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private_antimicrobial_coating_inverse_design

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snajmark commited on Jul 25, 2023

Commit

7721b2f

1 Parent(s): d210c54

Update app.py

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Files changed (1) hide show

app.py +32 -10

app.py CHANGED Viewed

@@ -318,9 +318,8 @@ test_data_columns = ['Binder_ADA',
  'Concetration (µg/mL)']
-### Define space and constraints
-constraints = []
 dimensionality_dict = {}
 one_hot_mapping = {}
 for c in categorical_columns:
@@ -335,12 +334,30 @@ for c in categorical_columns:
 domain = []
 for column in targets:
     df_columns.remove(column)
 for c in df_columns:
-    if c in numerical_columns:
-        domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.,1.)})
     else:
-        domain.append({'name': str(c), 'type': 'categorical', 'domain': (0,1),
-                       'dimensionality': dimensionality_dict[c]})
@@ -397,7 +414,7 @@ def predict_inverse(antimicrobial_activity_target, request: gr.Request):
     return optimized_x
-example_inputs = [80]
 css_styling = """#submit {background: #1eccd8}
 #submit:hover {background: #a2f1f6}
@@ -418,7 +435,7 @@ light_theme_colors = gr.themes.Color(c50="#e4f3fa", # Dataframe background cell
 # secondary color used for highlight box content when typing in light mode, and download option in dark mode
 # primary color used for login button in dark mode
 osium_theme = gr.themes.Default(primary_hue="cyan", secondary_hue="cyan", neutral_hue=light_theme_colors)
-page_title = "Recommendation of optimal parameters to fulfill coating antimicrobial activity requirement"
 favicon_path = "osiumai_favicon.ico"
 logo_path  = "osiumai_logo.jpg"
 html = f"""<html> <link rel="icon" type="image/x-icon" href="file={favicon_path}">
@@ -436,6 +453,10 @@ with gr.Blocks(css=css_styling, title=page_title, theme=osium_theme) as demo:
         with gr.Column():
             gr.Markdown("### The target antimicrobial activity of your textile coating")
             antimicrobial_activity_target = gr.Text(label="Enter the minimum acceptable antimicrobial activity for your textile coating")
         with gr.Column():
             with gr.Row():
@@ -444,13 +465,13 @@ with gr.Blocks(css=css_styling, title=page_title, theme=osium_theme) as demo:
                     optimal_conditions = gr.DataFrame(label="Optimal conditions")
     with gr.Row():
-        gr.Examples([example_inputs], [antimicrobial_activity_target])
     prediction_button.click(
         fn=predict_inverse,
-        inputs=[antimicrobial_activity_target],
         outputs=[optimal_conditions],
         show_progress=True,
     )
@@ -459,6 +480,7 @@ with gr.Blocks(css=css_styling, title=page_title, theme=osium_theme) as demo:
         [],
         [
             antimicrobial_activity_target,
             optimal_conditions,
         ],
     )

  'Concetration (µg/mL)']
+### Define space and constrains
 dimensionality_dict = {}
 one_hot_mapping = {}
 for c in categorical_columns:
 domain = []
 for column in targets:
     df_columns.remove(column)
+constrained_columns = ['Substrate', 'Washing_cycles', 'Microorganism ']
 for c in df_columns:
+    if c in constrained_columns:
+        if c.startswith('Substrate'):
+            if c == substrate:
+                domain.append({'name': str(c), 'type': 'categorical', 'domain': (1.0, 1.0)})
+            else:
+                domain.append({'name': str(c), 'type': 'categorical', 'domain': (0.0, 0.0)})
+        if c == 'Microorganism ':
+            if c == microorganism:
+                domain.append({'name': str(c), 'type': 'categorical', 'domain': (1.0, 1.0)})
+            else:
+                domain.append({'name': str(c), 'type': 'categorical', 'domain': (0.0, 0.0)})
+        if c == 'Washing_cycles':
+            domain.append({'name': str(c), 'type': 'categorical', 'domain': (int(num_washing_cycles), int(num_washing_cycles))})
     else:
+        if c in numerical_columns:
+            domain.append({'name': str(c), 'type': 'continuous', 'domain': (0.,1.)})
+        else:
+            domain.append({'name': str(c), 'type': 'categorical', 'domain': (0,1),
+                           'dimensionality': dimensionality_dict[c]})
+# Constraints
+constraints = []
     return optimized_x
+example_inputs = [80, "Substrate_Bamboo", "Microorganism _Alt_brassicicola", 50]
 css_styling = """#submit {background: #1eccd8}
 #submit:hover {background: #a2f1f6}
 # secondary color used for highlight box content when typing in light mode, and download option in dark mode
 # primary color used for login button in dark mode
 osium_theme = gr.themes.Default(primary_hue="cyan", secondary_hue="cyan", neutral_hue=light_theme_colors)
+page_title = "Recommendation of optimal parameters to fulfill coating antimicrobial activity requirement and constraints"
 favicon_path = "osiumai_favicon.ico"
 logo_path  = "osiumai_logo.jpg"
 html = f"""<html> <link rel="icon" type="image/x-icon" href="file={favicon_path}">
         with gr.Column():
             gr.Markdown("### The target antimicrobial activity of your textile coating")
             antimicrobial_activity_target = gr.Text(label="Enter the minimum acceptable antimicrobial activity for your textile coating")
+            gr.Markwdown("### Your constraints")
+            substrate = gr.Dropdown(label="Your substrate", choices=[c in test_data_columns if c.startwith("Substrate")])
+            num_washing_cycles = gr.Text("Your number of washing cycles")
+            microorganism = gr.Dropdown(label="Microorganism", choices=[c in test_data_columns if c.startwith("Microorganism"))
         with gr.Column():
             with gr.Row():
                     optimal_conditions = gr.DataFrame(label="Optimal conditions")
     with gr.Row():
+        gr.Examples([example_inputs], [antimicrobial_activity_target, substrate, microorganism, num_washing_cycles])
     prediction_button.click(
         fn=predict_inverse,
+        inputs=[antimicrobial_activity_target, substrate, microorganism, num_washing_cycles],
         outputs=[optimal_conditions],
         show_progress=True,
     )
         [],
         [
             antimicrobial_activity_target,
+            substrate, microorganism, num_washing_cycles,
             optimal_conditions,
         ],
     )