Update pages/3_Life cycle of ML.py

pages/3_Life cycle of ML.py

@@ -1,183 +1,64 @@
 import streamlit as st
-import webbrowser
 
-def display_lifecycle_step(step_name):
+# Initialize session state for navigation
+if "page" not in st.session_state:
+    st.session_state.page = "main"
+
+def navigate_to(page_name):
+    st.session_state.page = page_name
+
+if st.session_state.page == "main":
+    # Main Page Header
+    st.markdown(
+        """
+        <div style="background-color: #f0f0f5; padding: 10px; border-radius: 5px; text-align: center;">
+            <h2 style="color: #4a90e2; font-family: Arial, sans-serif;">ML Project Life Cycle</h2>
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    # Steps and Descriptions
     steps = {
-        "Problem Statement": "Objective of the project.",
-        "Data Collection": "Data is collected from various sources like APIs, databases, or web scraping atlast we've to go with manual collection.",
-        "Simple EDA": "Describing the quality of the data.",
-        "Data Pre-Processing": "It is a technique by which we can convert raw data into pre-procesed data --->1.Clean the data  2.Transform the data.",
-        "EDA": "Transforming insights into a clean dataset and providing proper visualizations.",
-        "Feature Engineering": "Creating and analyzing features and labels.",
-        "Model Training": "Training the machine about relationships between features and labels.",
-        "Testing": "Testing how efficiently the machine learned.",
-        "Deployment and Maintenance": "Deploying the machine to the client and ensuring maintenance for accurate results."
+        "1. Problem Statement": "Define the aim/goal of the ML model.",
+        "2. Data Collection": "Gather data from various sources like APIs and web scraping.",
+        "3. Simple EDA": "Explore data for missing values and outliers.",
+        "4. Data Pre-Processing": "Clean and transform the data.",
+        "5. EDA": "Gain further insights and visualize the data.",
+        "6. Feature Engineering": "Create new features for better model performance.",
+        "7. Training the Model": "Train the model using the prepared dataset.",
+        "8. Testing the Data": "Test and evaluate model performance.",
+        "9. Deployment": "Deploy the model for real-world usage.",
+        "10. Monitoring": "Monitor model performance and update as needed.",
     }
-    st.write(f"### {step_name}")
-    st.write(steps[step_name])
 
-# Sidebar with buttons for lifecycle steps
-st.sidebar.title("ML Lifecycle Steps")
-
-# Create buttons for each lifecycle step
-steps_list = [
-    "Problem Statement", "Data Collection", "Simple EDA", 
-    "Data Pre-Processing", "EDA", "Feature Engineering", 
-    "Model Training", "Testing", "Deployment and Maintenance"
-]
-
-selected_step = st.sidebar.radio(
-    "Choose a step in the ML lifecycle:",
-    steps_list,
-    index=0  
-)
-
-st.title("Machine Learning Lifecycle")
-
-display_lifecycle_step(selected_step)
-
-st.markdown("""
-    <style>
-        .stApp {
-            background-color: #f0f0f5;
-            font-family: 'Arial', sans-serif;
-        }
-        .stSidebar .sidebar-content {
-            background-color: #e3e4e8;
-            border-radius: 10px;
-            padding: 10px;
-        }
-        .stButton > button {
-            background-color: #008CBA;
+    # Step Selector
+    step = st.selectbox("Select a step in the ML life cycle:", list(steps.keys()))
+    st.write(f"**{step}**: {steps[step]}")
+
+    # Navigation Button
+    if step == "2. Data Collection":
+        if st.button("Learn More About Data Collection"):
+            navigate_to("data_collection")
+
+    # Custom Style
+    st.markdown(
+        """
+        <style>
+        .stButton button {
+            font-size: 16px;
             color: white;
-            border-radius: 50px;
-            font-size: 18px;
-            padding: 12px 24px;
+            background-color: #007bff;
+            border: none;
+            padding: 10px 20px;
+            border-radius: 5px;
+            cursor: pointer;
+            transition: background-color 0.3s ease;
         }
-        .stButton > button:hover {
-            background-color: #007B8C;
+        .stButton button:hover {
+            background-color: #0056b3;
         }
-    </style>
-""", unsafe_allow_html=True)
-
-def data_collection_page():
-    st.write("### What is Data?")
-    st.write("""
-    Data is a collection of facts, numbers, words, or observations that can be used to learn about something. 
-    It can be raw and unprocessed
-    It can be structured or unstructured and comes from various sources.
-    """)
-    
-    st.write("### Types of Data")
-    st.write("""
-    1. *Structured Data*: Organized data that follows a schema (e.g., rows and columns, sql).
-    2. *Unstructured Data*: Data that doesn't follow a predefined model (e.g., images, text, audio and video).
-    3. *Semi-Structured Data*: Data that has some organizational properties but isn't fully structured (e.g., JSON, XML, CSV,HTML).
-    """)
-
-    selected_data_type = st.radio("Choose Data Type", ["Structured Data", "Unstructured Data", "Semi-Structured Data"])
-
-    if selected_data_type == "Structured Data":
-        display_structured_data_info()
-
-def display_structured_data_info():
-    st.write("### Structured Data")
-    st.write("Structured data is data that is highly organized and stored in a fixed format, like tables, rows, and columns.")
-    
-    # Button for each structured data format (Excel, CSV, XML)
-    data_formats = st.radio("Choose a Data Format", ["Excel", "CSV", "XML"])
-    
-    if data_formats == "Excel":
-        display_excel_info()
-    elif data_formats == "CSV":
-        display_csv_info()
-    elif data_formats == "XML":
-        display_xml_info()
-
-# Function to display Excel-related information
-def display_excel_info():
-    st.write("### Excel Format")
-    st.write("""
-    *What it is*: Excel is a popular spreadsheet format commonly used for storing and analyzing structured data.
-    
-    *How to read these files*:
-    - Use pandas.read_excel() to read Excel files in Python.
-    
-    *Issues encountered when handling Excel files*:
-    - Large files can cause memory issues.
-    - Compatibility problems with different Excel versions.
-    
-    *How to overcome these errors*:
-    - Break large files into smaller chunks.
-    - Use libraries like openpyxl for handling newer Excel files and xlrd for older ones.
-    """)
-
-    # Button to open the Jupyter Notebook or PDF with coding examples
-    if st.button("Open Excel Code Example"):
-        open_code_example("excel")
-
-# Function to display CSV-related information
-def display_csv_info():
-    st.write("### CSV Format")
-    st.write("""
-    *What it is*: CSV (Comma Separated Values) is a text format for representing tabular data, where values are separated by commas.
-    
-    *How to read these files*:
-    - Use pandas.read_csv() to read CSV files in Python.
-    
-    *Issues encountered when handling CSV files*:
-    - Improper handling of special characters or delimiters.
-    - Missing or inconsistent data.
-    
-    *How to overcome these errors*:
-    - Specify delimiters using the delimiter parameter.
-    - Handle missing data by using fillna() or dropna() methods in pandas.
-    """)
-
-    # Button to open the Jupyter Notebook or PDF with coding examples
-    if st.button("Open CSV Code Example"):
-        open_code_example("csv")
-
-# Function to display XML-related information
-def display_xml_info():
-    st.write("### XML Format")
-    st.write("""
-    *What it is*: XML (Extensible Markup Language) is a flexible and structured format used to store data in a hierarchical manner.
-    
-    *How to read these files*:
-    - Use pandas.read_xml() to read XML files or xml.etree.ElementTree for more complex parsing.
-    
-    *Issues encountered when handling XML files*:
-    - Complex nested structures can be hard to parse.
-    - Compatibility issues between different XML schemas.
-    
-    *How to overcome these errors*:
-    - Use XPath or lxml for more advanced parsing.
-    - Handle encoding issues using the encoding parameter while reading the file.
-    """)
-
-    # Button to open the Jupyter Notebook or PDF with coding examples
-    if st.button("Open XML Code Example"):
-        open_code_example("xml")
-
-# Function to open a Jupyter Notebook or PDF for coding examples
-def open_code_example(data_format):
-    # Placeholder: Open a PDF/Jupyter notebook link for the data format
-    example_links = {
-        "excel": "https://yourlinktoexcelcode.com",
-        "csv": "https://yourlinktocsvcode.com",
-        "xml": "https://yourlinktoxmlcode.com",
-    }
-    
-    link = example_links.get(data_format)
-    if link:
-        webbrowser.open_new_tab(link)
-
-def main():
-    st.sidebar.title("ML Life Cycle Navigation")
-    
-    if st.sidebar.button("Data Collection"):
-        data_collection_page()
-
-if __name__ == "__main__":
-    main()
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )