app.py

# import os
# import sys
# from random import randint
# import time
# import uuid
# import argparse
# import streamlit as st
# sys.path.append(os.path.abspath("../supv"))
# from matumizi.util import *
# from mcclf import *

import os
import sys
from random import randint
import time
import uuid
import argparse
import pandas as pd
import streamlit as st

# Add the directory containing the required modules to sys.path
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):
        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 trainModel(mlfpath):
#     model = MarkovChainClassifier(mlfpath)
#     model.train()

# def predictModel(mlfpath):
#     model = MarkovChainClassifier(mlfpath)
#     model.predict()

def trainModel(mlfpath):
    model = MarkovChainClassifier(mlfpath)
    model.train()
    return model


def predictModel(mlfpath, userID):
    model = MarkovChainClassifier(mlfpath)
    res = model.predict(userID)
    return res


# 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")
#     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.")

#     op = st.sidebar.selectbox("Select Operation", ["Generate Visit History", "Train Model", "Predict"])

#     if op == "Generate Visit History":
#         st.write("Enter the parameters to generate the visit history:")
#         numUsers = st.number_input("Number of users", min_value=1, max_value=1000, value=100, step=1)
#         convRate = st.number_input("Conversion Rate (in percentage)", min_value=0, max_value=100, value=10, step=1)
#         label = st.checkbox("Add Labels")
#         st.write("Click the button below to generate the visit history")
#         if st.button("Generate"):
#             genVisitHistory(numUsers, convRate, label)
            
#     elif op == "Train Model":
#         st.write("Train the model using the following parameters:")
#         mlfpath = st.text_input("MLF Path")
#         if st.button("Train"):
#             trainModel(mlfpath)

#     elif op == "Predict":
#         st.write("Predict using the trained model:")
#         mlfpath = st.text_input("MLF Path")
#         userID = st.text_input("User ID")
#         if st.button("Predict"):
#             result = predictModel(mlfpath, userID)
#             st.write("Prediction Result: ", result)

# def main():
#     st.title("Markov Chain Classifier")

#     # Add input fields for command line arguments
#     op = st.selectbox("Operation", ["gen", "train", "pred"])
#     numUsers = st.slider("Number of Users", 1, 1000, 100)
#     convRate = st.slider("Conversion Rate", 1, 100, 10)
#     label = st.checkbox("Add Label")
#     mlfpath = st.text_input("ML Config File Path", value="false")

#     # Call functions based on selected operation
#     if op == "gen":
#         st.button("Generate Visit History", on_click=lambda: genVisitHistory(numUsers, convRate, label))
#     elif op == "train":
#         st.button("Train Model", on_click=lambda: trainModel(mlfpath))
#     elif op == "pred":
#         st.button("Predict Model", on_click=lambda: predictModel(mlfpath))

# if __name__ == "__main__":
#     main()