| --- |
| 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 |
|
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| This **Gradio Space** lets you upload a document (TXT, MD, HTML, or PDF) and predicts whether it was **AI-generated** or **Human-written**. |
|
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| 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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| --- |
|
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| ## ✨ Features |
|
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| ✅ **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 |
|
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| ✅ **Configurable Parameters** |
| - Adjust `MAX_LENGTH` and `STRIDE` for token chunking |
| - Choose aggregation method (`mean` or `max`) across chunks |
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|
| ✅ **Fully local** |
| - No Hub API calls beyond model loading |
| - Runs on CPU, GPU, or MPS automatically |
|
|
| --- |
|
|
| ## ⚙️ Environment Variables |
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| You can configure your Space in **Settings → Variables**: |
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| | 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 |
| ``` |
|
|
| --- |
|
|
| ## 🧠 Example Workflow |
|
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| 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 |
| ``` |
|
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| and a color‑coded **confidence badge**. |
|
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| --- |
|
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| ## 🚀 Run Locally |
|
|
| ```bash |
| pip install -r requirements.txt |
| python app.py |
| ``` |
|
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| Then open the Gradio URL shown in your terminal. |
|
|
| --- |
|
|
| ## 🖼️ UI Preview |
|
|
| >  |
| > |
| > *Top: prediction and probabilities; bottom: per‑chunk details.* |
|
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| --- |
|
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| ## 🧩 Notes |
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| - PDF parsing uses [`pypdf`](https://pypi.org/project/pypdf/); for better results or OCR, consider [`pymupdf`](https://pypi.org/project/PyMuPDF/) or [`unstructured`](https://github.com/Unstructured-IO/unstructured). |
| - The color scheme is based on the **Soft Indigo** theme for a calm, modern feel. |
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| --- |
|
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| ## 🪪 License |
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| MIT — feel free to modify and re‑deploy. |
