Update app.py

app.py

@@ -38,7 +38,7 @@ VKORC1_genotype = gr.Dropdown(choices=list(VKORC1_genotype_dict.keys()), label="
 model = gr.Dropdown(choices=["Linear Regression", "Ridge Regression", "Decision Tree", "KNN","ANN", "Random Forest"], label="Model")
 
 
-# Define the function for making predictions
+# Modify the gender encoding and ensure all categorical variables are properly encoded
 def multi_inputs(age, gender, race, weight, height, diabetes, simvastatin, amiodarone, INR_reported, cyp2C9_genotypes, VKORC1_genotype, model, dose):
     # Load the appropriate model based on the selected model
     if model == 'Linear Regression':
@@ -57,6 +57,7 @@ def multi_inputs(age, gender, race, weight, height, diabetes, simvastatin, amiod
     # Map categorical variables to their encoded values
     age_encoded = age_dict.get(age)
     race_encoded = race_dict.get(race)
+    gender_encoded = 0 if gender == "male" else 1  # Encoding gender
     diabetes_encoded = diabetes_dict.get(diabetes)
     simvastatin_encoded = simvastatin_dict.get(simvastatin)
     amiodarone_encoded = amiodarone_dict.get(amiodarone)
@@ -64,16 +65,15 @@ def multi_inputs(age, gender, race, weight, height, diabetes, simvastatin, amiod
     VKORC1_genotype_encoded = VKORC1_genotype_dict.get(VKORC1_genotype)
 
     # Transform input data
-    inputs = [age_encoded, gender, race_encoded, weight, height, diabetes_encoded, simvastatin_encoded, amiodarone_encoded, INR_reported, cyp2C9_genotypes_encoded, VKORC1_genotype_encoded]
-    inputs[1] = 0 if inputs[1] == "male" else 1  # Encoding gender to 0 for male and 1 for female
-    inputs = scaler.transform([inputs])
+    inputs = [age_encoded, gender_encoded, race_encoded, weight, height, diabetes_encoded, simvastatin_encoded, amiodarone_encoded, INR_reported, cyp2C9_genotypes_encoded, VKORC1_genotype_encoded]
 
     # Predict therapeutic dose of warfarin
-    input_data = np.array([inputs[0]])
+    input_data = np.array([inputs])
     output = model.predict(input_data)
 
     return output
 
+
 # Create the Gradio interface
 inputs = [age, gender, race, weight, height, diabetes, simvastatin, amiodarone, INR_reported, cyp2C9_genotypes, VKORC1_genotype, model]
 outputs = gr.Textbox(label="Predicted Therapeutic Dose of Warfarin")