Fix Custom Prediciton

app.py

@@ -88,15 +88,18 @@ st.write("Enter your own values for prediction:")
 selected_features = ["INITIAL_CALL_TYPE", "INITIAL_SEVERITY_LEVEL_CODE", "INCIDENT_DATETIME", "INCIDENT_CLOSE_DATETIME", "ZIPCODE", "POLICEPRECINCT", "FINAL_CALL_TYPE"]
 feature_inputs = {}
 for feature in selected_features:
-    if feature not in ["FINAL_CALL_TYPE"]: # ensure final not included
-        if feature in ['INITIAL_CALL_TYPE', 'DAY_OF_WEEK', 'POLICEPRECINCT', 'ZIPCODE']:  # categorical
-            options = list(sample_data[feature].unique())
-        else: # numerical
-            options = [str(x) for x in sample_data[feature].unique()]
-        feature_inputs[feature] = st.selectbox(feature, options=options)
+    if feature in ['INITIAL_CALL_TYPE', 'DAY_OF_WEEK', 'POLICEPRECINCT', 'ZIPCODE']:  # categorical
+        options = list(sample_data[feature].unique())
+    else: # numerical
+        options = [str(x) for x in sample_data[feature].unique()]
+    feature_inputs[feature] = st.selectbox(feature, options=options)
+
+custom_params = pd.Series(feature_inputs)
+
+print(custom_params);
 
 # encode user input
-user_data = encode(pd.DataFrame(feature_inputs, index=[0]))
+user_data = encode(custom_params)
 
 if st.button("Predict"):
     t1 = time.time() * 1000
@@ -104,8 +107,7 @@ if st.button("Predict"):
     t2 = time.time() * 1000
 
     st.write("Predicted Call Type:", predicted_label)
-    st.write(f"Prediction Time: {t2-t1:.2f}ms")
-
+    st.success(f"""Label successfully predicted in {t2-t1:.2f}ms!""")
 
 st.header("Results")
 st.markdown(

model.py

@@ -15,6 +15,8 @@ def encode(data):
 	params['INCIDENT_DATETIME'] = pd.to_datetime(params['INCIDENT_DATETIME'])
 	params['INCIDENT_CLOSE_DATETIME'] = pd.to_datetime(params['INCIDENT_CLOSE_DATETIME'])
 
+	print(type(params['INCIDENT_CLOSE_DATETIME']))
+
 	params['DAY_OF_WEEK'] = params['INCIDENT_DATETIME'].dayofweek
 	params['INCIDENT_HOUR'] = params['INCIDENT_DATETIME'].hour
 	params['INCIDENT_DURATION'] = (params['INCIDENT_CLOSE_DATETIME'] - params['INCIDENT_DATETIME']).total_seconds()
@@ -32,4 +34,4 @@ def predict(params):
 		return res[0]
 	except Exception as e:
 		print(e)
-		return "error"
+		return "error"