Update app.py

app.py

@@ -0,0 +1,123 @@
+import os
+import sys
+from random import randint
+import time
+import uuid
+import argparse
+sys.path.append(os.path.abspath("../supv"))
+from matumizi.util import *
+from mcclf import *
+
+def  genVisitHistory(numUsers, convRate, label):
+    for i in range(numUsers):
+        userID = genID(12)
+        userSess = []
+        userSess.append(userID)
+
+        conv = randint(0, 100)
+        if (conv < convRate):
+            #converted
+            if (label):
+                if (randint(0,100) < 90):
+                    userSess.append("T")
+                else:
+                    userSess.append("F")
+
+
+            numSession = randint(2, 20)
+            for j in range(numSession):
+                sess = randint(0, 100)
+                if (sess <= 15):
+                    elapsed = "H"
+                elif (sess > 15 and sess <= 40):
+                    elapsed = "M"
+                else:
+                    elapsed = "L"
+
+                sess = randint(0, 100)
+                if (sess <= 15):
+                    duration = "L"
+                elif (sess > 15 and sess <= 40):
+                    duration = "M"
+                else:
+                    duration = "H"
+
+                sessSummary = elapsed + duration
+                userSess.append(sessSummary)
+
+
+        else:
+            #not converted
+            if (label):
+                if (randint(0,100) < 90):
+                    userSess.append("F")
+                else:
+                    userSess.append("T")
+
+            numSession = randint(2, 12)
+            for j in range(numSession):
+                sess = randint(0, 100)
+                if (sess <= 20):
+                    elapsed = "L"
+                elif (sess > 20 and sess <= 45):
+                    elapsed = "M"
+                else:
+                    elapsed = "H"
+
+                sess = randint(0, 100)
+                if (sess <= 20):
+                    duration = "H"
+                elif (sess > 20 and sess <= 45):
+                    duration = "M"
+                else:
+                    duration = "L"
+
+                sessSummary = elapsed + duration
+                userSess.append(sessSummary)
+
+        print(",".join(userSess))
+
+
+def main():
+    st.set_page_config(page_title="Markov Chain Classifier", 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"])
+
+   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("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("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":
+        st.subheader("Generate User Visit History")
+        num_users = st.number_input("Number of users", min_value=1, max_value=10000, value=100, step=1)
+        conv_rate = st.slider("Conversion rate", min_value=0, max_value=100, value=10, step=1)
+        add_label = st.checkbox("Add label", value=False)
+        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 == "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)
+                model.predict()
+
+if __name__ == "__main__":
+    main()