thermo

app.py

@@ -174,7 +174,7 @@ def plot_thermo(thermo_data):
     #ax1.legend(loc='lower right')
     return fig
 
-def predict(input_file):
+def predict(input_file, charge):
     input_f = open(input_file.name, "r").read()
     input_format = input_file.name.split('.')[-1]
 
@@ -182,69 +182,28 @@ def predict(input_file):
         oF.write(input_f)
     
     input_f2 = open('dummy_struct.'+input_format, "r").read()
-    gfn2_input = ["filename","geom=dummy_struct."+input_format, 'calcdens=1', 'thermo=1']
+    gfn2_input = ["filename","geom=dummy_struct."+input_format, 'calcdens=1', 'thermo=1', "charge="+charge]
     gfn2_output = run_gfn2(gfn2_input)
-    print(gfn2_output['fetchthermo'].keys())
 
-    #qm_mol_features = get_qm_mol_features(gfn2_output, checked_features)
-    #basic_visualization_html = get_basic_visualization(input_f,input_format)
     feature_visualization_html = get_feature_visualization(input_f2,input_format, gfn2_output, gfn2_output['xyz'])
     qm_atom_features = get_qm_atom_features(gfn2_output)
     thermo_data = get_thermo_data(gfn2_output)
     plot = plot_thermo(thermo_data)
-    #pd.DataFrame(thermo_data)
-    return feature_visualization_html, pd.DataFrame(qm_atom_features), pd.DataFrame(thermo_data), plot#, pd.DataFrame(thermo_data)#, pd.DataFrame(qm_mol_features, index=[0])
-
-
-def line_plot_fn(input_exe):
-    #input_f = open(input_file.name, "r").read()
-    #input_format = input_file.name.split('.')[-1]
-#
-    #with open('dummy_struct.'+input_format, "w") as oF:
-    #    oF.write(input_f)
-    
-    #input_f2 = open('dummy_struct.'+input_format, "r").read()
-    gfn2_input = ["filename",input_exe, 'thermo=1']
-    gfn2_output = run_gfn2(gfn2_input)
-    print(gfn2_output['fetchthermo'].keys())
-
-    #qm_mol_features = get_qm_mol_features(gfn2_output, checked_features)
-    #basic_visualization_html = get_basic_visualization(input_f,input_format)
-    #feature_visualization_html = get_feature_visualization(input_f2,input_format, gfn2_output, gfn2_output['xyz'])
-    #qm_atom_features = get_qm_atom_features(gfn2_output)
-    thermo_data = get_thermo_data(gfn2_output)
-    pd.DataFrame(thermo_data)
-    return pd.DataFrame(thermo_data)#, pd.DataFrame(qm_mol_features, index=[0])
-
 
+    return feature_visualization_html, pd.DataFrame(qm_atom_features), pd.DataFrame(thermo_data), plot#, pd.DataFrame(thermo_data)#, pd.DataFrame(qm_mol_features, index=[0])
 
-#def get_plot_data(input_format):
-#    gfn2_input = ["filename","geom=dummy_struct."+input_format, 'thermo=1']
-#    gfn2_output = run_gfn2(gfn2_input)   
-#    print('plot data output', gfn2_output)
 
 with gr.Blocks() as demo:
     gr.Markdown("# QM property calculation")
-    
-    #text_input = gr.Textbox()
-    #text_output = gr.Textbox()
-    #text_button = gr.Button("Flip")
-    
-    
-
     with gr.Row():
         input_file = gr.File(label="Structure file for input (xyz format)")
         input_file2 = input_file
         charge = gr.Textbox(placeholder="Total charge", label="Give the total charge of the input molecule. (Default=0)")
     
-    #checked_features = gr.CheckboxGroup(["Charge", "Polarizability", "Koopman IP", "Electronic Density"], label="QM features", info="Which features shall be calculated?")
-    #temperature = gr.Slider(value=300,minimum=0, maximum=1000, label="Temperature for Thermodynamics evaluation in K", step=5)
-
     single_btn = gr.Button(label="Run")
     with gr.Row():
         basic_html = gr.HTML()
         plot = gr.Plot()
-        #gr.LinePlot(get_plot_data(input_format), every=5, x="Date", y="Sales", y_title="Sales ($ millions)", overlay_point=True, width=500, height=500)
     gr.HighlightedText(value=[("Positive Charge","Purple"),("Negative charge","red"),("Polarizability","Light blue"), ("Electronic Densities", "Beige")], color_map={"red":"#a0210f", "Light blue":"#809BAC", "Purple":"#4c4e9e", "Beige":"#F7D7BE"})
 
     with gr.Row():
@@ -257,13 +216,10 @@ with gr.Blocks() as demo:
         df_atom_features = gr.Dataframe()
         df_thermo_props = gr.Dataframe()
 
-    #with gr.Row():
-    #    plot = gr.Plot()
       
-    single_btn.click(fn=predict, inputs=[input_file], outputs=[basic_html, df_atom_features, df_thermo_props, plot])
+    single_btn.click(fn=predict, inputs=[input_file, charge], outputs=[basic_html, df_atom_features, df_thermo_props, plot])
     Dbutton.click(export_csv, df_atom_features, csv)
-    D2button.click(export_csv_thermo, df_thermo_props, csv2)
-    #demo.load(fn=line_plot_fn, inputs=[input_exe], ouputs=plot)  
+    D2button.click(export_csv_thermo, df_thermo_props, csv2) 
 
 
     demo.launch(server_name="0.0.0.0", server_port=7860)