Update app.py

app.py

@@ -14,10 +14,17 @@ df = pd.read_csv('processed_data.csv')  # Replace with the correct path to your
 with open('label_encoder.pkl', 'rb') as f:
     label_encoder = pickle.load(f)
     
+categorical_features = [0, 1, 9, 10]  # Update if column positions change
+
 ct = ColumnTransformer(
-    transformers=[('encoder', OneHotEncoder(sparse_output=False, drop="first"),[0, 1, 9, 10])],
+    transformers=[('encoder', OneHotEncoder(sparse_output=False, drop="first"), categorical_features)],
     remainder="passthrough"
 )
+
+# Fit it using your training data
+ct.fit(df[['Gender', 'Race (Reported)', 'Cyp2C9 genotypes', 'VKORC1 genotype: -1639 G>A (3673); chr16:31015190; rs9923231; C/T']])
+
+
 # Assuming 'ct' is your ColumnTransformer (replace this with the actual loading code for your preprocessor)
 # Make sure that 'ct' is properly loaded, or use the same transformation logic here.