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README.md

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----
-title: Tabularappspace
-emoji: 🐢
-colorFrom: purple
-colorTo: purple
-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
+# Predict Book Genre from Physical Features — AutoGluon + Gradio
+
+This Space wraps a class-trained AutoGluon `TabularPredictor` with a Gradio UI.
+
+## Dataset
+- Zion's Book tabular dataset (Hugging Face: its-zion-18/Books-tabular-dataset)
+- Features: Height, Width, Depth, Page Count
+- Target: Genre (5 classes)
+
+## Training
+- Framework: AutoGluon Tabular
+- Preset: best_quality
+- Time Limit: 300 seconds
+- Best model: WeightedEnsemble_L2
+- Evaluation: Accuracy 1.0, Weighted F1 1.0 (on original split)
+
+## Limitations
+- Very small dataset (30 eval samples)
+- Augmented synthetic data may not generalize well
+
+## Usage
+Adjust the sliders and number field to input book dimensions and page count.
+The app will output predicted genre probabilities.

app.py

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+import pathlib
+import pandas
+import gradio
+import huggingface_hub
+import autogluon.tabular
+
+MODEL_REPO_ID = "FaiyazAzam/24679-tabular-autolguon-predictor"
+ZIP_FILENAME  = "autogluon_predictor_dir.zip"
+CACHE_DIR = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+
+FEATURE_COLS = ['Height', 'Width', 'Depth', 'Page Count']
+TARGET_COL = 'Genre'
+
+def _prepare_predictor_dir() -> str:
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    local_zip = huggingface_hub.hf_hub_download(
+        repo_id=MODEL_REPO_ID,
+        filename=ZIP_FILENAME,
+        repo_type="model",
+        local_dir=str(CACHE_DIR),
+        local_dir_use_symlinks=False,
+    )
+    if EXTRACT_DIR.exists():
+        import shutil, zipfile
+        shutil.rmtree(EXTRACT_DIR)
+    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
+    with zipfile.ZipFile(local_zip, "r") as zf:
+        zf.extractall(str(EXTRACT_DIR))
+    contents = list(EXTRACT_DIR.iterdir())
+    predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else EXTRACT_DIR
+    return str(predictor_root)
+
+PREDICTOR_DIR = _prepare_predictor_dir()
+PREDICTOR = autogluon.tabular.TabularPredictor.load(PREDICTOR_DIR, require_py_version_match=False)
+
+def do_predict(height, width, depth, page_count):
+    row = {
+        "Height": float(height),
+        "Width": float(width),
+        "Depth": float(depth),
+        "Page Count": int(page_count),
+    }
+    X = pandas.DataFrame([row], columns=FEATURE_COLS)
+    pred_series = PREDICTOR.predict(X)
+    raw_pred = pred_series.iloc[0]
+    try:
+        proba = PREDICTOR.predict_proba(X)
+        if isinstance(proba, pandas.Series):
+            proba = proba.to_frame().T
+    except Exception:
+        proba = None
+    proba_dict = None
+    if proba is not None:
+        row0 = proba.iloc[0]
+        tmp = {str(cls): float(val) for cls, val in row0.items()}
+        proba_dict = dict(sorted(tmp.items(), key=lambda kv: kv[1], reverse=True))
+    return proba_dict
+
+EXAMPLES = [
+    [20.0, 13.0, 3.0, 350],
+    [23.0, 15.0, 5.0, 600],
+    [18.0, 11.0, 2.0, 200],
+]
+
+with gradio.Blocks() as demo:
+    gradio.Markdown("# Predict Book Genre from Physical Features")
+    with gradio.Row():
+        height = gradio.Slider(10, 30, step=0.5, value=20.0, label="Height (cm)")
+        width = gradio.Slider(8, 25, step=0.5, value=13.0, label="Width (cm)")
+    with gradio.Row():
+        depth = gradio.Slider(1, 10, step=0.1, value=3.0, label="Depth (cm)")
+        page_count = gradio.Number(value=350, precision=0, label="Page Count")
+    proba_pretty = gradio.Label(num_top_classes=5, label="Predicted Genre Probabilities")
+    inputs = [height, width, depth, page_count]
+    for comp in inputs:
+        comp.change(fn=do_predict, inputs=inputs, outputs=[proba_pretty])
+    gradio.Examples(examples=EXAMPLES, inputs=inputs, label="Representative examples", cache_examples=False)
+
+demo.launch()

requirements.txt

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+gradio
+huggingface_hub
+pandas
+autogluon.tabular