Update app.py

app.py

@@ -111,4 +111,89 @@ def predict_and_plot(velocity, temperature, precipitation, humidity):
         cleaning_times = calculate_cleaning_time(time_intervals, simulated_contamination_levels)
 
         # Lidar names
-        lidar_names = ['F/L', 'F/R', 'Left', '
+        lidar_names = ['F/L', 'F/R', 'Left', 'Right', 'Roof', 'Rear']
+
+        # Plot the graph
+        plt.figure(figsize=(12, 8))
+
+        for i in range(simulated_contamination_levels.shape[1]):
+            plt.plot(time_intervals, simulated_contamination_levels[:, i], label=f'{lidar_names[i]}')
+            plt.axhline(y=0.4, color='r', linestyle='--', label='Contamination Threshold' if i == 0 else "")
+            if i < len(cleaning_times):
+                plt.scatter(cleaning_times[i], 0.4, color='k')  # Mark the cleaning time point
+
+        plt.title('Contamination Levels Over Time for Each Lidar')
+        plt.xlabel('Time (seconds)')
+        plt.ylabel('Contamination Level')
+        plt.legend()
+        plt.grid(True)
+        
+        # Flatten the results into a single list of 13 outputs
+        plot_output = plt
+        contamination_output = [f"{val * 100:.2f}%" for val in contamination_levels[0]]
+        cleaning_time_output = [f"{val:.2f}" for val in cleaning_times]
+
+        return [plot_output] + contamination_output + cleaning_time_output
+
+    except Exception as e:
+        print(f"Error in Gradio interface: {e}")
+        return [plt.figure()] + ["Error"] * 12
+
+inputs = [
+    gr.Slider(minimum=0, maximum=100, value=50, step=0.05, label="Velocity (mph)"),
+    gr.Slider(minimum=-2, maximum=30, value=0, step=0.5, label="Temperature (°C)"),
+    gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label="Precipitation (inch)"),
+    gr.Slider(minimum=0, maximum=100, value=50, label="Humidity (%)")
+]
+
+contamination_outputs = [
+    gr.Textbox(label="Front Left Contamination"),
+    gr.Textbox(label="Front Right Contamination"),
+    gr.Textbox(label="Left Contamination"),
+    gr.Textbox(label="Right Contamination"),
+    gr.Textbox(label="Roof Contamination"),
+    gr.Textbox(label="Rear Contamination")
+]
+
+cleaning_time_outputs = [
+    gr.Textbox(label="Front Left Cleaning Time"),
+    gr.Textbox(label="Front Right Cleaning Time"),
+    gr.Textbox(label="Left Cleaning Time"),
+    gr.Textbox(label="Right Cleaning Time"),
+    gr.Textbox(label="Roof Cleaning Time"),
+    gr.Textbox(label="Rear Cleaning Time")
+]
+
+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.")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Input Parameters")
+            for inp in inputs:
+                inp.render()
+
+            # Centered image display
+            with gr.Row():
+                with gr.Column(scale=1, min_width=0):
+                    gr.Image(image_path)  # Ensure the image is centered
+            
+            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
+            )
+            gr.Button(value="Clear").click(fn=lambda: None)
+            
+        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()
+
+demo.launch()