pages/Life_cycle_of_ML.py

import streamlit as st

# Render the life cycle process with interactive buttons
st.title("Machine Learning Life Cycle")

# HTML content inside a string for correct SVG rendering
html_content = """
<svg width="800" height="650">
    <!-- Problem Statement -->
    <rect x="50" y="50" width="200" height="50" fill="#FFB6C1" stroke="#000" />
    <text x="75" y="80" fill="#000" font-size="14" text-anchor="middle">Problem Statement</text>
    
    <!-- Data Collection -->
    <circle cx="400" cy="75" r="50" fill="#ADD8E6" stroke="#000" />
    <text x="400" y="80" fill="#000" font-size="14" text-anchor="middle">Data Collection</text>
    
    <!-- Simple EDA -->
    <ellipse cx="700" cy="75" rx="100" ry="50" fill="#90EE90" stroke="#000" />
    <text x="700" y="80" fill="#000" font-size="14" text-anchor="middle">Simple EDA</text>
    
    <!-- Data Preprocessing -->
    <rect x="50" y="200" width="200" height="50" fill="#FFD700" stroke="#000" />
    <text x="150" y="230" fill="#000" font-size="14" text-anchor="middle">Data Preprocessing</text>
    
    <!-- EDA -->
    <circle cx="400" cy="225" r="50" fill="#FF7F50" stroke="#000" />
    <text x="400" y="230" fill="#000" font-size="14" text-anchor="middle">EDA</text>
    
    <!-- Feature Engineering -->
    <ellipse cx="700" cy="225" rx="100" ry="50" fill="#9370DB" stroke="#000" />
    <text x="700" y="230" fill="#000" font-size="14" text-anchor="middle">Feature Engineering</text>
    
    <!-- Training -->
    <rect x="50" y="350" width="200" height="50" fill="#FF6347" stroke="#000" />
    <text x="150" y="380" fill="#000" font-size="14" text-anchor="middle">Training</text>
    
    <!-- Testing -->
    <circle cx="400" cy="375" r="50" fill="#98FB98" stroke="#000" />
    <text x="400" y="380" fill="#000" font-size="14" text-anchor="middle">Testing</text>
    
    <!-- Deploying -->
    <ellipse cx="700" cy="375" rx="100" ry="50" fill="#F0E68C" stroke="#000" />
    <text x="700" y="380" fill="#000" font-size="14" text-anchor="middle">Deploying</text>
    
    <!-- Monitoring -->
    <rect x="350" y="500" width="200" height="50" fill="#B0E0E6" stroke="#000" />
    <text x="450" y="530" fill="#000" font-size="14" text-anchor="middle">Monitoring</text>
    
    <!-- Arrows -->
    <line x1="250" y1="75" x2="350" y2="75" stroke="#000" marker-end="url(#arrow)" />
    <line x1="450" y1="75" x2="600" y2="75" stroke="#000" marker-end="url(#arrow)" />
    <line x1="250" y1="225" x2="350" y2="225" stroke="#000" marker-end="url(#arrow)" />
    <line x1="450" y1="225" x2="600" y2="225" stroke="#000" marker-end="url(#arrow)" />
    <line x1="250" y1="375" x2="350" y2="375" stroke="#000" marker-end="url(#arrow)" />
    <line x1="450" y1="375" x2="600" y2="375" stroke="#000" marker-end="url(#arrow)" />
    <line x1="600" y1="375" x2="450" y2="500" stroke="#000" marker-end="url(#arrow)" />
    
    <!-- Define arrow marker -->
    <defs>
        <marker id="arrow" viewBox="0 0 10 10" refX="5" refY="5" markerWidth="4" markerHeight="4" orient="auto">
            <polygon points="0,0 10,5 0,10" fill="#000" />
        </marker>
    </defs>
</svg>
"""

# Render the SVG
st.markdown(html_content, unsafe_allow_html=True)

# Interactive buttons for stages
st.subheader("Stages in ML Life Cycle")
if st.button("Problem Statement"):
    st.write("Understanding the problem and setting objectives for the ML model.")
if st.button("Data Collection"):
    st.write("Gathering relevant data for model training.")
if st.button("Simple EDA"):
    st.write("Initial analysis to understand the dataset's basic properties.")
if st.button("Data Preprocessing"):
    st.write("Cleaning the data to ensure it's in a usable format.")
if st.button("EDA"):
    st.write("Deeper analysis to gain insights and find patterns in the data.")
if st.button("Feature Engineering"):
    st.write("Creating new features or modifying existing ones to improve model performance.")
if st.button("Training"):
    st.write("Training machine learning models using the processed data.")
if st.button("Testing"):
    st.write("Evaluating the trained model using a test set to assess its performance.")
if st.button("Deploying"):
    st.write("Deploying the model to a production environment.")
if st.button("Monitoring"):
    st.write("Continuously monitoring the model's performance in the production environment.")