Update app.py

app.py

@@ -64,8 +64,8 @@ def predict_and_plot(velocity, temperature, precipitation, humidity):
         # Scale the example data
         example_data_scaled = scaler_X.transform(example_data)
 
-        # Function to predict contamination levels
-        def predict_contamination(example_data_scaled):
+        # Function to predict contamination levels and gradients
+        def predict_contamination_and_gradients(example_data_scaled):
             # Predict using MLP model
             mlp_predictions_contamination, mlp_predictions_gradients = loaded_mlp_model.predict(example_data_scaled)
 
@@ -78,10 +78,10 @@ def predict_and_plot(velocity, temperature, precipitation, humidity):
             # Predict using meta model
             meta_predictions = loaded_meta_model.predict(combined_features)
 
-            return meta_predictions[:, :6]  # Assuming the first 6 columns are contamination predictions
+            return meta_predictions[:, :6], meta_predictions[:, 6:]  # Split predictions into contamination and gradients
 
-        # Predict contamination levels for the single example
-        contamination_levels = predict_contamination(example_data_scaled)
+        # Predict contamination levels and gradients for the single example
+        contamination_levels, gradients = predict_contamination_and_gradients(example_data_scaled)
 
         # Simulate contamination levels at multiple time intervals
         time_intervals = np.arange(0, 601, 60)  # Simulating time intervals from 0 to 600 seconds
@@ -130,16 +130,17 @@ def predict_and_plot(velocity, temperature, precipitation, humidity):
         ax.legend()
         ax.grid(True)
         
-        # Flatten the results into a single list of 13 outputs
+        # Flatten the results into a single list of 19 outputs (1 plot + 6 contamination + 6 gradients + 6 cleaning times)
         plot_output = fig
         contamination_output = [f"{val * 100:.2f}%" for val in contamination_levels[0]]
+        gradients_output = [f"{val:.4f}" for val in gradients[0]]
         cleaning_time_output = [f"{val:.2f}" for val in cleaning_times]
 
-        return [plot_output] + contamination_output + cleaning_time_output
+        return [plot_output] + contamination_output + gradients_output + cleaning_time_output
 
     except Exception as e:
         print(f"Error in Gradio interface: {e}")
-        return [plt.figure()] + ["Error"] * 12
+        return [plt.figure()] + ["Error"] * 18
 
 inputs = [
     gr.Slider(minimum=0, maximum=100, value=50, step=0.05, label="Velocity (mph)"),
@@ -157,6 +158,15 @@ contamination_outputs = [
     gr.Textbox(label="Rear Contamination")
 ]
 
+gradients_outputs = [
+    gr.Textbox(label="Front Left Gradient"),
+    gr.Textbox(label="Front Right Gradient"),
+    gr.Textbox(label="Left Gradient"),
+    gr.Textbox(label="Right Gradient"),
+    gr.Textbox(label="Roof Gradient"),
+    gr.Textbox(label="Rear Gradient")
+]
+
 cleaning_time_outputs = [
     gr.Textbox(label="Front Left Cleaning Time"),
     gr.Textbox(label="Front Right Cleaning Time"),
@@ -167,8 +177,8 @@ cleaning_time_outputs = [
 ]
 
 with gr.Blocks() as demo:
-    gr.Markdown("<h1 style='text-align: center;'>Environmental Factor-Based Contamination & Cleaning Time Prediction</h1>")
-    gr.Markdown("This application predicts the contamination levels, corresponding gradients, and cleaning times for different parts of a car's LiDAR system based on environmental factors such as velocity, temperature, precipitation, and humidity.")
+    gr.Markdown("<h1 style='text-align: center;'>Environmental Factor-Based Contamination, Gradient, & Cleaning Time Prediction</h1>")
+    gr.Markdown("This application predicts the contamination levels, gradients, and cleaning times for different parts of a car's LiDAR system based on environmental factors such as velocity, temperature, precipitation, and humidity.")
     
     with gr.Row():
         with gr.Column():
@@ -184,18 +194,29 @@ with gr.Blocks() as demo:
             gr.Button(value="Submit", variant="primary").click(
                 fn=predict_and_plot, 
                 inputs=inputs, 
-                outputs=[gr.Plot(label="Contamination Levels Over Time")] + contamination_outputs + cleaning_time_outputs
+                outputs=[gr.Plot(label="Contamination Levels Over Time")] + contamination_outputs + gradients_outputs + cleaning_time_outputs
             )
             gr.Button(value="Clear").click(fn=lambda: None)
             
-        with gr.Column():
-            gr.Markdown("### Contamination Predictions")
-            for out in contamination_outputs:
-                out.render()
+    # Plot above the three columns of outputs
+    with gr.Column():
+        gr.Markdown("### Predictions")
+        gr.Plot(label="Contamination Levels Over Time").render()
+
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("#### Contamination Predictions")
+                for out in contamination_outputs:
+                    out.render()
                 
-        with gr.Column():
-            gr.Markdown("### Cleaning Time Predictions")
-            for out in cleaning_time_outputs:
-                out.render()
+            with gr.Column():
+                gr.Markdown("#### Gradient Predictions")
+                for out in gradients_outputs:
+                    out.render()
+
+            with gr.Column():
+                gr.Markdown("#### Cleaning Time Predictions")
+                for out in cleaning_time_outputs:
+                    out.render()
 
 demo.launch()