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README.md

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----
-title: Classical AutoML
-emoji: 🔥
-colorFrom: green
-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
+# LEGO Brick Classification (Classical AutoML)
+
+**Model repo:** [Iris314/classical-automl-model](https://huggingface.co/Iris314/classical-automl-model)  
+**Dataset:** [aedupuga/lego-sizes](https://huggingface.co/datasets/aedupuga/lego-sizes)
+
+## About
+This model classifies LEGO pieces into three types: **Standard, Flat, Sloped**  
+Inputs: `Length`, `Height`, `Width`, `Studs`.
+
+- **Model:** AutoML (best model = LightGBM, via AutoGluon)
+- **Task:** Tabular classification
+- **Limitations:** Synthetic dataset (augmented from 30 → 300 samples); educational use only.
+
+## Demo
+Gradio interface allows:
+- Single-record input via sliders
+- Example inputs (3 presets)
+- Prediction (most likely brick type)
+- Class probabilities
+
+## Acknowledgments
+- Model by Xinxuan Tang (CMU), dataset curated by Anuhya Edupuganti (CMU).  
+- This Gradio app was implemented for CMU course HW3.  
+- GenAI disclosure: ChatGPT assisted in generating scaffolding code and documentation. All outputs were reviewed and edited.

app (1).py

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+import gradio as gr
+import pandas as pd
+from autogluon.tabular import TabularPredictor
+from huggingface_hub import hf_hub_download
+import os, zipfile
+
+REPO_ID = "Iris314/classical-automl-model"
+ZIP_FILE = "lego_predictor_dir.zip"
+
+local_zip = hf_hub_download(repo_id=REPO_ID, filename=ZIP_FILE)
+
+extract_dir = "lego_predictor_dir"
+os.makedirs(extract_dir, exist_ok=True)
+
+with zipfile.ZipFile(local_zip, 'r') as zip_ref:
+    zip_ref.extractall(extract_dir)
+
+predictor = TabularPredictor.load(extract_dir)
+
+def predict_brick(length, height, width, studs):
+    record = pd.DataFrame([{
+        "Length": length,
+        "Height": height,
+        "Width": width,
+        "Studs": studs
+    }])
+    pred = predictor.predict(record)[0]
+    proba = predictor.predict_proba(record).iloc[0].to_dict()
+    return f"Prediction: {pred}", proba
+
+with gr.Blocks(title="LEGO Brick Classifier") as demo:
+    gr.Markdown("## LEGO Brick Classification\nPredict Standard / Flat / Sloped")
+    with gr.Row():
+        with gr.Column():
+            length = gr.Slider(1, 10, step=0.5, value=4, label="Length")
+            height = gr.Slider(0.5, 5, step=0.1, value=1.2, label="Height")
+            width = gr.Slider(1, 10, step=0.5, value=2, label="Width")
+            studs = gr.Slider(0, 12, step=1, value=4, label="Studs")
+            btn = gr.Button("Predict")
+        with gr.Column():
+            out_label = gr.Textbox(label="Prediction")
+            out_probs = gr.Label(label="Class Probabilities")
+
+    btn.click(predict_brick, [length, height, width, studs], [out_label, out_probs])
+
+    gr.Examples(
+        examples=[
+            [4, 1.2, 2, 4],
+            [6, 0.5, 2, 6],
+            [3, 2.0, 2, 2]
+        ],
+        inputs=[length, height, width, studs]
+    )
+
+demo.launch()

requirements (1).txt

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+gradio==4.44.1
+autogluon==1.1.1
+pandas
+numpy
+huggingface_hub