Update app.py

app.py

@@ -1153,6 +1153,19 @@ class Network(object):
       # return idx, other_data_input, true_output, predicted_output
       return pandas.concat([fd, df_pred], axis=1), pandas.concat([fd, df_true], axis=1)
 
+
+  def analysis_from_geometry(self, geometry):
+      # Get the outputs
+      predicted_output = self.network.predict(geometry.flatten())
+      predicted_output = predicted_output.reshape((3, self.F))
+
+      f = numpy.linspace(0.05, 2.0, 64)
+      fd = pandas.DataFrame(f).rename(columns={0: "Frequency"})
+      df_pred = pandas.DataFrame(predicted_output.transpose()).rename(columns={0: "Surge", 1: "Heave", 2: "Pitch"})
+
+      return pandas.concat([fd, df_pred], axis=1), pandas.DataFrame()
+
+
   def synthesis(self, idx=None):
       print(idx)
 
@@ -1251,9 +1264,13 @@ def geometry(index):
     values = value_net.get_geometry(index)
     return plotly_fig(values)
 
-def simple_analysis(index): 
-    forward_net = Network("16forward_structure.json", "16forward_weights.h5")  
-    return forward_net.analysis(index)
+def simple_analysis(index, choice, shape, length, width, height, diameter): 
+    forward_net = Network("16forward_structure.json", "16forward_weights.h5")
+    if choice == "Construct Shape from Parameters":  
+        return forward_net.analysis_from_geometry(make_voxels(shape, length, height, width, diameter))
+    elif choice == "Pick Shape from Dataset":
+        return forward_net.analysis(index)
+    
 
 def simple_synthesis(index):
     inverse_net = Network("16inverse_structure.json", "16inverse_weights.h5")
@@ -1356,8 +1373,7 @@ with gradio.Blocks() as demo:
         num.change(fn=geometry, inputs=[num], outputs=[geo])
         
         btn1.click(fn=randomize_analysis, inputs=[whence_commeth_geometry], outputs=[radio, length, height, width, diameter, num, geo])
-        btn2.click(fn=simple_analysis, inputs=[num], outputs=[pred, true])
-    
+        btn2.click(fn=simple_analysis, inputs=[num, whence_commeth_geometry, radio, length, width, height, diameter], outputs=[pred, true])
     with gradio.Tab("Synthesis"):
         with gradio.Tab("Spectrum from Dataset"):