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 *
import streamlit as st


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)

        st.write(",".join(userSess))


def main():
    st.set_page_config(page_title="Customer Conversion Prediction", page_icon=":guardsman:", layout="wide")
    st.title("Customer Conversion Prediction")

    # # 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", "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("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")
        # 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("cc.mod")

            # # Load the model from cc.mod
            # model = MarkovChainClassifier.load_model("cc.mod")

            # Get user input for userID
            user_id = st.text_input("Enter User ID")
            
            # Check if the "Predict" button was clicked
            if st.button("Predict"):
                # Load the saved model
                model.load_model("cc.mod")
                
                # Call the predict method of the MarkovChainClassifier instance
                pred = model.predict()
                if pred == 'T':
                    st.write(f"UserID: {user_id}, Prediction: Visitor is likely to convert into a customer.")
                else:
                    st.write(f"UserID: {user_id}, Prediction: Visitor is unlikely to convert into a customer.")

if __name__ == "__main__":
    main()