Update app.py

app.py

@@ -1,219 +0,0 @@
-# # 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()