Update app.py

app.py

@@ -1,31 +1,38 @@
-import gradio as gr
-import pandas as pd
-from autogluon.tabular import TabularPredictor
-from huggingface_hub import snapshot_download
-import os, zipfile
-import torch
+# Import required libraries
+import gradio as gr                              # for building the user interface
+import pandas as pd                              # for handling tabular data
+from autogluon.tabular import TabularPredictor   # AutoGluon class to load tabular models
+from huggingface_hub import snapshot_download    # to download model files from Hugging Face Hub
+import os, zipfile                               # for file path operations and unzipping
+import torch                                     # model/device handling
 
+# Define the Hugging Face repo ID where the trained predictor is stored
 REPO_ID = "FaiyazAzam/24679-tabular-autolguon-predictor"
+
+# Download the repo snapshot locally
 repo_dir = snapshot_download(repo_id=REPO_ID)
 
-# unzip the dir that holds the predictor
+# Unzip the model directory into /tmp (safe location on Spaces)
 zip_path = os.path.join(repo_dir, "autogluon_predictor_dir.zip")
 extract_to = "/tmp/predictor_dir"
 if not os.path.exists(extract_to):
     with zipfile.ZipFile(zip_path, "r") as zf:
         zf.extractall(extract_to)
 
+# Load the AutoGluon Tabular predictor
 predictor = TabularPredictor.load(
     extract_to,
     require_py_version_match=False)
 
+# Function that takes book dimensions + page count and returns genre prediction
 def predict_tabular(height, width, depth, page_count):
-    # Validation
+    # Validate inputs (must be positive)
     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.")
 
+    # Build a single row DataFrame with the provided inputs
     row = {
         "Height": height,
         "Width": width,
@@ -33,13 +40,18 @@ def predict_tabular(height, width, depth, page_count):
         "Page Count": page_count,
     }
     df = pd.DataFrame([row])
+
+    # Run prediction with the loaded AutoGluon model
     pred = int(predictor.predict(df)[0])
 
+    # Return the prediction as a string
     return f"Predicted Genre Code: {pred}"
 
+# Build the Gradio UI
 with gr.Blocks(title="Book Genre Predictor") as demo:
     gr.Markdown("## Predict the Genre of a Book (Numeric Labels)")
 
+    # Input fields arranged in a row and column layout
     with gr.Row():
         with gr.Column():
             height = gr.Number(label="Height (cm)", info="Book height in cm")
@@ -47,15 +59,20 @@ with gr.Blocks(title="Book Genre Predictor") as demo:
             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)")
 
+    # Output text box to display results
     out = gr.Textbox(label="Result")
+
+    # Prediction button
     run_btn = gr.Button("Predict")
 
+    # Connect button click to prediction function
     run_btn.click(
         predict_tabular,
         inputs=[height, width, depth, page_count],
         outputs=out
     )
 
+    # Pre loaded example inputs for quick testing
     gr.Examples(
         examples=[
             [20.1, 13.5, 1.8, 250],
@@ -66,4 +83,5 @@ with gr.Blocks(title="Book Genre Predictor") as demo:
         outputs=out
     )
 
+# Launch the Gradio app
 demo.launch(share=True)