Upload README.md with huggingface_hub

README.md

@@ -1,12 +1,23 @@
----
-title: Classical ML Interface
-emoji: 💻
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 5.47.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Sleep Habits → Phone-Before-Bed (Tabular) — Gradio GUI
+
+**What this is:** A minimal GUI that drives a classmate’s AutoGluon Tabular model to predict whether a student uses their phone before bed (binary classification).  
+**Why:** Practice building a clean, user-facing interface with proper widgets, validation, and documentation.
+
+**Model (classmate’s):** https://huggingface.co/jennifee/classical_automl_model  
+**Dataset (original docs):** https://huggingface.co/datasets/Iris314/Students_sleep_tabular
+
+## How the GUI meets the rubric
+- Uses a classmate’s **tabular** model (not my own); links to **model** and **dataset**.
+- Accepts **single-record** inputs via appropriate widgets with clear labels/ranges:
+  - Integers: `phone_hours`, `computer_hours`, `device_count`, `sleep_time`, `sleep_hours`
+  - Category: `sleep_quality ∈ {bad, medium, good}`
+- ≥ **3 example** inputs are included in the app.
+- Validation via constrained slider ranges & dropdown choices; friendly, simple UX.
+- Presents predictions **clearly** via ranked class probabilities:
+  - “Yes (uses phone before bed)”
+  - “No (does not use phone before bed)”
+- Deployed as a **public Hugging Face Space** with this README.
+
+**Notes on inference parameters:** This is a binary classifier that exposes ranked probabilities. (A threshold slider is optional; the ranked probabilities already communicate confidence cleanly.)
+
+**Acknowledgments:** Model by @jennifee; dataset by @Iris314. Built for a GUI homework assignment.