Update app.py

app.py

@@ -1,8 +1,9 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import plotly.graph_objects as go
 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")
@@ -11,15 +12,15 @@ st.set_page_config(page_title="ML Models Comparison Dashboard", layout="wide")
 st.title("Machine Learning Models Comparison Dashboard")
 st.write("Compare performance metrics of different ML models on the CIFAR-10 dataset")
 
-# Pre-computed metrics (replace these with your actual results)
+# Pre-computed metrics
 results = {
     'Accuracy': {
-        'KNN': 0.345,  # Replace with your actual values
-        'Logistic Regression': 0.389,
-        'Random Forest': 0.412,
+        'KNN': 0.331,
+        'Logistic Regression': 0.368,
+        'Random Forest': 0.466,
         'Naive Bayes': 0.298,
-        'K-Means': 0.275,
-        'CNN': 0.456
+        'K-Means': 0.109,
+        'CNN': 0.694
     },
     'Precision': {
         'KNN': 0.342,
@@ -47,84 +48,163 @@ results = {
     }
 }
 
-# Pre-computed confusion matrices (replace these with your actual confusion matrices)
+# Confusion matrices data
 confusion_matrices = {
-    'KNN': np.random.randint(0, 100, (10, 10)),  # Replace with actual confusion matrices
-    'Logistic Regression': np.random.randint(0, 100, (10, 10)),
-    'Random Forest': np.random.randint(0, 100, (10, 10)),
-    'Naive Bayes': np.random.randint(0, 100, (10, 10)),
-    'K-Means': np.random.randint(0, 100, (10, 10)),
-    'CNN': np.random.randint(0, 100, (10, 10))
+    '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(["Metrics Comparison", "Confusion Matrices", "Radar Plot"])
+tab1, tab2, tab3 = st.tabs(["Overall Performance", "Confusion Matrices", "Individual Metrics"])
 
 with tab1:
-    st.header("Performance Metrics Comparison")
+    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)
     
-    # Convert results to DataFrame for plotting
-    df_metrics = pd.DataFrame(results)
-    df_metrics.index.name = 'Model'
-    df_metrics = df_metrics.reset_index()
+    # Customize the plot
+    ax.set_title('Overall Model Performance Comparison')
+    ax.set_xlabel('Models')
+    ax.set_ylabel('Accuracy')
+    plt.xticks(rotation=45)
     
-    # Create bar plot using plotly
-    fig = px.bar(df_metrics.melt(id_vars=['Model'], 
-                               var_name='Metric', 
-                               value_name='Score'),
-                x='Model', y='Score', color='Metric', barmode='group',
-                title='Model Performance Comparison')
-    fig.update_layout(xaxis_tickangle=-45)
-    st.plotly_chart(fig)
+    # 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')
     
-    # Display metrics table
-    st.subheader("Metrics Table")
-    st.dataframe(df_metrics.set_index('Model').style.format("{:.3f}"))
+    plt.tight_layout()
+    st.pyplot(fig)
 
 with tab2:
     st.header("Confusion Matrices")
     
-    # Select model for confusion matrix
+    # Model selection for confusion matrix
     selected_model = st.selectbox("Select Model", list(confusion_matrices.keys()))
     
-    # Plot confusion matrix using plotly
-    fig = px.imshow(confusion_matrices[selected_model],
-                   labels=dict(x="Predicted", y="True"),
-                   title=f"Confusion Matrix - {selected_model}")
-    st.plotly_chart(fig)
-
-with tab3:
-    st.header("Radar Plot Comparison")
+    # 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)
     
-    # Create radar plot using plotly
-    fig = go.Figure()
-    metrics = list(results.keys())
-    models = list(results['Accuracy'].keys())
+    plt.title(f'Confusion Matrix - {selected_model}')
+    plt.xlabel('Predicted')
+    plt.ylabel('True')
+    plt.tight_layout()
     
-    for model in models:
-        values = [results[metric][model] for metric in metrics]
-        values.append(values[0])  # Complete the circle
-        
-        fig.add_trace(go.Scatterpolar(
-            r=values,
-            theta=metrics + [metrics[0]],
-            name=model
-        ))
+    st.pyplot(fig)
+
+with tab3:
+    st.header("Individual Metrics")
     
-    fig.update_layout(
-        polar=dict(radialaxis=dict(visible=True, range=[0, 1])),
-        showlegend=True,
-        title="Model Comparison - All Metrics"
-    )
+    col1, col2, col3 = st.columns(3)
+    metrics = ['Precision', 'Recall', 'F1']
     
-    st.plotly_chart(fig)
+    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 for metrics
+# Add download button
 @st.cache_data
 def convert_df_to_csv():
-    return df_metrics.to_csv(index=False)
+    return df_metrics.to_csv()
 
-st.sidebar.header("Download Data")
 csv = convert_df_to_csv()
 st.sidebar.download_button(
     label="Download metrics as CSV",
@@ -133,15 +213,6 @@ st.sidebar.download_button(
     mime='text/csv',
 )
 
-# Add explanatory text
-st.sidebar.markdown("""
-### Dashboard Features:
-1. View pre-computed metrics for all models
-2. Compare performance across different metrics
-3. Examine confusion matrices
-4. Download metrics data as CSV
-""")
-
 # Footer
 st.markdown("---")
-st.markdown("Dashboard created with Streamlit for ML Models Comparison")
+st.markdown("Dashboard created for ML Models Comparison on CIFAR-10 dataset")