Update app.py

app.py

@@ -32,6 +32,7 @@ height = gr.Number(label="Height")
 diabetes = gr.Radio(["0.0", "1.0"], label="Diabetes")
 simvastatin = gr.Radio(["0.0", "1.0"], label="Simvastatin")
 amiodarone = gr.Radio(["0.0", "1.0"], label="Amiodarone")
+Target_INR=gr.Number(label="Target INR")
 INR_reported = gr.Number(label="INR on Reported Therapeutic Dose of Warfarin")
 cyp2C9_genotypes = gr.Dropdown(choices=list(cyp2C9_genotypes_dict.keys()), label="Cyp2C9 genotypes")
 VKORC1_genotype = gr.Dropdown(choices=list(VKORC1_genotype_dict.keys()), label="VKORC1 genotype")
@@ -39,7 +40,7 @@ model = gr.Dropdown(choices=["Linear Regression", "Ridge Regression", "Decision
 
 
 # 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):
+def multi_inputs(age, gender, race, weight, height, diabetes, simvastatin, amiodarone, Target_INR, INR_reported, cyp2C9_genotypes, VKORC1_genotype, model, dose):
     # Load the appropriate model based on the selected model
     if model == 'Linear Regression':
         model = load('Linear_regressor_model.pkl')
@@ -65,7 +66,7 @@ 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_encoded, race_encoded, weight, height, diabetes_encoded, simvastatin_encoded, amiodarone_encoded, INR_reported, cyp2C9_genotypes_encoded, VKORC1_genotype_encoded]
+    inputs = [gender_encoded, race_encoded,age_encoded, height, weight, diabetes_encoded, simvastatin_encoded, amiodarone_encoded, Target_INR ,INR_reported, cyp2C9_genotypes_encoded, VKORC1_genotype_encoded]
 
     # Predict therapeutic dose of warfarin
     input_data = np.array([inputs])
@@ -75,6 +76,6 @@ def multi_inputs(age, gender, race, weight, height, diabetes, simvastatin, amiod
 
 
 # Create the Gradio interface
-inputs = [age, gender, race, weight, height, diabetes, simvastatin, amiodarone, INR_reported, cyp2C9_genotypes, VKORC1_genotype, model]
+inputs = [age, gender, race, weight, height, diabetes, simvastatin, amiodarone, Target_INR, INR_reported, cyp2C9_genotypes, VKORC1_genotype, model]
 outputs = gr.Textbox(label="Predicted Therapeutic Dose of Warfarin")
 gr.Interface(fn=multi_inputs, inputs=inputs, outputs=outputs).launch(share=True, debug=True)