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)

import os
import streamlit as st
from mcclf import MarkovChainClassifier

def app():
    st.title("Hugging Face Prediction App")
    st.subheader("Enter User ID:")
    userID = st.text_input("User ID")

    # Add any other input fields or widgets for user interaction
# Add a "Predict" button
    if st.button("Predict"):
        # Load the Markov chain classifier model from the model folder
        model_path = os.path.join("model", "cc.mod")
        model = MarkovChainClassifier(model_path)

        # Call the predict method on the loaded model
        prediction = model.predict(userID)

        # Display the prediction result
        st.write("Prediction: ", prediction)

if __name__ == "__main__":
    app()







# # 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()