Update app.py

app.py

@@ -51,7 +51,7 @@ try:
 except Exception as e:
     print(f"Error loading models or scaler: {e}")
 
-def predict_contamination_gradients(velocity, temperature, precipitation, humidity):
+def predict_and_plot(velocity, temperature, precipitation, humidity):
     try:
         # Prepare the example data
         example_data = pd.DataFrame({
@@ -83,24 +83,13 @@ def predict_contamination_gradients(velocity, temperature, precipitation, humidi
         # Predict contamination levels and gradients for the single example
         contamination_levels, gradients = predict_contamination_and_gradients(example_data_scaled)
 
-        return contamination_levels[0], gradients[0]
-
-    except Exception as e:
-        print(f"Error in Gradio interface: {e}")
-        return ["Error"] * 12
-
-def plot_contamination_over_time(velocity, temperature, precipitation, humidity):
-    try:
-        # Predict contamination levels first
-        contamination_levels, _ = predict_contamination_gradients(velocity, temperature, precipitation, humidity)
-
         # Simulate contamination levels at multiple time intervals
         time_intervals = np.arange(0, 601, 60)  # Simulating time intervals from 0 to 600 seconds
 
         # Generate simulated contamination levels (linear interpolation between predicted values)
         simulated_contamination_levels = np.array([
-            np.linspace(contamination_levels[i], contamination_levels[i] * 2, len(time_intervals))
-            for i in range(len(contamination_levels))
+            np.linspace(contamination_levels[0][i], contamination_levels[0][i] * 2, len(time_intervals))
+            for i in range(contamination_levels.shape[1])
         ]).T
 
         # Plot the graph
@@ -117,11 +106,17 @@ def plot_contamination_over_time(velocity, temperature, precipitation, humidity)
         ax.legend()
         ax.grid(True)
 
-        return fig
+        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
+
+        return [plot_output] + contamination_output + gradients_output + cleaning_time_output
 
     except Exception as e:
-        print(f"Error in plotting: {e}")
-        return plt.figure()
+        print(f"Error in prediction and plotting: {e}")
+        return [plt.figure()] + ["Error"] * 18
 
 inputs = [
     gr.Slider(minimum=0, maximum=100, value=50, step=0.05, label="Velocity (mph)"),
@@ -170,9 +165,9 @@ with gr.Blocks() as demo:
             # Submit and Clear Buttons under the inputs
             with gr.Row():
                 gr.Button(value="Submit", variant="primary").click(
-                    fn=predict_contamination_gradients, 
+                    fn=predict_and_plot, 
                     inputs=inputs, 
-                    outputs=contamination_outputs + gradients_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)
 
@@ -196,14 +191,4 @@ with gr.Blocks() as demo:
             for out in cleaning_time_outputs:
                 out.render()
 
-    # Bottom Section: Graph at the very end
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("### Contamination Levels Over Time")
-            gr.Plot(label="Contamination Levels Over Time").click(
-                fn=plot_contamination_over_time, 
-                inputs=inputs, 
-                outputs="plot"
-            )
-
 demo.launch()