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import streamlit as st
import matplotlib.pyplot as plt
from src.clustering import load_data, extract_features, fit_kmeans, calculate_wcss
from src.utils import plot_cluster_counts, visualize_clusters
from typing import List
# Page configuration
st.set_page_config(
page_title="Customer Segmentation",
layout="wide",
initial_sidebar_state="expanded"
)
# Sidebar styling via markdown (optional)
st.markdown(
"""
<style>
.reportview-container { padding: 2rem; }
.sidebar .sidebar-content { background-color: #ffffff; padding: 1.5rem; border-radius: 8px; }
</style>
""",
unsafe_allow_html=True
)
# App title
st.title("📊 Customer Segmentation")
# File upload
uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=["csv"])
if not uploaded_file:
st.sidebar.info("Please upload a CSV file to proceed.")
st.stop()
# Load data
data = load_data(uploaded_file)
st.subheader("Dataset Preview")
st.dataframe(data.head())
# Select features
feature_options: List[str] = list(data.columns)
selected_features = st.sidebar.multiselect(
"Select two features for clustering:",
options=feature_options,
default=feature_options[3:5]
)
if len(selected_features) != 2:
st.sidebar.error("Please select exactly two features.")
st.stop()
# Clustering settings
n_clusters = st.sidebar.slider(
"Number of clusters", min_value=2, max_value=10, value=5
)
# Run clustering
if st.sidebar.button("Run Clustering"):
# Extract features
X = extract_features(data, selected_features)
# Compute elbow
wcss = calculate_wcss(X, max_clusters=10)
fig_elbow, ax = plt.subplots(figsize=(8, 4))
ax.plot(range(1, len(wcss) + 1), wcss, marker='o')
ax.set_title("Elbow Method: WCSS vs. Number of Clusters", fontsize=14)
ax.set_xlabel("Number of Clusters", fontsize=12)
ax.set_ylabel("WCSS", fontsize=12)
ax.grid(True, linestyle="--", alpha=0.6)
st.subheader("Elbow Method")
st.pyplot(fig_elbow)
# Fit KMeans
labels, centers = fit_kmeans(X, n_clusters)
data['Cluster'] = labels
# Cluster visualization
st.subheader("Cluster Plot")
fig_clusters = visualize_clusters(X, labels, centers)
st.pyplot(fig_clusters)
# Cluster counts
st.subheader("Cluster Size Distribution")
fig_counts = plot_cluster_counts(labels)
st.pyplot(fig_counts)
st.success("Clustering completed!") |