import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np

# 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")