Create app.py

app.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 = "FaiyazAzam/24679-tabular-autolguon-predictor"
+repo_dir = snapshot_download(repo_id=REPO_ID)
+
+# unzip the dir that holds the predictor
+zip_path = os.path.join(repo_dir, "autogluon_predictor_dir.zip")
+extract_to = "/content/predictor_dir"
+if not os.path.exists(extract_to):
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        zf.extractall(extract_to)
+
+predictor = TabularPredictor.load(extract_to)
+
+def predict_tabular(height, width, depth, page_count):
+    # Validation
+    if height <= 0 or width <= 0 or depth <= 0:
+        raise ValueError("All dimensions must be positive numbers.")
+    if page_count <= 0:
+        raise ValueError("Page count must be a positive integer.")
+
+    row = {
+        "Height": height,
+        "Width": width,
+        "Depth": depth,
+        "Page Count": page_count,
+    }
+    df = pd.DataFrame([row])
+    pred = int(predictor.predict(df)[0])
+
+    return {f"Predicted Genre = {pred}"}
+
+with gr.Blocks(title="Book Genre Predictor") as demo:
+    gr.Markdown("## Predict the Genre of a Book (Numeric Labels)")
+
+    with gr.Row():
+        with gr.Column():
+            height = gr.Number(label="Height (cm)", info="Book height in cm")
+            width = gr.Number(label="Width (cm)", info="Book width in cm")
+            depth = gr.Number(label="Depth (cm)", info="Book spine thickness in cm")
+            page_count = gr.Number(label="Page Count", info="Number of pages (positive integer)")
+
+    out = gr.JSON(label="Result")
+    run_btn = gr.Button("Predict")
+
+    run_btn.click(
+        predict_tabular,
+        inputs=[height, width, depth, page_count, show_proba],
+        outputs=out
+    )
+
+    gr.Examples(
+        examples=[
+            [20.1, 13.5, 1.8, 250],
+            [24.0, 15.0, 2.2, 320],
+            [18.5, 12.0, 1.5, 180],
+        ],
+        inputs=[height, width, depth, page_count, show_proba],
+        outputs=out
+    )
+
+
+demo.launch(share=True)
+