Update app.py

app.py

@@ -103,6 +103,11 @@ def main():
         # 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.")
 
+        # Description of MarkovChainClassifier
+        mcclf_description = "The MarkovChainClassifier is a machine learning classifier that utilizes the concept of Markov chains for prediction. Markov chains are mathematical models that represent a system where the future state of the system depends only on its current state, and not on the previous states. The MarkovChainClassifier uses this concept to make predictions by modeling the transition probabilities between different states or categories in the input data. It captures the probabilistic relationships between variables and uses them to classify new data points into one or more predefined categories. The MarkovChainClassifier can be useful in scenarios where the data has a sequential or time-dependent structure, and the relationships between variables can be modeled as Markov chains. It can be applied to various tasks, such as text classification, speech recognition, recommendation systems, and financial forecasting."
+        # Display the description in Streamlit app
+        st.write(mcclf_description)
+        
     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)
@@ -171,9 +176,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-
-# Description of MarkovChainClassifier
-mcclf_description = "The MarkovChainClassifier is a machine learning classifier that utilizes the concept of Markov chains for prediction. Markov chains are mathematical models that represent a system where the future state of the system depends only on its current state, and not on the previous states. The MarkovChainClassifier uses this concept to make predictions by modeling the transition probabilities between different states or categories in the input data. It captures the probabilistic relationships between variables and uses them to classify new data points into one or more predefined categories. The MarkovChainClassifier can be useful in scenarios where the data has a sequential or time-dependent structure, and the relationships between variables can be modeled as Markov chains. It can be applied to various tasks, such as text classification, speech recognition, recommendation systems, and financial forecasting."
-
-# Display the description in Streamlit app
-st.write(mcclf_description)