Text2Doc: Universal Document Conversion Ecosystem

🌟 Motto

"Transform Data, Unleash Potential"

🚀 Mission Statement

To provide a seamless, powerful, and flexible document conversion platform that empowers businesses and developers to transform data across multiple formats with unprecedented ease and efficiency.

🎯 Vision

We envision a world where data flows freely between formats, breaking down barriers of communication and enabling intelligent, automated document processing.

🌈 Project Overview

Strategic Objectives

1. Flexibility: Create a modular document conversion ecosystem
2. Efficiency: Minimize manual data transformation efforts
3. Accessibility: Make complex document conversions simple
4. Extensibility: Support continuous innovation in document processing

Text2Doc examples: Real-World Use Cases and Solutions

These examples demonstrate the versatility of Text2Doc in solving real-world data transformation challenges across various industries. The library provides a flexible, powerful solution for:

• Automating complex reporting processes
• Ensuring data consistency and accuracy
• Simplifying data extraction and transformation
• Supporting multiple output formats
• Maintaining data privacy and compliance

1. Sales Reporting Automation

Problem

Manual creation of sales reports is time-consuming and error-prone, requiring data extraction, formatting, and distribution.

Solution

Automated pipeline that extracts sales data, transforms it, and generates professional reports.

from text2doc import DocumentPipeline

def generate_sales_report():
    pipeline = DocumentPipeline("monthly_sales_report")
    pipeline.add_stage('sql', {
        'connection_string': 'postgresql://sales_database',
        'query': '''
            SELECT 
                product_category, 
                SUM(quantity) as total_quantity, 
                SUM(total_price) as revenue,
                AVG(unit_price) as avg_price
            FROM sales
            WHERE sale_date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '1 month')
            GROUP BY product_category
        '''
    })
    pipeline.add_stage('json', {
        'transformations': [
            {'sort_by': 'revenue'},
            {'top_n': 10}
        ]
    })
    pipeline.add_stage('html', {
        'template': 'sales_report_template.html'
    })
    pipeline.add_stage('pdf')
    pipeline.add_stage('print', {
        'printer': 'management_reports_printer'
    })
    
    pipeline.execute()

2. Customer Support Ticket Analysis

Problem

Difficulty in tracking and analyzing customer support interactions across multiple channels.

Solution

Consolidate support ticket data from various sources and generate comprehensive analysis reports.

from text2doc import DocumentPipeline

def support_ticket_analysis():
    pipeline = DocumentPipeline("support_ticket_insights")
    pipeline.add_stage('sql', {
        'connection_string': 'postgresql://support_db',
        'query': '''
            SELECT 
                category,
                COUNT(*) as ticket_count,
                AVG(resolution_time) as avg_resolution_time,
                COUNT(CASE WHEN status = 'resolved' THEN 1 END) as resolved_tickets
            FROM support_tickets
            WHERE created_at >= DATE_TRUNC('quarter', CURRENT_DATE)
            GROUP BY category
        '''
    })
    pipeline.add_stage('json', {
        'transformations': [
            {'calculate_percentages': {
                'resolved_percentage': 'resolved_tickets / ticket_count * 100'
            }}
        ]
    })
    pipeline.add_stage('html', {
        'template': 'support_analysis_template.html'
    })
    pipeline.add_stage('pdf')
    
    report = pipeline.execute()

3. Inventory Management Reporting

Problem

Complex inventory tracking across multiple warehouses and product lines.

Solution

Create dynamic inventory reports with real-time data aggregation and visualization.

from text2doc import DocumentPipeline

def inventory_management_report():
    pipeline = DocumentPipeline("inventory_status_report")
    pipeline.add_stage('sql', {
        'connection_string': 'mysql://inventory_system',
        'query': '''
            SELECT 
                warehouse_location,
                product_category,
                SUM(stock_quantity) as total_stock,
                SUM(CASE WHEN stock_quantity < reorder_point THEN 1 ELSE 0 END) as low_stock_items,
                AVG(stock_value) as avg_stock_value
            FROM inventory
            GROUP BY warehouse_location, product_category
        '''
    })
    pipeline.add_stage('json', {
        'transformations': [
            {'flag_low_stock': 'total_stock < 100'},
            {'calculate_total_value': 'total_stock * avg_stock_value'}
        ]
    })
    pipeline.add_stage('html', {
        'template': 'inventory_report_template.html',
        'chart_type': 'pie'
    })
    pipeline.add_stage('pdf')
    pipeline.add_stage('zpl', {
        'label_type': 'inventory_warning'
    })
    
    pipeline.execute()

4. Financial Compliance Reporting

Problem

Generating standardized financial reports that meet regulatory requirements.

Solution

Automated pipeline to extract, transform, and format financial data for compliance reporting.

from text2doc import DocumentPipeline

def financial_compliance_report():
    pipeline = DocumentPipeline("quarterly_financial_report")
    pipeline.add_stage('sql', {
        'connection_string': 'postgresql://financial_db',
        'query': '''
            SELECT 
                account_type,
                SUM(total_revenue) as revenue,
                SUM(total_expenses) as expenses,
                SUM(net_profit) as net_profit,
                AVG(profit_margin) as avg_profit_margin
            FROM financial_statements
            WHERE quarter = CURRENT_QUARTER
            GROUP BY account_type
        '''
    })
    pipeline.add_stage('json', {
        'transformations': [
            {'validate_compliance_rules': True},
            {'calculate_ratios': [
                'debt_to_equity_ratio',
                'current_ratio'
            ]}
        ]
    })
    pipeline.add_stage('html', {
        'template': 'financial_compliance_template.html',
        'watermark': 'CONFIDENTIAL'
    })
    pipeline.add_stage('pdf', {
        'encryption': True
    })
    
    report = pipeline.execute()

5. Supply Chain Logistics Tracking

Problem

Complex tracking of shipments, inventory movement, and logistics performance.

Solution

Create comprehensive logistics reports with detailed tracking and performance metrics.

from text2doc import DocumentPipeline

def logistics_performance_report():
    pipeline = DocumentPipeline("logistics_tracking_report")
    pipeline.add_stage('sql', {
        'connection_string': 'postgresql://logistics_db',
        'query': '''
            SELECT 
                shipping_partner,
                COUNT(*) as total_shipments,
                AVG(delivery_time) as avg_delivery_time,
                SUM(CASE WHEN status = 'delayed' THEN 1 ELSE 0 END) as delayed_shipments
            FROM shipment_tracking
            WHERE shipment_date >= DATE_SUB(CURRENT_DATE, INTERVAL 1 MONTH)
            GROUP BY shipping_partner
        '''
    })
    pipeline.add_stage('json', {
        'transformations': [
            {'calculate_performance_score': True},
            {'rank_shipping_partners': 'avg_delivery_time'}
        ]
    })
    pipeline.add_stage('html', {
        'template': 'logistics_performance_template.html',
        'include_charts': True
    })
    pipeline.add_stage('pdf')
    pipeline.add_stage('zpl', {
        'label_type': 'shipping_performance'
    })
    
    pipeline.execute()

6. Healthcare Patient Data Anonymization

Problem

Generating anonymized patient reports while maintaining data privacy and compliance.

Solution

Create a pipeline that extracts, anonymizes, and reports patient data securely.

from text2doc import DocumentPipeline

def anonymized_patient_report():
    pipeline = DocumentPipeline("patient_data_report")
    pipeline.add_stage('sql', {
        'connection_string': 'postgresql://medical_records',
        'query': '''
            SELECT 
                department,
                COUNT(*) as patient_count,
                AVG(treatment_duration) as avg_treatment_time,
                SUM(treatment_cost) as total_treatment_cost
            FROM patient_records
            WHERE treatment_date >= DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH)
            GROUP BY department
        '''
    })
    pipeline.add_stage('json', {
        'transformations': [
            {'anonymize_data': True},
            {'remove_personal_identifiers': ['patient_id']}
        ]
    })
    pipeline.add_stage('html', {
        'template': 'patient_report_template.html',
        'compliance_mode': 'HIPAA'
    })
    pipeline.add_stage('pdf', {
        'encryption': True,
        'access_controls': True
    })
    
    pipeline.execute()

🏗️ Comprehensive Project Structure

Project Hierarchy

text2doc/
│
├── text2doc/                   # Core Library
│   ├── __init__.py             # Package initialization
│   │
│   ├── core/                   # Conversion Components
│   │   ├── base_converter.py   # Base conversion logic
│   │   ├── sql_converter.py    # SQL to data converter
│   │   ├── json_converter.py   # JSON transformations
│   │   ├── html_converter.py   # HTML rendering
│   │   ├── pdf_converter.py    # PDF generation
│   │   ├── zpl_converter.py    # ZPL label printing
│   │   └── print_converter.py  # Printing utilities
│   │
│   ├── pipeline/               # Pipeline Management
│   │   ├── base_pipeline.py    # Core pipeline logic
│   │   └── document_pipeline.py# Document conversion pipeline
│   │
│   ├── utils/                  # Utility Modules
│   │   ├── config_manager.py   # Configuration handling
│   │   ├── logger.py           # Logging utilities
│   │   ├── exceptions.py       # Custom exceptions
│   │   └── scheduler.py        # Pipeline scheduling
│   │
│   ├── gui/                    # Graphical Interfaces
│   │   ├── main_window.py      # Main application window
│   │   ├── converter_panel.py  # Conversion interface
│   │   └── pipeline_builder.py # Pipeline creation UI
│   │
│   └── cli/                    # Command Line Interface
│       └── main.py             # CLI entry point
│
├── frontend/                   # React Configuration UI
│   ├── src/
│   │   ├── App.js
│   │   └── PipelineConfigApp.js
│   ├── Dockerfile
│   └── package.json
│
├── backend/                    # Flask Backend
│   ├── app.py
│   ├── Dockerfile
│   └── requirements.txt
│
├── examples/                   # Usage Examples
│   ├── simple_conversion.py
│   ├── pipeline_example.py
│   └── advanced_pipeline.py
│
├── tests/                      # Testing Suite
│   ├── test_converters.py
│   ├── test_pipeline.py
│   └── test_config.py
│
├── docs/                       # Documentation
│   ├── index.md
│   ├── installation.md
│   └── usage.md
│
├── setup.py
├── pyproject.toml
└── docker-compose.yml

🔧 Key Components

1. Converters

• SQL to various formats
• JSON transformation
• HTML rendering
• PDF generation
• ZPL label printing

2. Pipeline Management

• Modular stage-based conversions
• Flexible configuration
• Error handling
• Logging and monitoring

3. Scheduling System

• Cron-based scheduling
• Retry mechanisms
• Notification support
• Multi-process execution

4. User Interfaces

• Web-based configuration
• CLI support
• Graphical pipeline builder

💡 Core Technologies

• Python
• Flask
• React
• SQLAlchemy
• Jinja2
• Pandas
• WeasyPrint

🛡️ Guiding Principles

1. Modularity: Each component should be independent and replaceable
2. Configurability: Maximum flexibility for diverse use cases
3. Performance: Efficient data processing
4. Reliability: Robust error handling and logging

📦 Installation

Prerequisites

• Python 3.8+
• pip
• Docker (optional)

Quick Install

pip install text2doc

Docker Deployment

docker-compose up

🚀 Quick Start

Basic Conversion

from text2doc import DocumentPipeline

pipeline = DocumentPipeline("sales_report")
pipeline.add_stage('sql')
pipeline.add_stage('json')
pipeline.add_stage('html')
pipeline.add_stage('pdf')

report = pipeline.execute()

🤝 Contributing

1. Fork the repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Create Pull Request

📄 License

Apache License 2.0

📞 Contact

• GitHub: https://github.com/text2doc/python
• Email: support@text2doc.com

🌍 Community

• Slack Channel
• Discussion Forums
• Regular Meetups

🔮 Future Roadmap

• Machine Learning Integration
• More Converter Types
• Enhanced Scheduling
• Cloud Service Support

---

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DOCS:

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---

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