documentprocessinghub-ljd 0.5.0

File type identification and validation for document processing workflows

Document Processing Hub

A Python library for searching, copying, and moving document files intelligently. Automatically find the latest file of a specific type and perform operations on it.

🌟 Features

• Smart File Search: Find the latest file of a specific type in predefined locations or custom folders
• Intelligent Sorting: Files are ordered by relevance (date, version, etc.) per type
• Copy & Move Operations: Perform file operations with simple method chaining
• Two Search Modes:
  • search_file.exists - Search in predefined system locations
  • search_file.local - Search in user-specified folders
• Multiple File Types: Support for ZJ, Manpower Budget, Manpower Documents, Manpower, Real-time Production
• Zero Dependencies: Pure Python implementation with no external dependencies

📦 Installation

From PyPI

pip install documentprocessinghub-ljd

From GitHub

git clone https://github.com/LJD-UwU/Document-Processing-Hub.git
cd Document-Processing-Hub
pip install .

🚀 Quick Start

from documentprocessinghub import search_file

# Find latest file in predefined locations
ruta = search_file.exists.manpower_budget()
print(f"Found: {ruta}")

# Find and copy to another location
resultado = search_file.exists.manpower_budget(r"C:\Backup").copy()
print(f"Copied to: {resultado}")

# Find in local folder and move to another
resultado = search_file.local.zj(r"C:\Documentos", r"C:\Procesados").move()
print(f"Moved to: {resultado}")

🔍 API Reference: search_file

Overview

The search_file API provides two modes for finding and manipulating files:

1. search_file.exists - Search in predefined system locations (fast & automatic)
2. search_file.local - Search in folders you specify (flexible & explicit)

Return types:

• Without destination: Returns str (file path)
• With destination: Returns FileResult object (for .copy() or .move())

Mode 1: search_file.exists - Predefined Locations

Only manpower_budget is available in exists mode.

Example 1: Get File Path Only

from documentprocessinghub import search_file

# Returns the path of the latest Manpower Budget file found in predefined locations
ruta = search_file.exists.manpower_budget()

if ruta:
    print(f"Latest file: {ruta}")
    # Output: C:\Sistema\Archivos\Manpower Budget Rev 18.2.xlsx
else:
    print("No file found")

# Use it in your code
procesar_archivo(ruta)

Example 2: Find and Copy

# Find latest file and copy it (original remains in place)
backup_path = search_file.exists.manpower_budget(r"C:\Backups").copy()

print(f"Backed up to: {backup_path}")
# Output: C:\Backups\Manpower Budget Rev 18.2.xlsx

# Practical use: Daily backup
from datetime import date
today = date.today().strftime("%Y%m%d")
backup_folder = f"C:\\Backups\\{today}"
search_file.exists.manpower_budget(backup_folder).copy()

Example 3: Find and Move

# Find latest file and move it to another location
procesado_path = search_file.exists.manpower_budget(r"C:\Procesados").move()

print(f"Moved to: {procesado_path}")
# Output: C:\Procesados\Manpower Budget Rev 18.2.xlsx

# Note: The file is removed from original location

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Mode 2: search_file.local - Custom Folders

All file types are available in local mode:

• manpower_budget
• manpower_documents
• zj
• manpower
• real_time_production

Example 1: Get File Path from Local Folder

# Search in a specific folder and get the latest file
ruta = search_file.local.zj(r"C:\Documentos")

if ruta:
    print(f"Latest ZJ file: {ruta}")
    # Output: C:\Documentos\ZJ26042912-8105.xlsx
else:
    print("No ZJ files found in folder")

# Multiple searches
zj_file = search_file.local.zj(r"C:\Docs")
budget_file = search_file.local.manpower_budget(r"C:\Docs")
docs_file = search_file.local.manpower_documents(r"C:\Docs")

print(f"ZJ: {zj_file}")
print(f"Budget: {budget_file}")
print(f"Documents: {docs_file}")

Example 2: Search Local and Copy

# Find in one folder and copy to another
copia_path = search_file.local.manpower_budget(
    r"C:\Documentos\Entrada",
    r"C:\Documentos\Copia"
).copy()

print(f"Copied to: {copia_path}")
# Output: C:\Documentos\Copia\Manpower Budget Rev 18.1.xlsx

# Practical use: Process and backup
search_file.local.zj(
    r"C:\Entrada",
    r"C:\Backup"
).copy()  # Backup before processing

Example 3: Search Local and Move

# Find in source folder and move to destination
procesado_path = search_file.local.manpower_documents(
    r"C:\Documentos\Entrada",
    r"C:\Documentos\Procesados"
).move()

print(f"Moved to: {procesado_path}")
# Output: C:\Documentos\Procesados\Manpower Documents Q1 2026.xlsx

# Practical use: Processing pipeline
for carpeta_entrada in [r"C:\Q1", r"C:\Q2", r"C:\Q3"]:
    resultado = search_file.local.manpower_documents(
        carpeta_entrada,
        r"C:\Procesados"
    ).move()
    if resultado:
        print(f"Procesado: {resultado}")

Example 4: Process Multiple File Types

# Search for different types in the same folder
origen = r"C:\Documentos"
destino = r"C:\Procesados"

# Process each type
zj = search_file.local.zj(origen, destino).move()
budget = search_file.local.manpower_budget(origen, destino).move()
docs = search_file.local.manpower_documents(origen, destino).move()

# Log results
if zj:
    print(f"ZJ: {zj}")
if budget:
    print(f"Budget: {budget}")
if docs:
    print(f"Documents: {docs}")

---

File Selection Criteria

The "latest" file is selected based on the file type:

ZJ Files: By date, version, and duplicate count

ZJ26042912-8105(4) > ZJ26042912-8105(1) > ZJ26042822-8005
     ↑ newer        ↑ same date         ↑ older date
                    ↑ higher duplicate

MANPOWER_BUDGET: By version and month

Rev 18.2 > Rev 18.1 > Rev 17.0 April
 ↑ newer   ↑ same major version

MANPOWER_DOCUMENTS: By year, month, and quarter

2026 Q2 > 2026 Q1 > 2025 Q4
↑ newer   ↑ newer in same year

MANPOWER: By month and day

April_29 > April_28 > March_28
 ↑ newer    ↑ newer in month

REAL_TIME_PRODUCTION: By modification date (most recent first)

---

💡 Common Patterns

Pattern 1: Daily Backup

from documentprocessinghub import search_file
from datetime import date

def daily_backup():
    today = date.today().strftime("%Y%m%d")
    backup_folder = f"C:\\Backups\\{today}"
    
    ruta = search_file.exists.manpower_budget(backup_folder).copy()
    if ruta:
        print(f"✓ Backup successful: {ruta}")
    else:
        print("✗ No file found to backup")

# Run daily
daily_backup()

Pattern 2: Processing Pipeline

from documentprocessinghub import search_file

def process_documents():
    entrada = r"C:\Entrada"
    procesados = r"C:\Procesados"
    
    # Process each type
    for tipo in ["zj", "manpower_budget", "manpower_documents"]:
        # Get the function dynamically
        search_func = getattr(search_file.local, tipo)
        
        resultado = search_func(entrada, procesados).move()
        if resultado:
            print(f"Procesado ({tipo}): {resultado}")

process_documents()

Pattern 3: Safe Backup Before Processing

from documentprocessinghub import search_file

def safe_process(carpeta_entrada):
    # Step 1: Backup the file
    backup = search_file.local.manpower_budget(
        carpeta_entrada,
        r"C:\Backup"
    ).copy()
    
    if not backup:
        print("✗ Error: No file found")
        return
    
    # Step 2: Process the original
    procesado = search_file.local.manpower_budget(
        carpeta_entrada,
        r"C:\Procesados"
    ).move()
    
    print(f"✓ Backed up: {backup}")
    print(f"✓ Processed: {procesado}")

safe_process(r"C:\Entrada")

---

🏗️ Project Structure

document-processing-hub/
├── documentprocessinghub/          # Main package
│   ├── __init__.py                # Package initialization
│   ├── fileSelector.py            # search_file API implementation
│   ├── scanNameFiles.py           # File type identification
│   ├── validators.py              # Format validation
│   └── paths_config.py            # Predefined paths configuration
├── examples/                       # Usage examples
│   ├── main.py                    # Interactive examples
│   └── USAGE.md                   # Detailed usage guide
├── pyproject.toml                 # Project configuration
├── README.md                       # This file
├── LICENSE                         # MIT License
└── .gitignore                      # Git ignore rules

---

📝 Changelog

Version 0.4.0 (2026-04-30)

Major Changes

• Renamed API: find_latest_file → search_file (clearer intent)
• Simplified behavior:
  • Without destination: Returns str (file path)
  • With destination: Returns FileResult for .copy() or .move()
• Restricted exists mode: Only manpower_budget available in search_file.exists
• Enhanced documentation: Complete docstrings for IDE support
• All types in local: All file types available in search_file.local

Version 0.3.1 (2026-04-30)

Fixes

• Fixed FileResult.copy() missing destination argument
• Improved API parameter handling

Version 0.3.0 (2026-04-29)

New Features

• Fluent API with dynamic methods for each file type
• FileResult class for file operations

Version 0.2.0 (2026-04-29)

New Features

• Initial file search functionality
• Support for multiple file types
• Smart file selection based on date and version

---

📄 License

MIT License - See LICENSE file for details

👨‍💻 Author

LJD-UwU

• Email: [e-mail]
• GitHub: @LJD-UwU

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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🔧 Data Processing: process_file

The process_file module provides tools for cleaning and processing Excel data.

clean_sheet() - Clean and Flatten Excel Data

Automatically detects headers, cleans data, and creates a structured "Datos_Limpios" sheet.

Features:

• ✅ Automatic header detection
• ✅ Data normalization and cleaning
• ✅ Professional formatting (colors, borders, frozen headers)
• ✅ Intelligent column width adjustment
• ✅ Removes empty rows and duplicate columns
• ✅ Returns pandas DataFrame for further analysis

Usage:

from documentprocessinghub import clean_sheet

# Option 1: Clean and overwrite
df = clean_sheet("datos.xlsx")

# Option 2: Save to new file
df = clean_sheet("entrada.xlsx", output_path="salida.xlsx")

# Option 3: Process specific sheet
df = clean_sheet("datos.xlsx", nombre_hoja="Producción")

# Result is a pandas DataFrame
print(df.shape)      # (rows, columns)
print(df.columns)    # Column names
print(df.head())     # First rows

What It Does:

1. Reads the Excel file
2. Detects headers and data rows
3. Cleans data (removes nulls, normalizes columns)
4. Applies professional formatting
5. Creates "Datos_Limpios" sheet with cleaned data
6. Returns DataFrame for analysis

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📚 References

• PyPI Package
• GitHub Repository
• Usage Guide

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Made with care for document processing automation