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--- |
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title: Cheese Texture (Tabular) — AutoGluon Gradio App |
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emoji: 🧀 |
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colorFrom: yellow |
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colorTo: blue |
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sdk: gradio |
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sdk_version: "4.44.0" |
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app_file: app.py |
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pinned: false |
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--- |
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# Cheese Texture (Tabular) — Gradio App |
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Predicts **texture** from nutritional/origin features using a **classmate's AutoGluon model**. |
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- **Model (classmate):** [rlogh/cheese-texture-autogluon-classifier](https://huggingface.co/rlogh/cheese-texture-autogluon-classifier) |
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- **Original dataset:** [aslan-ng/cheese-tabular](https://huggingface.co/datasets/aslan-ng/cheese-tabular) |
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## How to use |
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1. Set **fat**, **price**, **protein**, **origin**, and **holed**. |
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2. Choose inference parameters (base model, output mode, Top‑k). |
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3. View **Predicted texture**, **probability table**, and a **summary** of your input + results. |
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## Notes |
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- Sliders are constrained to dataset‑observed ranges. |
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- Inputs are validated and friendly warnings are shown when we auto‑correct values. |
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- This app uses AutoGluon's `TabularPredictor` for inference. |
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## Credits |
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- Model: rlogh (classmate) |
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- Dataset: aslan-ng |
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## Citations |
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- Model: rlogh/cheese-texture-autogluon-classifier (Hugging Face model card) |
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- Dataset: aslan-ng/cheese-tabular (Hugging Face dataset card, MIT license) |
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## License |
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- MIT |
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## Acknowledgments |
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- Thanks to rlogh for the trained AutoGluon model and to aslan-ng for the dataset. |
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- Collaboration & GenAI usage: This submission was prepared with peer feedback and limited use of generative AI (ChatGPT) for packaging/refactoring and documentation polish. |
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