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import streamlit as st
# Add custom CSS for background image
st.markdown(
"""
<style>
body {
background-image: url('https://via.placeholder.com/1920x1080'); /* Replace with your image URL */
background-size: cover;
background-position: center;
background-attachment: fixed;
color: #FFFFFF; /* Set text color to contrast the background */
}
.shape-box {
background-color: rgba(255, 221, 193, 0.8);
padding: 10px;
border-radius: 5px;
text-align: center;
margin-bottom: 10px;
}
.shape-circle {
background-color: rgba(193, 225, 255, 0.8);
padding: 10px;
border-radius: 50%;
text-align: center;
margin-bottom: 10px;
}
.shape-diamond {
background-color: rgba(193, 255, 193, 0.8);
padding: 10px;
clip-path: polygon(50% 0%, 100% 50%, 50% 100%, 0% 50%);
text-align: center;
margin-bottom: 10px;
}
</style>
""",
unsafe_allow_html=True,
)
# Function to display lifecycle descriptions
def display_lifecycle_stage(stage_name, description):
st.subheader(stage_name)
st.write(description)
# Title
st.title("Enhanced Machine Learning Life Cycle")
# Markdown Diagram with Shapes and Colors
st.markdown(
"""
<div class="shape-box">Problem Statement</div>
<div class="shape-circle">Data Collection</div>
<div class="shape-box">Simple EDA</div>
<div class="shape-diamond">Data Preprocessing</div>
<div class="shape-box">EDA</div>
<div class="shape-circle">Feature Engineering</div>
<div class="shape-box">Training</div>
<div class="shape-diamond">Testing</div>
<div class="shape-box">Deploying</div>
<div class="shape-circle">Monitoring</div>
""",
unsafe_allow_html=True,
)
# Buttons for each stage
st.markdown("### Select a Lifecycle Stage to Learn More:")
col1, col2 = st.columns(2)
with col1:
if st.button("Problem Statement"):
display_lifecycle_stage(
"Problem Statement",
"Defining the problem and setting objectives for the machine learning project."
)
if st.button("Simple EDA"):
display_lifecycle_stage(
"Simple EDA",
"Performing initial exploratory data analysis to understand data distribution and trends."
)
if st.button("EDA"):
display_lifecycle_stage(
"EDA",
"Detailed exploratory data analysis for deeper insights into data patterns."
)
if st.button("Training"):
display_lifecycle_stage(
"Training",
"Fitting the model using the training dataset to learn patterns and relationships."
)
if st.button("Deploying"):
display_lifecycle_stage(
"Deploying",
"Deploying the trained model to production for real-world use."
)
with col2:
if st.button("Data Collection"):
display_lifecycle_stage(
"Data Collection",
"Gathering the data required for the machine learning project."
)
if st.button("Data Preprocessing"):
display_lifecycle_stage(
"Data Preprocessing",
"Cleaning and transforming the data to prepare it for analysis."
)
if st.button("Feature Engineering"):
display_lifecycle_stage(
"Feature Engineering",
"Creating new features or modifying existing ones to improve model performance."
)
if st.button("Testing"):
display_lifecycle_stage(
"Testing",
"Evaluating the model's performance using a separate testing dataset."
)
if st.button("Monitoring"):
display_lifecycle_stage(
"Monitoring",
"Monitoring the deployed model's performance and maintaining its accuracy."
)
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