README.md

metadata

title: AI vs Human Document Classifier
emoji: 📄
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.49.0
app_file: app.py
pinned: false
license: mit

🔎 AI vs Human — Document Classifier

This Gradio Space lets you upload a document (TXT, MD, HTML, or PDF) and predicts whether it was AI-generated or Human-written.

The app supports long documents by splitting them into overlapping 512‑token chunks and aggregating predictions to provide an overall document‑level probability.

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✨ Features

✅ Interactive Interface

• Upload documents directly (TXT, MD, HTML, PDF)
• Displays clean probability bars for AI‑generated vs Human‑written
• Shows a confidence badge (“Likely AI” / “Likely Human”) with traffic‑light colors
• Separate Basic and Advanced tabs for simplicity
• A Chunk Details accordion with per‑chunk probabilities for deeper inspection

✅ Configurable Parameters

• Adjust MAX_LENGTH and STRIDE for token chunking
• Choose aggregation method (mean or max) across chunks

✅ Fully local

• No Hub API calls beyond model loading
• Runs on CPU, GPU, or MPS automatically

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⚙️ Environment Variables

You can configure your Space in Settings → Variables:

Variable	Description	Default
MODEL_ID	Hugging Face repo ID of your model	bert-base-uncased
MAX_LENGTH	Tokens per chunk	512
STRIDE	Overlap tokens between chunks	128

Example:

MODEL_ID=your-username/bert-binclass
MAX_LENGTH=512
STRIDE=128

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🧠 Example Workflow

1. Train your binary classifier using train.py and push to Hub.
2. Deploy this Space with your model:
   • Set the Space variable MODEL_ID to your repo.
3. Upload any text file — the app will:
   • Chunk the text
   • Run inference on each chunk
   • Show probabilities like:

AI generated: 0.82
Human written: 0.18

and a color‑coded confidence badge.

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🚀 Run Locally

pip install -r requirements.txt
python app.py

Then open the Gradio URL shown in your terminal.

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🖼️ UI Preview

Top: prediction and probabilities; bottom: per‑chunk details.

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🧩 Notes

• PDF parsing uses pypdf; for better results or OCR, consider pymupdf or unstructured.
• The color scheme is based on the Soft Indigo theme for a calm, modern feel.

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🪪 License

MIT — feel free to modify and re‑deploy.