Update app.py

app.py

@@ -5,12 +5,11 @@ import joblib
 import tensorflow as tf
 import pandas as pd
 from joblib import load
-
 from tensorflow.keras.models import load_model
 from sklearn.preprocessing import MinMaxScaler
 import os
-import sklearn
 
+# Display library versions
 print(f"Gradio version: {gr.__version__}")
 print(f"NumPy version: {np.__version__}")
 print(f"Scikit-learn version: {sklearn.__version__}")
@@ -112,19 +111,24 @@ outputs = [
 ]
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Environmental Factor-Based Contamination Level Prediction")
-    gr.Markdown("Enter the environmental factors to get the contamination levels for Front Left, Front Right, Left, Right, Roof, and Rear LiDARs.")
+    gr.Markdown("<h1 style='text-align: center;'>Environmental Factor-Based Contamination Level Prediction</h1>")
+    gr.Markdown("This application predicts the contamination levels on different parts of a car's LiDAR system based on environmental factors such as velocity, temperature, precipitation, and humidity.")
+    
+    # Layout with two columns
     with gr.Row():
         with gr.Column():
             gr.Markdown("### Input Parameters")
             for inp in inputs:
                 inp.render()
+
+            # Display the car image immediately after inputs
+            gr.Image(image_path, width=500, height=300)  # Adjust the width and height as needed
             gr.Button(value="Submit", variant="primary").click(fn=gradio_interface, inputs=inputs, outputs=outputs)
             gr.Button(value="Clear").click(fn=lambda: None)
+            
         with gr.Column():
             gr.Markdown("### Output Predictions")
             for out in outputs:
                 out.render()
-    gr.Image(image_path, width=500, height=300)  # Adjust the width and height as needed
 
 demo.launch()