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| 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("2. **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") | |
| # Create an instance of MarkovChainClassifier with the ML config file | |
| model = MarkovChainClassifier("mcclf_cc.properties") | |
| # Get user input for userID | |
| user_id = st.text_input("Enter User ID") | |
| # Check if the "Predict" button was clicked | |
| if st.button("Predict"): | |
| # 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.") | |
| # 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("mcclf_cc.properties") | |
| # # # 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() |