Update app.py

app.py

@@ -159,11 +159,11 @@ def predict(example_df,Kitchen_afternoon_illuminance, Lounge_night_illuminance,
 
     statements = []
     if proba[0][1] > 0.7814:
-        statements.append("There is a <span style='color: red; font-weight: bold;'>high risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: red; font-weight: bold;'>high probability estimate</span> that the patient was agitated the past week. During the previous week:")
     if 0.3225 < proba[0][1] <= 0.7814:
-        statements.append("There is a <span style='color: orange; font-weight: bold;'>medium risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: orange; font-weight: bold;'>medium probability estimate</span> that the patient was agitated the past week. During the previous week:")
     if proba[0][1] <= 0.3225:
-        statements.append("There is a <span style='color: green; font-weight: bold;'>low risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: green; font-weight: bold;'>low probability estimate</span> that the patient was agitated the past week. During the previous week:")
 
     for i, (shap_value, feature, feature_value) in enumerate(top_three_class_features):
         if feature_value > 0:
@@ -434,11 +434,11 @@ def update_data(example_df, Kitchen_afternoon_illuminance, Lounge_night_illumina
 
     statements = []
     if proba[0][1] > 0.7814:
-        statements.append("There is a <span style='color: red; font-weight: bold;'>high risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: red; font-weight: bold;'>high probability estimate</span> that the patient was agitated the past week. During the previous week:")
     if 0.3225 < proba[0][1] <= 0.7814:
-        statements.append("There is a <span style='color: orange; font-weight: bold;'>medium risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: orange; font-weight: bold;'>medium probability estimate</span> that the patient was agitated the past week. During the previous week:")
     if proba[0][1] <= 0.3225:
-        statements.append("There is a <span style='color: green; font-weight: bold;'>low risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: green; font-weight: bold;'>low probability estimate</span> that the patient was agitated the past week. During the previous week:")
     
     
     for i, (shap_value, feature, feature_value) in enumerate(top_three_class_features):
@@ -745,11 +745,11 @@ def return_original(example_df):
     statements = []
 
     if proba[0][1] > 0.7814:
-        statements.append("There is a <span style='color: red; font-weight: bold;'>high risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: red; font-weight: bold;'>high probability estimate</span> that the patient was agitated the past week. During the previous week:")
     if 0.3225 < proba[0][1] <= 0.7814:
-        statements.append("There is a <span style='color: orange; font-weight: bold;'>medium risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: orange; font-weight: bold;'>medium probability estimate</span> that the patient was agitated the past week. During the previous week:")
     if proba[0][1] <= 0.3225:
-        statements.append("There is a <span style='color: green; font-weight: bold;'>low risk</span> that the patient was agitated the past week. During the previous week:")
+        statements.append("There is a <span style='color: green; font-weight: bold;'>low probability estimate</span> that the patient was agitated the past week. During the previous week:")
 
     for i, (shap_value, feature, feature_value) in enumerate(top_three_class_features):
         if feature_value > 0:
@@ -1003,7 +1003,7 @@ with gr.Blocks(theme=gr.themes.Default(spacing_size="md", primary_hue="red", sec
     gr.HTML("""
         <div style="margin-top: 20px; text-align: center;">
             <h1 style="font-size: 36px; font-weight: bold; color: #377eb8;">
-               An Interpretable Machine Learning Tool for Weekly In-Home Monitoring of Agitation Episodes in People Living with Dementia : a proof of concept study
+               An Interpretable Machine Learning Tool for In-Home Monitoring of Agitation Episodes in People Living with Dementia : a proof of concept study
             </h1>
         </div>
     """)