Update app.py

app.py

@@ -5,27 +5,22 @@ from tensorflow.keras.models import load_model
 import tensorflow as tf
 import pickle
 
-# Load the LabelEncoder from Hugging Face Space
+# Load the LabelEncoder and ColumnTransformer before prediction
 with open('label_encoder.pkl', 'rb') as f:
     label_encoder = pickle.load(f)
 
-# Assuming 'ct' is your ColumnTransformer (replace this with the actual loading code for your preprocessor)
-# If you have a preprocessor file (such as a pickle file), you can load it here, e.g.,
-# with open('column_transformer.pkl', 'rb') as f:
-#     ct = pickle.load(f)
-
 # UI Components for user input
 input_Gender = gr.Radio(["male", "female"], label="Gender")
-input_Race = gr.Dropdown(list(dict(df['Race (Reported)'].value_counts()).keys()), label="Race")
-input_Age = gr.Dropdown(list(dict(df['Age'].value_counts())), label='Age')
+input_Race = gr.Dropdown([], label="Race")  # Use empty list as placeholder
+input_Age = gr.Dropdown([], label='Age')  # Use empty list as placeholder
 input_Height = gr.Number(label='Height (cm)')
 input_Weight = gr.Number(label='Weight (kg)')
 input_Diabetes = gr.Radio([0.0, 1.0], label='Diabetes')
 input_Simvastatin = gr.Radio([0.0, 1.0], label='Simvastatin (Zocor)')
 input_Amiodarone = gr.Radio([0.0, 1.0], label='Amiodarone (Cordarone)')
 input_INR_reported = gr.Number(label='INR on Reported Therapeutic Dose of Warfarin')
-input_Cyp2C9_genotypes = gr.Dropdown(list(dict(df['Cyp2C9 genotypes'].value_counts())), label='Cyp2C9 genotypes')
-input_VKORC1_genotypes = gr.Radio(list(dict(df['VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T'].value_counts())), label='VKORC1 genotypes')
+input_Cyp2C9_genotypes = gr.Dropdown([], label='Cyp2C9 genotypes')  # Use empty list as placeholder
+input_VKORC1_genotypes = gr.Radio([], label='VKORC1 genotypes')  # Use empty list as placeholder
 input_model = gr.Dropdown(['Decision Tree Regression', 'Support Vector Regression', 'Random Forest Regression', 'Deep Learning'], label='Model Selection')
 
 # Output textbox to display predicted dose