update

app.py

@@ -23,15 +23,31 @@ features = features.drop(columns=["survived"])
 featureLabels = features.columns
 
 
-def titanic(input_list):
+def titanic(input1, input2, input3, input4, input5, input6, input7, input8):
+    input_list = []
+
+    # Add all inputs to the input_list
+    input_list.append(input1)
+    input_list.append(input2)
+    input_list.append(input3)
+    input_list.append(input4)
+    input_list.append(input5)
+    input_list.append(input6)
+    input_list.append(input7)
+    input_list.append(input8)
+
     # 'res' is a list of predictions returned as the label.
     res = model.predict(np.asarray(input_list).reshape(1, -1))
 
     # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
     # the first element.
+
+    intLabelToText = {0: "Died", 1: "Survived"}
+
     age = input_list[0]
     gender = input_list[1]
     survived = res[0]
+    survivedText = intLabelToText[survived]
 
     # Temp:
     age = 20
@@ -56,7 +72,7 @@ demo = gr.Interface(
     title="Titanic Survivor Predictive Analytics",
     description="Experiment with person features to predict which survivor it is.",
     allow_flagging="never",
-    inputs=[inputs],
+    inputs=inputs,
     outputs=gr.Image(type="pil"))
 
 demo.launch()