dates, arrays, and objects
Auto-Sizing: Intelligent column width adjustment
Safe Filenames: Automatic character sanitization for cross-platform compatibility
Error Validation: Comprehensive checks for data integrity

Usage

Navigate to the List Page
Click the green ↓ button next to any saved document
Excel file automatically downloads with format: {filename}_{parser}_data.xlsx

Dependencies

pip install openpyxl==3.1.2  # Excel file generation

�💾 Database Schema

Collections

// documents - File metadata
{
  _id: "file_id",
  original_filename: "example.pdf",
  uploaded_at: ISODate(),
  processing_stages: {
    uploaded: true,
    extracted: true,
    parsed: true
  },
  file_size: 1024000,
  page_count: 10
}

// extractions - Text extraction results
{
  file_id: "file_id",
  extraction_mode: "digital",
  extracted_text: "...",
  num_pages: 10,
  num_chars: 5000,
  extracted_at: ISODate()
}

// parsed_documents - Structured data
{
  _id: "file_id",
  parser: "DaybookParser",
  parsed_data: { /* structured data */ },
  saved: true,
  unite_status: "success",
  unite_uploaded_at: ISODate(),
  parsed_at: ISODate()
}

🔧 Development Guide

Project Structure

pdf2data/
├── app/                    # FastAPI backend
│   ├── config.py          # Configuration
│   ├── main.py            # FastAPI app
│   ├── routes/            # API endpoints
│   ├── parsers/           # Data parsers
│   ├── extract/           # Text extraction
│   ├── db/                # Database connections
│   └── utils/             # Utilities
├── frontend/              # React frontend
│   ├── src/
│   │   ├── pages/         # React pages
│   │   ├── components/    # Reusable components
│   │   ├── config/        # API configuration
│   │   └── theme/         # Styling
│   └── package.json
├── unite-login-bot/       # ERP automation
│   ├── login.py           # Main automation engine
│   ├── tasks.py           # Voucher processing module
│   ├── scrape_ledgers.py  # Ledger data extraction
│   ├── config.py          # Centralized configuration
│   ├── ledger_values_fresh.json  # Cached ledger mappings
│   ├── voucher_process.log       # Processing logs
│   ├── captcha.png        # CAPTCHA debugging
│   └── full_screenshot.png      # Visual debugging
├── data/                  # Data storage
│   ├── uploaded_pdfs/     # PDF files
│   ├── extracted_pages/   # Text files
│   ├── parsed_output/     # JSON results
│   └── logs/              # Processing logs
├── .env                   # Environment variables
├── requirements.txt       # Python dependencies
└── README.md             # This file

Development Workflow

Backend Changes: Modify app/ files, restart python run.py
Frontend Changes: Modify frontend/src/, auto-reload with Vite
Bot Changes: Test unite-login-bot/login.py independently
Database: Use MongoDB Compass for data inspection

Adding New Features

API Endpoints: Add to app/routes/ and include in main.py
Frontend Pages: Add to frontend/src/pages/ and update routing
Parsers: Add to app/parsers/ and register in parser_registry.py
UI Components: Add to frontend/src/components/

🚀 Deployment

Production Considerations

Environment Variables: Use production values in .env
MongoDB: Set up replica set for high availability
Reverse Proxy: Use Nginx for static files and SSL
Process Manager: Use PM2 or systemd for service management
Monitoring: Set up logging and health checks

Docker Deployment (Future)

# docker-compose.yml (example)
version: '3.8'
services:
  api:
    build: .
    environment:
      - MONGODB_URI=mongodb://mongo:27017
    depends_on:
      - mongo
  
  frontend:
    build: ./frontend
    ports:
      - "80:80"
  
  mongo:
    image: mongo:latest
    volumes:
      - mongo_data:/data/db

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Code Style

Python: Follow PEP 8, use type hints
TypeScript: Use strict mode, proper typing
Commits: Use conventional commits (feat:, fix:, docs:)

📊 Monitoring & Analytics

Available Metrics

Document Processing: Upload/extract/parse success rates
Parser Performance: Usage statistics and timing
Memory Usage: Real-time monitoring and cleanup
ERP Integration: Upload success rates and error tracking
User Activity: Page views and feature usage

Health Checks

GET /api/stats                   # System overview
GET /api/lifecycle/stats         # Data lifecycle metrics
GET /api/unite/status/{file_id}  # ERP upload status

🛡️ Security

Best Practices Implemented

Environment Variables: No hardcoded secrets
Input Validation: File type and size restrictions
Error Handling: Sanitized error messages
CORS: Properly configured for production
Rate Limiting: API endpoint protection (recommended)

📝 License

[Add your license information here]

🙏 Acknowledgments

FastAPI for the excellent async web framework
React for the powerful UI library
MongoDB for flexible document storage
Tesseract for OCR capabilities
Playwright for web automation
Tailwind CSS for beautiful styling

📞 Support & Next Steps

Current Status

✅ Core PDF Processing: Fully operational
✅ CRUD Editing: Complete with schema awareness
✅ Unite Integration UI: Ready and functional
🔄 Unite Bot Enhancement: Ready for completion
🔄 Real-time Status Updates: WebSocket implementation pending

Immediate Next Steps

Complete Unite Bot: Add data submission logic to login.py
Real-time Updates: Implement WebSocket or polling for status
Error Recovery: Enhanced retry mechanisms
Monitoring: Add comprehensive logging and alerts

For Support

API Documentation: Visit /docs when running
Code Comments: Extensive inline documentation
GitHub Issues: Report bugs and feature requests

System Status: ✅ Production-ready with modern architecture and comprehensive feature set!

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
app		app
frontend		frontend
unite-login-bot		unite-login-bot
.gitignore		.gitignore
README.md		README.md
requirements.txt		requirements.txt
run.py		run.py

alokumarjaiswal/pdf2data

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PDF2Data - Complete PDF Processing & ERP Integration System

🚀 Features

Core PDF Processing

ERP Integration

Advanced Features

📋 Table of Contents

⚡ Quick Start

1. Backend Setup

2. Frontend Setup

3. Unite Bot Setup

4. Access the Application

🏗️ Architecture Overview

System Components

Data Flow

🛠️ Installation & Setup

Prerequisites

Environment Configuration

Detailed Installation

📋 PDF Processing Workflow

1. Upload Phase

2. Extraction Phase

3. Parsing Phase

4. Preview & Edit Phase

5. Integration Phase

🤖 Unite ERP Integration

Automation Components

Workflow Overview

Real-Time Features

Integration Status

📡 API Documentation

Core Endpoints

Response Examples

🎨 Frontend Features

Pages Overview

Key UI Components

PDF Viewer Features

Tabbed Interface

Navigation & UX

Technical Features

Action Buttons (List Page)

Responsive Features

Intensity Mode (Theme Toggle)

📊 Parser Development

Creating a New Parser

Available Parsers

🤖 Unite Bot Configuration

Bot Components

1. login.py - Main Automation Engine

2. tasks.py - Voucher Processing Module

3. scrape_ledgers.py - Ledger Data Extraction

Configuration Structure

Usage Workflow

Basic Login & Voucher Entry

Ledger Data Extraction

Advanced Features

CAPTCHA Processing Pipeline

Ledger Management

Process Monitoring

Dependencies & Setup

Output Files

Integration with Main System

� Excel Export Feature

Overview

Supported Formats

Features

Usage

Dependencies

�💾 Database Schema

Collections

🔧 Development Guide

Project Structure

Development Workflow

Adding New Features

🚀 Deployment

Production Considerations