app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

# Set page config
st.set_page_config(page_title="ML Models Comparison Dashboard", layout="wide")

# Title and description
st.title("Machine Learning Models Comparison Dashboard")
st.write("Compare performance metrics of different ML models on the CIFAR-10 dataset")

# Pre-computed metrics
results = {
    'Accuracy': {
        'KNN': 0.331,
        'Logistic Regression': 0.368,
        'Random Forest': 0.466,
        'Naive Bayes': 0.298,
        'K-Means': 0.109,
        'CNN': 0.694
    },
    'Precision': {
        'KNN': 0.342,
        'Logistic Regression': 0.387,
        'Random Forest': 0.409,
        'Naive Bayes': 0.295,
        'K-Means': 0.271,
        'CNN': 0.453
    },
    'Recall': {
        'KNN': 0.345,
        'Logistic Regression': 0.389,
        'Random Forest': 0.412,
        'Naive Bayes': 0.298,
        'K-Means': 0.275,
        'CNN': 0.456
    },
    'F1': {
        'KNN': 0.343,
        'Logistic Regression': 0.388,
        'Random Forest': 0.410,
        'Naive Bayes': 0.296,
        'K-Means': 0.273,
        'CNN': 0.454
    }
}

# Confusion matrices data
confusion_matrices = {
    'CNN': np.array([
        [672, 27, 78, 21, 21, 3, 3, 13, 116, 46],
        [20, 807, 3, 15, 8, 2, 7, 1, 22, 115],
        [54, 4, 593, 82, 144, 36, 28, 32, 18, 9],
        [17, 8, 73, 586, 100, 108, 38, 34, 12, 24],
        [19, 0, 53, 69, 720, 14, 15, 90, 15, 5],
        [5, 3, 89, 300, 58, 458, 6, 64, 9, 8],
        [3, 9, 55, 122, 118, 13, 653, 6, 7, 14],
        [17, 3, 30, 74, 70, 36, 0, 754, 1, 15],
        [41, 24, 11, 20, 9, 3, 6, 8, 844, 34],
        [20, 51, 4, 22, 9, 3, 6, 12, 25, 848]
    ]),
    'K-Means': np.array([
        [106, 109, 62, 41, 139, 81, 33, 185, 211, 33],
        [97, 184, 92, 141, 96, 159, 106, 25, 42, 58],
        [54, 69, 252, 160, 42, 46, 120, 109, 84, 64],
        [83, 120, 146, 154, 18, 60, 121, 87, 61, 150],
        [39, 49, 245, 219, 19, 51, 117, 73, 20, 168],
        [72, 185, 163, 103, 30, 35, 88, 132, 38, 154],
        [86, 94, 211, 212, 11, 28, 206, 30, 39, 83],
        [111, 88, 205, 131, 54, 135, 58, 57, 22, 139],
        [31, 167, 46, 28, 328, 195, 33, 91, 39, 42],
        [131, 81, 83, 123, 141, 331, 19, 18, 37, 36]
    ]),
    'KNN': np.array([
        [565, 12, 107, 20, 52, 6, 25, 4, 205, 4],
        [195, 244, 121, 61, 120, 28, 31, 4, 178, 18],
        [150, 7, 463, 58, 201, 24, 48, 10, 39, 0],
        [108, 10, 279, 243, 133, 92, 80, 17, 32, 6],
        [100, 6, 282, 54, 443, 23, 38, 11, 43, 0],
        [89, 4, 254, 178, 143, 208, 68, 10, 41, 5],
        [49, 1, 317, 102, 260, 24, 227, 2, 18, 0],
        [127, 16, 226, 76, 236, 40, 45, 192, 41, 1],
        [181, 31, 56, 45, 52, 11, 8, 4, 607, 5],
        [192, 81, 127, 80, 117, 27, 41, 14, 205, 116]
    ]),
    'Logistic Regression': np.array([
        [424, 51, 58, 50, 25, 41, 17, 56, 202, 76],
        [72, 426, 35, 48, 26, 36, 48, 47, 78, 184],
        [96, 34, 266, 90, 123, 94, 130, 84, 51, 32],
        [43, 52, 115, 235, 72, 194, 131, 56, 43, 59],
        [55, 35, 137, 80, 280, 97, 152, 112, 25, 27],
        [41, 41, 103, 202, 90, 300, 77, 64, 45, 37],
        [14, 47, 97, 147, 94, 94, 417, 42, 23, 25],
        [47, 47, 91, 70, 97, 91, 47, 397, 45, 68],
        [146, 85, 31, 37, 17, 41, 10, 18, 513, 102],
        [78, 180, 26, 36, 30, 29, 45, 59, 98, 419]
    ]),
    'Naive Bayes': np.array([
        [494, 20, 39, 10, 84, 34, 50, 9, 200, 60],
        [141, 166, 24, 31, 66, 72, 192, 19, 121, 168],
        [225, 24, 83, 15, 292, 48, 209, 21, 54, 29],
        [163, 36, 54, 76, 151, 129, 262, 26, 34, 69],
        [86, 8, 57, 26, 417, 38, 265, 22, 50, 31],
        [156, 17, 55, 51, 167, 264, 159, 36, 57, 38],
        [106, 2, 60, 18, 228, 46, 467, 15, 19, 39],
        [134, 24, 36, 41, 228, 94, 102, 131, 72, 138],
        [168, 41, 18, 17, 56, 83, 39, 8, 471, 99],
        [144, 67, 17, 20, 48, 32, 101, 23, 141, 407]
    ]),
    'Random Forest': np.array([
        [559, 36, 62, 21, 28, 20, 20, 30, 165, 59],
        [29, 544, 10, 38, 22, 34, 45, 35, 67, 176],
        [100, 36, 337, 79, 137, 69, 123, 54, 34, 31],
        [53, 46, 76, 282, 88, 173, 132, 62, 23, 65],
        [54, 21, 139, 60, 381, 47, 158, 91, 27, 22],
        [32, 27, 89, 167, 74, 400, 77, 74, 27, 33],
        [11, 33, 87, 78, 99, 53, 567, 31, 6, 35],
        [47, 44, 53, 58, 104, 83, 38, 451, 24, 98],
        [90, 83, 19, 33, 15, 39, 7, 22, 615, 77],
        [50, 176, 18, 38, 20, 24, 27, 43, 80, 524]
    ])
}

# Create tabs for different visualizations
tab1, tab2, tab3 = st.tabs(["Overall Performance", "Confusion Matrices", "Individual Metrics"])

with tab1:
    st.header("Overall Model Performance")
    
    # Create bar plot for overall accuracy
    fig, ax = plt.subplots(figsize=(12, 6))
    models = list(results['Accuracy'].keys())
    accuracies = list(results['Accuracy'].values())
    
    colors = ['purple', 'navy', 'teal', 'green', 'lime', 'yellow']
    bars = ax.bar(models, accuracies, color=colors)
    
    # Customize the plot
    ax.set_title('Overall Model Performance Comparison')
    ax.set_xlabel('Models')
    ax.set_ylabel('Accuracy')
    plt.xticks(rotation=45)
    
    # Add value labels on top of bars
    for bar in bars:
        height = bar.get_height()
        ax.text(bar.get_x() + bar.get_width()/2., height,
                f'{height:.3f}',
                ha='center', va='bottom')
    
    plt.tight_layout()
    st.pyplot(fig)

with tab2:
    st.header("Confusion Matrices")
    
    # Model selection for confusion matrix
    selected_model = st.selectbox("Select Model", list(confusion_matrices.keys()))
    
    # Create confusion matrix plot using seaborn
    fig, ax = plt.subplots(figsize=(10, 8))
    sns.heatmap(confusion_matrices[selected_model], 
                annot=True, 
                fmt='d',
                cmap='Blues',
                ax=ax)
    
    plt.title(f'Confusion Matrix - {selected_model}')
    plt.xlabel('Predicted')
    plt.ylabel('True')
    plt.tight_layout()
    
    st.pyplot(fig)

with tab3:
    st.header("Individual Metrics")
    
    col1, col2, col3 = st.columns(3)
    metrics = ['Precision', 'Recall', 'F1']
    
    for metric, col in zip(metrics, [col1, col2, col3]):
        with col:
            fig, ax = plt.subplots(figsize=(8, 6))
            models = list(results[metric].keys())
            values = list(results[metric].values())
            
            ax.bar(models, values)
            ax.set_title(f'Comparison of {metric}')
            plt.xticks(rotation=45, ha='right')
            ax.set_ylabel(metric)
            
            plt.tight_layout()
            st.pyplot(fig)

# Add metrics table to sidebar
st.sidebar.header("Metrics Table")
df_metrics = pd.DataFrame(results)
st.sidebar.dataframe(df_metrics.style.format("{:.3f}"))

# Add download button
@st.cache_data
def convert_df_to_csv():
    return df_metrics.to_csv()

csv = convert_df_to_csv()
st.sidebar.download_button(
    label="Download metrics as CSV",
    data=csv,
    file_name='model_metrics.csv',
    mime='text/csv',
)

# Footer
st.markdown("---")
st.markdown("Dashboard created for ML Models Comparison on CIFAR-10 dataset")