eng-input-cleaner 0.1.0

Text cleaning, scoring and compression pipeline

Eng Input Cleaner

Lightweight text preprocessing pipeline for cleaning, scoring and compressing user input.

Designed for LLM optimization, noise reduction and token efficiency.

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Features

• Text normalization and cleanup
• Noise detection using heuristic scoring
• Input compression (removes low-value words)
• Quality scoring system (0–100)
• Smart truncation for long inputs
• Token reduction tracking using tiktoken

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Installation

pip install eng_input_cleaner

Or local development:

pip install -e .

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Quick Start

from input_cleaner import process

result = process("hello hello I really really need help with this")

print(result)

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Example Output

{
  "original": "hello hello I really really need help with this",
  "cleaned": "i really need help with this",
  "output": "need help",
  "score": 78,
  "status": "accepted",
  "was_compressed": true,
  "tokens_before": 10,
  "tokens_after": 2,
  "token_reduction": 0.8,
  "token_reduction_pct": "80.00%"
}

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Pipeline Overview

The system processes text in 4 sequential stages:

raw input
    ↓
1. Cleaner     ← normalizes and removes noise
    ↓
2. Scorer      ← evaluates quality on cleaned text (0–100)
    ↓
3. Decision    ← accepts or blocks based on score
    ↓
4. Compressor  ← always runs on accepted text
    ↓
  LLM

1. Cleaning

• Normalize text (lowercase, symbols, encoding)
• Remove duplicate words and repeated characters
• Filter noise patterns using heuristic scoring
• Collapse redundant symbols

2. Scoring

Evaluates the cleaned text using:

• Character entropy
• Character diversity
• Vowel ratio
• Common bigram ratio
• Consonant run length
• Cyclic and structural pattern detection

3. Decision

• Block low-quality or noisy input
• Accept valid input (short or long)
• Apply smart truncation for very long inputs

4. Compression

Runs on every accepted text regardless of score:

• Removes low-value words (articles, weak intensifiers, filler)
• Preserves intent words, negations, question words and structured data

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Status Values

Status	Meaning
accepted	Valid input, normal or long length
accepted_short	Valid input, short (1–5 words)
blocked_empty	Empty or whitespace-only input
blocked_low_score	Quality score below threshold
blocked_noise	Single long token detected as noise
blocked_low_info	Too short to carry meaningful information

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Output Fields

Field	Type	Description
original	str	Raw input as received
cleaned	str	After normalization and noise removal
output	str | None	Final text sent to LLM, None if blocked
score	float	Quality score from 0 to 100
status	str	Pipeline decision result
was_compressed	bool	Whether compression was applied
tokens_before	int	Token count of original input
tokens_after	int	Token count of final output
token_reduction	float	Fractional reduction (e.g. 0.8)
token_reduction_pct	str	Human-readable reduction (e.g. "80.00%")

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Use Cases

• Preprocessing user input for LLMs
• Reducing token costs
• Filtering spam or noisy text
• Improving prompt quality
• Input validation layer for APIs

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Requirements

• Python 3.8+
• tiktoken

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Project Structure

input_cleaner/
├── cleaner.py      ← normalization and noise filtering
├── scorer.py       ← quality scoring (0–100)
├── decision.py     ← accept / block logic
├── compressor.py   ← low-value word removal
└── pipeline.py     ← orchestrates all stages

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Design Goals

• Fast — pure Python, no heavy dependencies
• Deterministic — no randomness, same input always produces same output
• Modular — each stage is isolated and independently testable
• Easy to extend — thresholds and word lists are configurable

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License

Apache-2.0

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Author

marlon (MARLM8)