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Created page with "= GPT Humanizer Overview = This project exists to explore human-centered control over LLM output without turning the system into a policy engine or a brittle ruleset. The goal is not to censor language, but to: * Shape tone and wording deliberately * Preserve meaning while avoiding specific terms * Create space for “humanizing” transformations downstream '''Design priorities''' * Simplicity over abstraction * Observability over automation * Clear separation betwee..."
 
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= GPT Humanizer Overview =
= GPT Humanizer Overview =


This project exists to explore human-centered control over LLM output without turning the system into a policy engine or a brittle ruleset.
This is a software program designed to humanize GPT text. In early versions, the name is Matt's GPT Humanizer. Version 1.0 was developed in December 2025. It currently only restricts the generation of words that regularly appear in GPT output. The addition of features that further humanize generated text is already being explored.
 
The goal is not to censor language, but to:
* Shape tone and wording deliberately
* Preserve meaning while avoiding specific terms
* Create space for “humanizing” transformations downstream
 
'''Design priorities'''
* Simplicity over abstraction
* Observability over automation
* Clear separation between generation, evaluation, and rewriting
* Minimal UI that reveals behavior rather than hides it
 
This is a foundation, not a product.
 
Future work may include additional rewrite strategies, tone shaping, and documentation-first development, but the system should remain understandable end-to-end.
 
 
 
 
A lightweight FastAPI service that generates text via an LLM, applies banned-word detection, and optionally rewrites output to preserve meaning while avoiding specified terms. A minimal headless web UI is included for interactive use and inspection of requests and responses.


== Features ==
== Features ==
* Plain-text generation endpoint
* Minimal headless UI for dashboard-like controls
* Optional banned-word filtering with rewrite pass
* Plain-text output with the endpoint within the UI
* Temperature and token controls
* Standard list of restricted words
* Headless browser UI for prompt entry and testing
* Simple addition of more restricted words
* No client-side model logic
* Controls for model selection, temperature, and token usage
* Model logic is entirely on the server


== Requirements ==
== Requirements ==
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* An OpenAI API key
* An OpenAI API key


== Setup ==
== Installation ==
1. Create and activate a virtual environment
1. Create and activate a virtual environment on your OS
2. Install dependencies from `requirements.txt
2. Install dependencies from ''''requirements.txt''''
3. Export `OPENAI_API_KEY` in the shell
3. Export ''''OPENAI_API_KEY'''' in the OS shell
4. Run the server with Uvicorn
4. Run the server with Uvicorn


== Usage ==
== Usage ==
* API endpoint: `POST /generate`
* Access the UI: open http://127.0.0.1:8080 in a browser window
* UI: open `http://127.0.0.1:8080/` in a browser
* Write a prompt in the first text area
* Health check: `GET /healthz`
* Insert additional restricted words in the next text area, one line at a time
 
* Choose a model, or accept the default model, gpt-4o-mini
The `/generate` endpoint returns plain text only.
* Set the temperature between 1 (most creative) and 0 (most deterministic)
* Click the ''''Send'''' button
* The text will generate in the last text area on the page

Revision as of 01:09, 26 December 2025

GPT Humanizer Overview

This is a software program designed to humanize GPT text. In early versions, the name is Matt's GPT Humanizer. Version 1.0 was developed in December 2025. It currently only restricts the generation of words that regularly appear in GPT output. The addition of features that further humanize generated text is already being explored.

Features

  • Minimal headless UI for dashboard-like controls
  • Plain-text output with the endpoint within the UI
  • Standard list of restricted words
  • Simple addition of more restricted words
  • Controls for model selection, temperature, and token usage
  • Model logic is entirely on the server

Requirements

  • Python 3.11+
  • An OpenAI API key

Installation

1. Create and activate a virtual environment on your OS 2. Install dependencies from 'requirements.txt' 3. Export 'OPENAI_API_KEY' in the OS shell 4. Run the server with Uvicorn

Usage

  • Access the UI: open http://127.0.0.1:8080 in a browser window
  • Write a prompt in the first text area
  • Insert additional restricted words in the next text area, one line at a time
  • Choose a model, or accept the default model, gpt-4o-mini
  • Set the temperature between 1 (most creative) and 0 (most deterministic)
  • Click the 'Send' button
  • The text will generate in the last text area on the page