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import streamlit as st
# Custom CSS for styled boxes and centering buttons
st.markdown("""
<style>
.step-box {
border: 2px solid #00FFFF;
border-radius: 10px;
padding: 20px;
margin: 10px 0;
background-color: #e0f7fa;
text-align: center;
cursor: pointer;
transition: background-color 0.3s, transform 0.2s;
font-size: 22px;
font-weight: bold;
box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
}
.step-box:hover {
background-color: #b2ebf2;
transform: scale(1.05);
}
.button-back {
display: inline-block;
margin-top: 20px;
padding: 10px 15px;
font-size: 16px;
color: white;
background-color: #00bcd4;
border: none;
border-radius: 5px;
cursor: pointer;
text-decoration: none;
transition: background-color 0.3s;
}
.button-back:hover {
background-color: #0097a7;
}
.main-title {
color: #00796b;
font-size: 30px;
font-weight: bold;
text-align: center;
margin-bottom: 30px;
}
.intro-text {
font-size: 18px;
text-align: center;
color: #004d40;
margin-bottom: 40px;
}
/* Center buttons container */
.buttons-container {
display: flex;
justify-content: center;
flex-wrap: wrap;
margin-top: 20px;
}
.button {
margin: 10px;
padding: 15px 25px;
font-size: 16px;
font-weight: bold;
background-color: #00bcd4; /* Light blue button color */
border: none;
border-radius: 5px;
color: white;
cursor: pointer;
transition: background-color 0.3s;
}
.button:hover {
background-color: #0097a7;
}
</style>
""", unsafe_allow_html=True)
# Initialize session state for navigation
if "selected_step" not in st.session_state:
st.session_state["selected_step"] = "Main Page"
# Steps of the ML lifecycle
steps = [
"Problem Statement",
"Collect the Data (Data Collection)",
"Simple EDA (Exploratory Data Analysis)",
"Data Processing",
"Main EDA",
"Feature Engineering",
"Training the Model",
"Testing the Model",
"Deployment",
"Monitoring",
]
# Sidebar navigation
st.sidebar.title("Return")
if st.session_state["selected_step"] != "Main Page":
st.sidebar.button("🔙 Back to Main Page", on_click=lambda: st.session_state.update({"selected_step": "Main Page"}))
# Main Page layout
if st.session_state["selected_step"] == "Main Page":
st.markdown('<h1 class="main-title"> Lifecycle of a Machine Learning Projects 🌟</h1>', unsafe_allow_html=True)
st.markdown('<p class="intro-text">Explore the steps that are involved in an ML project\'s lifecycle by clicking on the boxes below</p>', unsafe_allow_html=True)
# Centered buttons container
st.markdown('<div class="buttons-container">', unsafe_allow_html=True)
for i, step in enumerate(steps, start=1):
if st.button(f"{i}. {step}", key=f"button_{i}", help=f"Click", use_container_width=True):
st.session_state["selected_step"] = step
st.markdown('</div>', unsafe_allow_html=True)
# Detailed Summary Pages
elif st.session_state["selected_step"] in steps:
selected_step = st.session_state["selected_step"]
st.title(f"🔎 Step: {selected_step}")
summaries = {
"Problem Statement": "📍 Clearly state the problem you're solving with ML. Example: Predict house prices based on size, location, and number of rooms. 🤔",
"Collect the Data (Data Collection)": "📂 Gather data from various sources such as databases, websites, or APIs.The Data can be of different forms like xslx,json,xml. Example: Use housing price data or collect information from a real estate site.📥",
"Simple EDA (Exploratory Data Analysis)": "🔍 Analyze the data to find trends, outliers, and missing values. Example: Create histograms and boxplots to see how the data is distributed. 🛠️",
"Data Processing": "🧹 Clean the data by fixing missing values, normalizing, and transforming it. Example: Fill missing values with the average or scale the features. ✨",
"Main EDA": "🔎 Do a deeper analysis using advanced techniques like correlation and pair plots. Example: Check how features like square footage and price are related. 🔬",
"Feature Engineering": "⚙️ Create new features or change existing ones to improve the model's performance. Example: Image Augmentation (Resizing, Shifting, Scaling) 🧩",
"Training the Model": "🧠 Split the data into training and testing sets, then train a machine learning model. 🏋️♀️",
"Testing the Model": "🧪 Check how well the model performs on new, unseen data. Use accuracy, precision, recall, or RMSE to evaluate a model's performance. 📊",
"Deployment": "🚀 Put the model into production so people can use it. Host the model on the cloud and create an API for users to get predictions. 🌐",
"Monitoring": "📈 Track the model's performance over time and retrain if needed.📡",
}
st.write(summaries.get(selected_step, "No summary available for this step."))
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