src/streamlit_app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris, load_wine, load_breast_cancer
from sklearn.preprocessing import StandardScaler
import plotly.graph_objects as go

# Set page config
st.set_page_config(
    page_title="PCA Visualization App",
    page_icon="📊",
    layout="wide"
)

# Title and description
st.title("📊 PCA Visualization Dashboard")
st.markdown("""
This app demonstrates Principal Component Analysis (PCA) visualization using different datasets.
Use the controls in the sidebar to customize your analysis.
""")

# Sidebar controls
st.sidebar.header("🎛️ Controls")

# Dataset selection
dataset_name = st.sidebar.selectbox(
    "Select Dataset",
    ("Iris", "Wine", "Breast Cancer")
)

# Number of components
n_components = st.sidebar.slider(
    "Number of Components",
    min_value=2,
    max_value=3,
    value=2,
    help="Select 2D or 3D visualization"
)

# Load selected dataset
@st.cache_data
def load_data(dataset_name):
    if dataset_name == "Iris":
        data = load_iris()
    elif dataset_name == "Wine":
        data = load_wine()
    else:
        data = load_breast_cancer()
    
    df = pd.DataFrame(data.data, columns=data.feature_names)
    df['target'] = data.target
    df['target_names'] = [data.target_names[i] for i in data.target]
    return df, data.target_names

# Load data
df, target_names = load_data(dataset_name)

# Display dataset info
st.subheader(f"Dataset: {dataset_name}")
st.write(f"Shape: {df.shape[0]} rows, {df.shape[1]-2} features")
st.write(f"Target classes: {', '.join(target_names)}")

# Show raw data toggle
if st.checkbox("Show raw data"):
    st.write(df.head())

# Prepare data for PCA
X = df.drop(['target', 'target_names'], axis=1)
y = df['target']
target_names = df['target_names'].unique()

# Standardize the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Apply PCA
pca = PCA(n_components=n_components)
X_pca = pca.fit_transform(X_scaled)

# Create DataFrame with PCA results
pca_columns = [f"PC{i+1}" for i in range(n_components)]
df_pca = pd.DataFrame(X_pca, columns=pca_columns)
df_pca['target'] = y
df_pca['target_names'] = df['target_names']

# Display PCA info
st.subheader("PCA Analysis")
col1, col2 = st.columns(2)
with col1:
    st.write(f"Explained Variance Ratio: {[f'{val:.2%}' for val in pca.explained_variance_ratio_]}")
    st.write(f"Total Variance Explained: {pca.explained_variance_ratio_.sum():.2%}")

with col2:
    st.write(f"Cumulative Variance Explained: {[f'{val:.2%}' for val in pca.explained_variance_ratio_.cumsum()]}")

# Create visualization
if n_components == 2:
    fig = px.scatter(
        df_pca,
        x='PC1',
        y='PC2',
        color='target_names',
        title=f"PCA Visualization - {dataset_name} Dataset (2D)",
        labels={
            'PC1': f'PC1 ({pca.explained_variance_ratio_[0]:.1%} variance)',
            'PC2': f'PC2 ({pca.explained_variance_ratio_[1]:.1%} variance)'
        },
        hover_data=['target_names']
    )
    
    fig.update_traces(marker=dict(size=8, opacity=0.8))
    fig.update_layout(
        width=800,
        height=600,
        legend_title_text='Classes'
    )
    
else:  # 3D visualization
    fig = px.scatter_3d(
        df_pca,
        x='PC1',
        y='PC2',
        z='PC3',
        color='target_names',
        title=f"PCA Visualization - {dataset_name} Dataset (3D)",
        labels={
            'PC1': f'PC1 ({pca.explained_variance_ratio_[0]:.1%} variance)',
            'PC2': f'PC2 ({pca.explained_variance_ratio_[1]:.1%} variance)',
            'PC3': f'PC3 ({pca.explained_variance_ratio_[2]:.1%} variance)'
        },
        hover_data=['target_names']
    )
    
    fig.update_traces(marker=dict(size=5, opacity=0.8))
    fig.update_layout(
        width=800,
        height=600,
        legend_title_text='Classes'
    )

# Display plot
st.plotly_chart(fig, use_container_width=True)

# Feature contribution to principal components
st.subheader("Feature Contributions to Principal Components")
feature_importance = pd.DataFrame(
    pca.components_.T,
    columns=pca_columns,
    index=X.columns
)

# Display feature importance as a heatmap
fig_importance = px.imshow(
    feature_importance.T,
    labels=dict(x="Features", y="Principal Components", color="Contribution"),
    color_continuous_scale='RdBu_r',
    aspect="auto",
    title="Feature Contributions to Principal Components"
)

fig_importance.update_layout(
    width=800,
    height=400
)

st.plotly_chart(fig_importance, use_container_width=True)

# Show top contributing features
st.subheader("Top Contributing Features")
for i in range(n_components):
    st.write(f"**PC{i+1}**:")
    pc_features = feature_importance[f'PC{i+1}'].abs().sort_values(ascending=False)
    top_features = pc_features.head(5)
    for feature, value in top_features.items():
        st.write(f"- {feature}: {value:.3f}")
    st.write("")

# Information about PCA
with st.expander("ℹ️ About PCA"):
    st.markdown("""
    **Principal Component Analysis (PCA)** is a dimensionality reduction technique that transforms 
    high-dimensional data into a lower-dimensional space while preserving as much variance as possible.
    
    **Key Concepts:**
    - **Principal Components**: New axes that capture maximum variance in the data
    - **Explained Variance Ratio**: Proportion of total variance explained by each component
    - **Standardization**: Important preprocessing step to ensure all features contribute equally
    
    **Benefits:**
    - Reduces computational complexity
    - Removes multicollinearity
    - Helps with data visualization
    - Can improve model performance by reducing noise
    
    **Applications:**
    - Data visualization
    - Feature extraction
    - Noise reduction
    - Data compression
    """)