Update pages/Lifecycle of Machine Learning.py

pages/Lifecycle of Machine Learning.py

@@ -1,147 +1,30 @@
-
-import streamlit as st
-import pandas as pd
-import numpy as np
-
-st.markdown(f"""
-<style>
-    /* Set the background image for the entire app */
-    .stApp {{
-        background-color:rgba(0,0,0, 0.5);
-        background-size: 1300px;
-        background-repeat: no-repeat;
-        background-attachment: fixed;
-        background-position: center;
-    }}
-    
-    </style>
-""", unsafe_allow_html=True)
-
 import streamlit as st
+from page1 import page1
+from page2 import page2
 
-# Navigation
-st.title("Life Cycle of ML")
-if 'page' not in st.session_state:
-    st.session_state['page'] = 'home'
+def background():
+    st.markdown(f"""
+    <style>
+        /* Set the background image for the entire app */
+        .stApp {{
+            background-color:rgba(96, 155, 124, 0.5);
+            background-size: 1300px;
+            background-repeat: no-repeat;
+            background-attachment: fixed;
+            background-position: center;
+        }}
+        
+        </style>
+    """, unsafe_allow_html=True)
 
-# Main Navigation
-if st.session_state['page'] == 'home':
-    st.subheader("Explore the Life Cycle Stages")
-    if st.button("Data Collection"):
-        st.session_state['page'] = 'data_collection'
+def main():
+    if 'page' not in st.session_state:
+        st.session_state.page = 'Page1'
 
-elif st.session_state['page'] == 'data_collection':
-    # Data Collection Page
-    st.title("Data Collection")
-    st.header("1. What is Data?")
-    st.write(
-        "Data refers to raw facts and figures that are collected, stored, and analyzed to derive insights. "
-        "It serves as the foundation for any machine learning model."
-    )
-    
-    st.header("2. Types of Data")
-    data_type = st.radio(
-        "Select a type of data to learn more:",
-        ("Structured", "Unstructured", "Semi-Structured")
-    )
-    
-    if data_type == "Structured":
-        st.subheader("Structured Data")
-        st.write(
-            "Structured data is highly organized and easily searchable within databases. "
-            "It includes rows and columns, such as in relational databases."
-        )
-        
-        st.write("Data Formats:")
-        format_selected = st.radio(
-            "Select a format to explore further:",
-            ("Excel", "CSV")
-        )
-        
-        if format_selected == "Excel":
-            # Excel Data Format Section
-            st.subheader("Excel Data Format")
-            st.write("*What is it?*")
-            st.write(
-                "Excel files are spreadsheets used to organize and analyze data in rows and columns. "
-                "They are widely used due to their user-friendly nature and support for various data types."
-            )
-            
-            st.write("*How to Read Excel Files?*")
-            st.code(
-                """
-import pandas as pd
-# Reading an Excel file
-df = pd.read_excel('file.xlsx')
-print(df.head())
-                """,
-                language="python"
-            )
-            
-            st.write("*Common Issues When Handling Excel Files*")
-            st.write(
-                """
-- Missing or corrupted files
-- Version incompatibilities
-- Incorrect file paths
-- Handling large Excel files
-                """
-            )
-            
-            st.write("*How to Overcome These Errors/Issues?*")
-            st.write(
-                """
-- Use proper error handling with try-except.
-- Convert Excel files to CSV for better compatibility.
-- Use libraries like openpyxl or xlrd for specific Excel versions.
-- Break large files into smaller chunks for processing.
-                """
-            )
-            
-            # Button to open Jupyter Notebook or PDF
-            if st.button("Open Excel Documentation"):
-                st.write("Download the [documentation notebook](path/to/excel_notebook.ipynb) or [PDF](path/to/excel_documentation.pdf).")
+    if st.session_state.page == 'Page1':
+        page1()
+    elif st.session_state.page == 'Page2':
+        page2()
 
-        elif format_selected == "CSV":
-            # CSV Data Format Section
-            st.subheader("CSV Data Format")
-            st.write("*What is it?*")
-            st.write(
-                "CSV (Comma-Separated Values) files store tabular data in plain text, where each line represents a record, "
-                "and fields are separated by commas."
-            )
-            
-            st.write("*How to Read CSV Files?*")
-            st.code(
-                """
-import pandas as pd
-# Reading a CSV file
-df = pd.read_csv('file.csv')
-print(df.head())
-                """,
-                language="python"
-            )
-            
-            st.write("*Common Issues When Handling CSV Files*")
-            st.write(
-                """
-- Encoding issues (e.g., UTF-8, ISO-8859-1)
-- Inconsistent delimiters
-- Missing or corrupted files
-- Large file sizes causing memory errors
-                """
-            )
-            
-            st.write("*How to Overcome These Errors/Issues?*")
-            st.write(
-                """
-- Specify the correct encoding when reading files using encoding='utf-8' or similar.
-- Use libraries like csv or pandas to handle different delimiters.
-- Employ error handling to catch and manage missing/corrupted files.
-- Use chunking to read large files in smaller parts: pd.read_csv('file.csv', chunksize=1000).
-                """
-            )
-            
-            # Button to open Jupyter Notebook or PDF
-            if st.button("Open CSV Documentation"):
-                st.write("Download the [documentation notebook](path/to/csv_notebook.ipynb) or [PDF](path/to/csv_documentation.pdf).")
+if __name__ == "__main__":
+    main()