Update app.py

app.py

@@ -19,6 +19,7 @@ def preprocess_input_data(input_data):
 
     return input_scaled_df
 
+
 # Define a function to make the sepsis prediction
 def predict_sepsis(input_data):
     input_scaled_df = preprocess_input_data(input_data)
@@ -26,19 +27,15 @@ def predict_sepsis(input_data):
     probabilities = model.predict_proba(input_scaled_df)[0]
     sepsis_status = "Positive" if prediction == 1 else "Negative"
     
-    if prediction == 1:
-        status_icon = "✔"  # Red 'X' icon for positive sepsis prediction
-        sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention."
-    else:
-        status_icon = "✘"  # Green checkmark icon for negative sepsis prediction
-        sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
+    status_icon = "✔" if prediction == 1 else "✘"  # Red 'X' icon for positive sepsis prediction, green checkmark icon for negative sepsis prediction
+    sepsis_explanation = "Sepsis is a life-threatening condition caused by an infection. A positive prediction suggests that the patient might be exhibiting sepsis symptoms and requires immediate medical attention." if prediction == 1 else "Sepsis is a life-threatening condition caused by an infection. A negative prediction suggests that the patient is not currently exhibiting sepsis symptoms."
 
     output_df = pd.DataFrame(input_data, columns=['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance'])
     output_df['Prediction'] = sepsis_status
     output_df['Negative Probability'] = probabilities[0]
     output_df['Positive Probability'] = probabilities[1]
 
-    return output_df, probabilities
+    return output_df, probabilities, status_icon, sepsis_explanation
 
 # Create a Streamlit app
 def main():