Deploy PyCaret model baseline_dt_20250426_212853.pkl with fixed indentation

app.py

@@ -27,7 +27,7 @@ MODEL_FILE = "model.pkl" # Relative path within the Space
 
 # --- Processed Schema (for type checking later) ---
 # Use double braces to embed the schema dict correctly in the generated code
-APP_SCHEMA = {'PassengerId': {'type': 'numerical'}, 'Pclass': {'type': 'numerical'}, 'Name': {'type': 'numerical'}, 'Sex': {'type': 'numerical'}, 'Age': {'type': 'numerical'}, 'SibSp': {'type': 'numerical'}, 'Parch': {'type': 'numerical'}, 'Ticket': {'type': 'numerical'}, 'Fare': {'type': 'numerical'}, 'Cabin': {'type': 'numerical'}, 'Embarked': {'type': 'numerical'}, 'Survived': {'type': 'numerical'}}
+APP_SCHEMA = {'PassengerId': {'type': 'numerical'}, 'Pclass': {'type': 'numerical'}, 'Name': {'type': 'numerical'}, 'Sex': {'type': 'categorical', 'values': ['male', 'female']}, 'Age': {'type': 'numerical'}, 'SibSp': {'type': 'numerical'}, 'Parch': {'type': 'numerical'}, 'Ticket': {'type': 'numerical'}, 'Fare': {'type': 'numerical'}, 'Cabin': {'type': 'categorical', 'values': ['A', 'B', 'C']}, 'Embarked': {'type': 'categorical', 'values': ['S', 'C', 'Q']}}
 
 
 # --- Load Model ---
@@ -83,15 +83,14 @@ else:
         input_PassengerId = st.number_input(label='PassengerId', format='%f', key='input_PassengerId')
         input_Pclass = st.number_input(label='Pclass', format='%f', key='input_Pclass')
         input_Name = st.number_input(label='Name', format='%f', key='input_Name')
-        input_Sex = st.number_input(label='Sex', format='%f', key='input_Sex')
+        input_Sex = st.selectbox(label='Sex', options=['male', 'female'], key='input_Sex')
         input_Age = st.number_input(label='Age', format='%f', key='input_Age')
         input_SibSp = st.number_input(label='SibSp', format='%f', key='input_SibSp')
         input_Parch = st.number_input(label='Parch', format='%f', key='input_Parch')
         input_Ticket = st.number_input(label='Ticket', format='%f', key='input_Ticket')
         input_Fare = st.number_input(label='Fare', format='%f', key='input_Fare')
-        input_Cabin = st.number_input(label='Cabin', format='%f', key='input_Cabin')
-        input_Embarked = st.number_input(label='Embarked', format='%f', key='input_Embarked')
-        input_Survived = st.number_input(label='Survived', format='%f', key='input_Survived')
+        input_Cabin = st.selectbox(label='Cabin', options=['A', 'B', 'C'], key='input_Cabin')
+        input_Embarked = st.selectbox(label='Embarked', options=['S', 'C', 'Q'], key='input_Embarked')
         submitted = st.form_submit_button("📊 Get Prediction")
 
     # --- Prediction Logic & Output Section ---
@@ -100,7 +99,7 @@ else:
         try:
             # Create DataFrame from inputs using original feature names as keys
             # The values are automatically fetched by Streamlit using the keys assigned to widgets
-            input_data_dict = {'PassengerId': input_PassengerId, 'Pclass': input_Pclass, 'Name': input_Name, 'Sex': input_Sex, 'Age': input_Age, 'SibSp': input_SibSp, 'Parch': input_Parch, 'Ticket': input_Ticket, 'Fare': input_Fare, 'Cabin': input_Cabin, 'Embarked': input_Embarked, 'Survived': input_Survived} # Use triple braces for dict literal inside f-string
+            input_data_dict = {'PassengerId': input_PassengerId, 'Pclass': input_Pclass, 'Name': input_Name, 'Sex': input_Sex, 'Age': input_Age, 'SibSp': input_SibSp, 'Parch': input_Parch, 'Ticket': input_Ticket, 'Fare': input_Fare, 'Cabin': input_Cabin, 'Embarked': input_Embarked} # Use triple braces for dict literal inside f-string
             logger.info(f"Raw input data from form: {input_data_dict}")
             input_data = pd.DataFrame([input_data_dict])