Update app.py

app.py

@@ -22,6 +22,7 @@ import streamlit as st
 sys.path.append(os.path.abspath("../supv"))
 from matumizi.util import *
 from mcclf import *
+from markov_chain_classifier import MarkovChainClassifier
 
 def  genVisitHistory(numUsers, convRate, label):
     for i in range(numUsers):
@@ -111,13 +112,38 @@ def predictModel(mlfpath, userID):
     res = model.predict(userID)
     return res
 
-if op == "Predict":
-    st.write("Enter the parameters to make a prediction:")
-    userID = st.text_input("User ID")
-    st.write("Click the button below to make a prediction")
-    if st.button("Predict"):
-        prediction = predictModel(mlfpath, userID)
-        st.write("Prediction:", prediction)
+
+# Define MLF path and user ID
+mlfpath = "mcclf_cc.properties"
+userID = "56C96HWLR9ZO"
+
+# Load the Markov chain classifier model
+model = MarkovChainClassifier('cc.mod')
+
+# Perform prediction
+result = model.predict(userID)
+
+# Display the prediction result
+st.title("Conversion Prediction App")
+st.write("Welcome to the Conversion Prediction App. This app uses a Markov chain based classifier to predict whether a customer will convert or not based on their visit history.")
+st.write("Prediction Result for User ID: ", userID)
+st.write("Conversion: ", result)
+
+
+
+
+
+
+
+
+
+# if op == "Predict":
+#     st.write("Enter the parameters to make a prediction:")
+#     userID = st.text_input("User ID")
+#     st.write("Click the button below to make a prediction")
+#     if st.button("Predict"):
+#         prediction = predictModel(mlfpath, userID)
+#         st.write("Prediction:", prediction)
 
 # if __name__ == "__main__":
 #     st.title("Conversion Prediction App")