document-analyser

Extracts text from documents and returns readability metrics, word counts, and structural information. Accepts PDF, DOCX, PPTX, and plain text formats.

Part of the analyser family.

Install

pip install document-analyser

Requires Python 3.11+.

Usage

Python

from app.analyser import DocumentAnalyser

result = DocumentAnalyser().analyse("report.pdf")

print(f"Words:       {result['word_count']}")
print(f"Sentences:   {result['sentence_count']}")
print(f"Readability: {result['readability']['flesch_reading_ease']:.1f} (Flesch)")
print(result["text"][:500])

CLI

# Human-readable summary
document-analyser report.pdf

# Machine-readable JSON
document-analyser thesis.docx --json

# Start the HTTP server
document-analyser serve --port 8000

HTTP API

curl -X POST http://localhost:8000/analyse \
  -F "file=@report.pdf"

Supported formats

Format	Extensions
PDF	.pdf
Word	.docx
PowerPoint	.pptx
Plain text	.txt .md

Output

{
  "format": "pdf",
  "file_path": "/path/to/report.pdf",
  "file_size": 204800,
  "page_count": 12,
  "word_count": 4823,
  "sentence_count": 312,
  "paragraph_count": 89,
  "text": "Executive summary...",
  "readability": {
    "flesch_reading_ease": 52.3,
    "flesch_kincaid_grade": 11.2,
    "gunning_fog": 13.8,
    "smog_index": 12.1,
    "automated_readability_index": 11.9
  }
}

The analyser family

Low-level analysis tools. Each accepts files directly and returns structured JSON. Build your own UI or pipeline on top.

Package	Handles
speech-analyser	audio and video files — transcript and speech metrics
video-analyser	video files — frames, scenes, and visual quality
document-analyser	PDF, DOCX, PPTX, TXT — text and readability
code-analyser	source code — style, complexity, and quality metrics
records-analyser	CSV, Excel, SQLite, Parquet, JSON — data profiling
auto-analyser	any file — detects format and routes to the right tool

Licence

MIT