Update app.py

app.py

@@ -1,4 +1,147 @@
 import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# 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 (replace these with your actual results)
+results = {
+    'Accuracy': {
+        'KNN': 0.345,  # Replace with your actual values
+        'Logistic Regression': 0.389,
+        'Random Forest': 0.412,
+        'Naive Bayes': 0.298,
+        'K-Means': 0.275,
+        'CNN': 0.456
+    },
+    '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
+    }
+}
+
+# Pre-computed confusion matrices (replace these with your actual confusion matrices)
+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))
+}
+
+# Create tabs for different visualizations
+tab1, tab2, tab3 = st.tabs(["Metrics Comparison", "Confusion Matrices", "Radar Plot"])
+
+with tab1:
+    st.header("Performance Metrics Comparison")
+    
+    # Convert results to DataFrame for plotting
+    df_metrics = pd.DataFrame(results)
+    df_metrics.index.name = 'Model'
+    df_metrics = df_metrics.reset_index()
+    
+    # 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)
+    
+    # Display metrics table
+    st.subheader("Metrics Table")
+    st.dataframe(df_metrics.set_index('Model').style.format("{:.3f}"))
+
+with tab2:
+    st.header("Confusion Matrices")
+    
+    # Select model 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 radar plot using plotly
+    fig = go.Figure()
+    metrics = list(results.keys())
+    models = list(results['Accuracy'].keys())
+    
+    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
+        ))
+    
+    fig.update_layout(
+        polar=dict(radialaxis=dict(visible=True, range=[0, 1])),
+        showlegend=True,
+        title="Model Comparison - All Metrics"
+    )
+    
+    st.plotly_chart(fig)
+
+# Add download button for metrics
+@st.cache_data
+def convert_df_to_csv():
+    return df_metrics.to_csv(index=False)
+
+st.sidebar.header("Download Data")
+csv = convert_df_to_csv()
+st.sidebar.download_button(
+    label="Download metrics as CSV",
+    data=csv,
+    file_name='model_metrics.csv',
+    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")