Update app.py

app.py

@@ -9,6 +9,7 @@ from matumizi.util import *
 from mcclf import *
 import streamlit as st
 
+
 def  genVisitHistory(numUsers, convRate, label):
     for i in range(numUsers):
         userID = genID(12)
@@ -80,20 +81,26 @@ def  genVisitHistory(numUsers, convRate, label):
 
 
 def main():
-    st.set_page_config(page_title="Markov Chain Classifier", page_icon=":guardsman:", layout="wide")
+    st.set_page_config(page_title="Customer Conversion Prediction", page_icon=":guardsman:", layout="wide")
     st.title("Markov Chain Classifier")
 
+    # # Add sidebar
+    # st.sidebar.title("Navigation")
+    # app_mode = st.sidebar.selectbox("Choose the app mode",
+    #     ["Instructions", "Generate User Visit History", "Train Model", "Predict Conversion"])
+
     # Add sidebar
     st.sidebar.title("Navigation")
-    app_mode = st.sidebar.selectbox("Choose the app mode",
-        ["Instructions", "Generate User Visit History", "Train Model", "Predict Conversion"])
+    app_mode = st.sidebar.selectbox("Choose the App Mode",
+        ["Instructions", "Generate User Visit History", "Predict Conversion"])
 
     if app_mode == "Instructions":
         st.write("Welcome to the Markov Chain Classifier app!")
+        # st.write("This app allows you to generate user visit history, train a Markov Chain Classifier model, and predict conversion.")
         st.write("This app allows you to generate user visit history, train a Markov Chain Classifier model, and predict conversion.")
         st.write("To get started, use the sidebar to navigate to the desired functionality.")
         st.write("1. **Generate User Visit History**: Select the number of users and conversion rate, and click the 'Generate' button to generate user visit history.")
-        st.write("2. **Train Model**: Upload an ML config file using the file uploader, and click the 'Train' button to train the Markov Chain Classifier model.")
+        # st.write("2. **Train Model**: Upload an ML config file using the file uploader, and click the 'Train' button to train the Markov Chain Classifier model.")
         st.write("3. **Predict Conversion**: Upload an ML config file using the file uploader, and click the 'Predict' button to make predictions with the trained model.")
 
     elif app_mode == "Generate User Visit History":
@@ -104,20 +111,31 @@ def main():
         if st.button("Generate"):
             genVisitHistory(num_users, conv_rate, add_label)
 
-    elif app_mode == "Train Model":
-        st.subheader("Train Model")
-        mlf_path = st.file_uploader("Upload ML config file")
-        if st.button("Train"):
-            if mlf_path is not None:
-                model = MarkovChainClassifier(mlf_path)
-                model.train()
+    # elif app_mode == "Train Model":
+    #     st.subheader("Train Model")
+    #     mlf_path = st.file_uploader("Upload ML config file")
+    #     if st.button("Train"):
+    #         if mlf_path is not None:
+    #             model = MarkovChainClassifier(mlf_path)
+    #             model.train()
 
     elif app_mode == "Predict Conversion":
         st.subheader("Predict Conversion")
-        mlf_path = st.file_uploader("Upload ML config file")
-        if st.button("Predict"):
-            if mlf_path is not None:
-                model = MarkovChainClassifier(mlf_path)
+        # Upload ML config file using Streamlit's file_uploader function
+        mlf_file = st.file_uploader("Upload ML config file", type=["properties"])
+
+        # Check if ML config file was uploaded
+        if mlf_file is not None:
+            # Save the uploaded file to a local file
+            with open("mcclf_cc.properties", "wb") as f:
+                f.write(mlf_file.read())
+
+            # Create an instance of MarkovChainClassifier with the uploaded ML config file
+            model = MarkovChainClassifier("mcclf_cc.properties")
+
+            # Check if the "Predict" button was clicked
+            if st.button("Predict"):
+            # Call the predict method of the MarkovChainClassifier instance
                 model.predict()
 
 if __name__ == "__main__":