Update app.py

app.py

@@ -1,135 +1,158 @@
+# # 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()
 
-import os
-import sys
-from random import randint
-import time
-import uuid
-import argparse
-import pandas as pd
-import streamlit as st
+# # def predictModel(mlfpath):
+# #     model = MarkovChainClassifier(mlfpath)
+# #     model.predict()
 
-# 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()
+#     return model
+
 
-# def predictModel(mlfpath):
+# def predictModel(mlfpath, userID):
 #     model = MarkovChainClassifier(mlfpath)
-#     model.predict()
+#     res = model.predict(userID)
+#     return res
+
 
-def trainModel(mlfpath):
-    model = MarkovChainClassifier(mlfpath)
-    model.train()
-    return model
+# # Define MLF path and user ID
+# mlfpath = "mcclf_cc.properties"
+# userID = "56C96HWLR9ZO"
 
+# # Load the Markov chain classifier model
+# model = MarkovChainClassifier('cc.mod')
 
-def predictModel(mlfpath, userID):
-    model = MarkovChainClassifier(mlfpath)
-    res = model.predict(userID)
-    return res
+# # 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)
 
-# Define MLF path and user ID
-mlfpath = "mcclf_cc.properties"
-userID = "56C96HWLR9ZO"
+import os
+import streamlit as st
+from mcclf import MarkovChainClassifier
 
-# Load the Markov chain classifier model
-model = MarkovChainClassifier('cc.mod')
+def app():
+    st.title("Hugging Face Prediction App")
+    st.subheader("Enter User ID:")
+    userID = st.text_input("User ID")
 
-# Perform prediction
-result = model.predict(userID)
+    # 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)
 
-# 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)
+        # Call the predict method on the loaded model
+        prediction = model.predict(userID)
 
+        # Display the prediction result
+        st.write("Prediction: ", prediction)
 
+if __name__ == "__main__":
+    app()
 
 
 
@@ -137,60 +160,60 @@ 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 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.")
+# # 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"])
+# #     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)
+# #     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()
+# #     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()