Update app.py

app.py

@@ -24,6 +24,7 @@ scaler_path = os.path.join(script_dir, 'toolkit', 'scaler_X.json')
 rf_model_path = os.path.join(script_dir, 'toolkit', 'rf_model.joblib')
 mlp_model_path = os.path.join(script_dir, 'toolkit', 'mlp_model.keras')
 meta_model_path = os.path.join(script_dir, 'toolkit', 'meta_model.joblib')
+image_path = os.path.join(script_dir, 'toolkit', 'car.png')
 
 # Load the scaler and models
 try:
@@ -110,10 +111,19 @@ outputs = [
     gr.Textbox(label="Rear")
 ]
 
-gr.Interface(
-    fn=gradio_interface, 
-    inputs=inputs, 
-    outputs=outputs, 
-    title="Environmental Factor-Based Contamination Level Prediction",
-    description="Enter the environmental factors to get the contamination levels for Front Left, Front Right, Left, Right, Roof, and Rear LiDARs."
-).launch()
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Environmental Factor-Based Contamination Level Prediction\nEnter the environmental factors to get the contamination levels for Front Left, Front Right, Left, Right, Roof, and Rear LiDARs.")
+            gr.Markdown("### Input Parameters")
+            for inp in inputs:
+                inp.render()
+            gr.Button(value="Submit", variant="primary").click(fn=gradio_interface, inputs=inputs, outputs=outputs)
+            gr.Button(value="Clear").click(fn=lambda: None)
+            gr.Image(image_path, width=400, height=250)  # Adjust the width and height as needed
+        with gr.Column():
+            gr.Markdown("### Output Predictions")
+            for out in outputs:
+                out.render()
+
+demo.launch()