update

app.py

@@ -259,7 +259,7 @@ if st.session_state.AM_input_changed == True:
     st.session_state.AM_design_button_clicked = False
     st.session_state.AM_input_changed = False
     
-st.button("AM process design", width=400, on_click=AM_design_click)
+st.button("AM process design", use_container_width=True, on_click=AM_design_click)
 
 if  st.session_state.AM_design_button_clicked == True:
     st.write("Process parameters")
@@ -282,21 +282,19 @@ if  st.session_state.AM_design_button_clicked == True:
     st.dataframe(data1, hide_index=True, width=500)
     
     data2 = pd.DataFrame({
-    'Nozzel velocity (cm/s)': best['input'][0],
-    'Extruder temperature (C)': best['input'][1],
-    'Bed temperature (C)': best['input'][2]})
+    'Nozzel velocity (cm/s)': [best['input'][0]],
+    'Extruder temperature (C)': [best['input'][1]],
+    'Bed temperature (C)': [best['input'][2]]})
     st.dataframe(data2, hide_index=True, width=500)
  
     data3 = pd.DataFrame({
-    'Maximum angle A (degree)': best['output'][0],
-    'Maximum angle B (degree)': best['output'][1],
-    'Maximum angle C (degree)': best['output'][2],
-    'Maximum residual stress': best['output'][3]
+    'Maximum angle A (degree)': [best['output'][0]],
+    'Maximum angle B (degree)': [best['output'][1]],
+    'Maximum angle C (degree)': [best['output'][2]],
+    'Maximum residual stress': [best['output'][3]]
     })
     st.dataframe(data3, hide_index=True, width=700)
 
 
 
 
-
-

model_fdm.py

@@ -5,7 +5,10 @@ from Dataset import DataAdditiveManufacturing, DataThermoforming
 from model import NeuralNetwork
 
 DEVICE = torch.device('cpu')
+<<<<<<< HEAD
 
+=======
+>>>>>>> 1951294 (update)
 # Set global plotting parameters
 plt.rcParams.update({'font.size': 14,
                      'figure.figsize': (10, 8),
@@ -249,7 +252,7 @@ def main():
     torch.save(checkpoint, model_save_path)
 
 def load_model(model_path):
-    checkpoint = torch.load(model_path)
+    checkpoint = torch.load(model_path, map_location=DEVICE)
     model_config = checkpoint['model_config']
     model = NeuralNetwork(model_config['layer_sizes'], dropout_rate=model_config['dropout_rate'], activation=torch.nn.ReLU).to(DEVICE)
     model.load_state_dict(checkpoint['model_state_dict'])