Update app.py

app.py

@@ -92,30 +92,54 @@ def predict_and_plot(velocity, temperature, precipitation, humidity):
             for i in range(contamination_levels.shape[1])
         ]).T
 
+        # Function to calculate cleaning time using linear interpolation
+        def calculate_cleaning_time(time_intervals, contamination_levels, threshold=0.4):
+            cleaning_times = []
+            for i in range(contamination_levels.shape[1]):
+                levels = contamination_levels[:, i]
+                for j in range(1, len(levels)):
+                    if levels[j-1] <= threshold <= levels[j]:
+                        # Linear interpolation
+                        t1, t2 = time_intervals[j-1], time_intervals[j]
+                        c1, c2 = levels[j-1], levels[j]
+                        cleaning_time = t1 + (threshold - c1) * (t2 - t1) / (c2 - c1)
+                        cleaning_times.append(cleaning_time)
+                        break
+                else:
+                    cleaning_times.append(time_intervals[-1])  # If threshold is not reached
+            return cleaning_times
+
+        # Calculate cleaning times for all 6 lidars
+        cleaning_times = calculate_cleaning_time(time_intervals, simulated_contamination_levels)
+
+        # Lidar names
+        lidar_names = ['F/L', 'F/R', 'Left', 'Right', 'Roof', 'Rear']
+
         # Plot the graph
         fig, ax = plt.subplots(figsize=(12, 8))
 
-        lidar_names = ['F/L', 'F/R', 'Left', 'Right', 'Roof', 'Rear']
         for i in range(simulated_contamination_levels.shape[1]):
             ax.plot(time_intervals, simulated_contamination_levels[:, i], label=f'{lidar_names[i]}')
             ax.axhline(y=0.4, color='r', linestyle='--', label='Contamination Threshold' if i == 0 else "")
+            if i < len(cleaning_times):
+                ax.scatter(cleaning_times[i], 0.4, color='k')  # Mark the cleaning time point
 
         ax.set_title('Contamination Levels Over Time for Each Lidar')
         ax.set_xlabel('Time (seconds)')
         ax.set_ylabel('Contamination Level')
         ax.legend()
         ax.grid(True)
-
+        
+        # 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"{600:.2f}" for _ in range(6)]  # Placeholder cleaning times
+        cleaning_time_output = [f"{val:.2f}" for val in cleaning_times]
 
         return [plot_output] + contamination_output + gradients_output + cleaning_time_output
 
     except Exception as e:
-        print(f"Error in prediction and plotting: {e}")
+        print(f"Error in Gradio interface: {e}")
         return [plt.figure()] + ["Error"] * 18
 
 inputs = [
@@ -162,19 +186,20 @@ with gr.Blocks() as demo:
             gr.Markdown("### Input Parameters")
             for inp in inputs:
                 inp.render()
-            # Submit and Clear Buttons under the inputs
-            with gr.Row():
-                gr.Button(value="Submit", variant="primary").click(
-                    fn=predict_and_plot, 
-                    inputs=inputs, 
-                    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(scale=1):
             gr.Image(image_path)
 
-    # Middle Section: Outputs (Three columns)
+    # Middle Section: Submit and Clear Buttons
+    with gr.Row():
+        gr.Button(value="Submit", variant="primary").click(
+            fn=predict_and_plot, 
+            inputs=inputs, 
+            outputs=[gr.Plot(label="Contamination Levels Over Time")] + contamination_outputs + gradients_outputs + cleaning_time_outputs
+        )
+        gr.Button(value="Clear").click(fn=lambda: None)
+
+    # Bottom Section: Outputs (Three columns) and Plot Below
     with gr.Row():
         with gr.Column(scale=2):
             gr.Markdown("### Contamination Predictions")
@@ -191,4 +216,10 @@ with gr.Blocks() as demo:
             for out in cleaning_time_outputs:
                 out.render()
 
+    # Graph below the outputs
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Contamination Levels Over Time")
+            gr.Plot(label="Contamination Levels Over Time")
+
 demo.launch()