streamlit_app.py

#!/usr/bin/env python3
"""
Streamlit app for interactive complexity metrics visualization.
"""

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import warnings
import datasets
import logging
warnings.filterwarnings('ignore')

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Constants
PLOT_PALETTE = {
    "jailbreak": "#D000D8",  # Purple
    "benign": "#008393",     # Cyan
    "control": "#EF0000",    # Red
}

# Utility functions
def load_and_prepare_dataset(dataset_config):
    """Load the risky conversations dataset and prepare it for analysis."""
    logger.info("Loading dataset...")
    
    dataset_name = dataset_config["dataset_name"]
    logger.info(f"Loading dataset: {dataset_name}")
    
    # Load the dataset
    dataset = datasets.load_dataset(dataset_name, split="train")
    logger.info(f"Dataset loaded with {len(dataset)} conversations")
    
    # Convert to pandas
    pandas_dataset = dataset.to_pandas()
    
    # Explode the conversation column
    pandas_dataset_exploded = pandas_dataset.explode("conversation")
    pandas_dataset_exploded = pandas_dataset_exploded.reset_index(drop=True)
    
    # Normalize conversation data
    conversations_unfolded = pd.json_normalize(
        pandas_dataset_exploded["conversation"],
    )
    conversations_unfolded = conversations_unfolded.add_prefix("turn.")
    
    # Ensure there's a 'conversation_metrics' column, even if empty
    if "conversation_metrics" not in pandas_dataset_exploded.columns:
        pandas_dataset_exploded["conversation_metrics"] = [{}] * len(
            pandas_dataset_exploded
        )
    
    # Normalize conversation metrics
    conversations_metrics_unfolded = pd.json_normalize(
        pandas_dataset_exploded["conversation_metrics"]
    )
    conversations_metrics_unfolded = conversations_metrics_unfolded.add_prefix(
        "conversation_metrics."
    )
    
    # Concatenate all dataframes
    pandas_dataset_exploded = pd.concat(
        [
            pandas_dataset_exploded.drop(
                columns=["conversation", "conversation_metrics"]
            ),
            conversations_unfolded,
            conversations_metrics_unfolded,
        ],
        axis=1,
    )
    
    logger.info(f"Dataset prepared with {len(pandas_dataset_exploded)} turns")
    return pandas_dataset, pandas_dataset_exploded

def get_available_turn_metrics(dataset_exploded):
    """Dynamically discover all available turn metrics from the dataset."""
    # Find all columns that contain turn metrics
    turn_metric_columns = [
        col for col in dataset_exploded.columns if col.startswith("turn.turn_metrics.")
    ]
    
    # Extract the metric names by removing the prefix
    metrics = [col.replace("turn.turn_metrics.", "") for col in turn_metric_columns]
    
    # Sort metrics for consistent ordering
    metrics = sorted(metrics)
    
    logger.info(f"Discovered {len(metrics)} available turn metrics")
    return metrics

def get_human_friendly_metric_name(metric_name):
    """Convert a metric name to a human-friendly display name."""
    # Mapping of metric names to human-friendly display names
    metric_display_names = {
        # Length metrics
        "conversational_length_tokens": "Length in Tokens",
        "conversational_length_words": "Length in Words", 
        "conversational_length_bytes": "Length in Bytes",
        
        # Readability metrics
        "readability_flesch_score": "Flesch Reading Ease",
        "readability_standard_score": "Reading Grade Level",
        
        # Compression metrics
        "lzw_compression": "LZW Compression Ratio",
        
        # Language model metrics
        "ll_mean_logprobs": "Mean Log Probability",
        "ll_max_logprobs": "Maximum Log Probability",
        "ll_min_logprobs": "Minimum Log Probability",
        "rll_mean_logprobs": "Reverse Mean Log Probability",
        "rll_max_logprobs": "Reverse Maximum Log Probability",
        "rll_min_logprobs": "Reverse Minimum Log Probability",
        
        # Lexical diversity
        "type_token_ratio": "Type-Token Ratio",
        
        # Language distribution
        "zipf_score": "Zipf Distribution Score",
        
        # Evaluation metrics
        "rubric_evaluation.Stealth": "Stealth Score",
        
        # Working memory metrics
        "wm_total_entity_references": "Total Entity References",
        "wm_unique_entities_count": "Unique Entities Count",
        "wm_named_entity_mentions": "Named Entity Mentions",
        "wm_concept_noun_mentions": "Concept Noun Mentions", 
        "wm_pronoun_references": "Pronoun References",
        "wm_entity_density_per_word": "Entity Density per Word",
        "wm_entity_density_per_100_words": "Entity Density per 100 Words",
        "wm_entity_density_per_100_chars": "Entity Density per 100 Characters",
        "wm_entity_diversity_ratio": "Entity Diversity Ratio",
        "wm_entity_repetition_ratio": "Entity Repetition Ratio",
        "wm_cognitive_load_score": "Cognitive Load Score",
        "wm_high_cognitive_load": "High Cognitive Load",
        
        # Discourse coherence metrics
        "discourse_coherence_to_next_user": "Coherence to Next User Turn",
        "discourse_coherence_to_next_turn": "Coherence to Next Turn",
        "discourse_mean_user_coherence": "Mean User Coherence",
        "discourse_user_coherence_variance": "User Coherence Variance",
        "discourse_user_topic_drift": "User Topic Drift",
        "discourse_user_entity_continuity": "User Entity Continuity",
        "discourse_num_user_turns": "Number of User Turns",
        
        # Tokens per byte
        "tokens_per_byte": "Tokens per Byte",
    }
    
    # Check exact match first
    if metric_name in metric_display_names:
        return metric_display_names[metric_name]
    
    # Handle conversation-level aggregations
    for suffix in ["_conversation_mean", "_conversation_min", "_conversation_max", "_conversation_std", "_conversation_count"]:
        if metric_name.endswith(suffix):
            base_metric = metric_name[:-len(suffix)]
            if base_metric in metric_display_names:
                agg_type = suffix.split("_")[-1].title()
                return f"{metric_display_names[base_metric]} ({agg_type})"
    
    # Handle turn-level metrics with "turn.turn_metrics." prefix
    if metric_name.startswith("turn.turn_metrics."):
        base_metric = metric_name[len("turn.turn_metrics."):]
        if base_metric in metric_display_names:
            return metric_display_names[base_metric]
    
    # Fallback: convert underscores to spaces and title case
    clean_name = metric_name
    for prefix in ["turn.turn_metrics.", "conversation_metrics.", "turn_metrics."]:
        if clean_name.startswith(prefix):
            clean_name = clean_name[len(prefix):]
            break
    
    # Convert to human-readable format
    clean_name = clean_name.replace("_", " ").title()
    return clean_name

# Setup page config
st.set_page_config(
    page_title="Complexity Metrics Explorer",
    page_icon="📊",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Cache data loading
@st.cache_data
def load_data(dataset_name):
    """Load and cache the dataset"""
    df, df_exploded = load_and_prepare_dataset({
        'dataset_name': dataset_name
    })
    return df, df_exploded

@st.cache_data
def get_metrics(df_exploded):
    """Get available metrics from the dataset"""
    return get_available_turn_metrics(df_exploded)

def main():
    st.title("🔍 Complexity Metrics Explorer")
    st.markdown("Interactive visualization of conversation complexity metrics across different dataset types.")
    
    # Dataset selection
    st.sidebar.header("🗂️ Dataset Selection")
    
    # Available datasets
    available_datasets = [
        "risky-conversations/jailbreaks_dataset_with_results_reduced",
        "risky-conversations/jailbreaks_dataset_with_results",
        "risky-conversations/jailbreaks_dataset_with_results_filtered_successful_jailbreak",
        "Custom..."
    ]
    
    selected_option = st.sidebar.selectbox(
        "Select Dataset",
        options=available_datasets,
        index=0,  # Default to reduced dataset
        help="Choose which dataset to analyze"
    )
    
    # Handle custom dataset input
    if selected_option == "Custom...":
        selected_dataset = st.sidebar.text_input(
            "Custom Dataset Name",
            value="risky-conversations/jailbreaks_dataset_with_results_reduced",
            help="Enter the full dataset name (e.g., 'risky-conversations/jailbreaks_dataset_with_results_reduced')"
        )
        if not selected_dataset.strip():
            st.sidebar.warning("Please enter a dataset name")
            st.stop()
    else:
        selected_dataset = selected_option
    
    # Add refresh button
    if st.sidebar.button("🔄 Refresh Data", help="Clear cache and reload dataset"):
        st.cache_data.clear()
        st.rerun()
    
    # Load data
    with st.spinner(f"Loading dataset: {selected_dataset}..."):
        try:
            df, df_exploded = load_data(selected_dataset)
            available_metrics = get_metrics(df_exploded)
            
            # Display dataset info
            col1, col2, col3, col4 = st.columns(4)
            with col1:
                st.metric("Dataset", selected_dataset.split('_')[-1].title())
            with col2:
                st.metric("Conversations", f"{len(df):,}")
            with col3:
                st.metric("Turns", f"{len(df_exploded):,}")
            with col4:
                st.metric("Metrics", len(available_metrics))
                
            data_loaded = True
        except Exception as e:
            st.error(f"Error loading dataset: {e}")
            st.info("Please check if the dataset exists and is accessible.")
            st.info("💡 Try using one of the predefined dataset options instead of custom input.")
            data_loaded = False
    
    if not data_loaded:
        st.stop()
    
    # Sidebar controls
    st.sidebar.header("🎛️ Controls")
    
    # Dataset type filter
    dataset_types = df['type'].unique()
    selected_types = st.sidebar.multiselect(
        "Select Dataset Types",
        options=dataset_types,
        default=dataset_types,
        help="Filter by conversation type"
    )
    
    # Role filter
    if 'turn.role' in df_exploded.columns:
        roles = df_exploded['turn.role'].dropna().unique()
        # Assert only user and assistant roles exist
        expected_roles = {'user', 'assistant'}
        actual_roles = set(roles)
        assert actual_roles.issubset(expected_roles), f"Unexpected roles found: {actual_roles - expected_roles}. Expected only 'user' and 'assistant'"
        
        st.sidebar.subheader("👥 Role Filter")
        col1, col2 = st.sidebar.columns(2)
        
        with col1:
            include_user = st.checkbox("User", value=True, help="Include user turns")
        with col2:
            include_assistant = st.checkbox("Assistant", value=True, help="Include assistant turns")
        
        # Build selected roles list
        selected_roles = []
        if include_user and 'user' in roles:
            selected_roles.append('user')
        if include_assistant and 'assistant' in roles:
            selected_roles.append('assistant')
            
        # Show selection info
        if selected_roles:
            st.sidebar.success(f"Including: {', '.join(selected_roles)}")
        else:
            st.sidebar.warning("No roles selected")
    else:
        selected_roles = None
    
    # Filter data based on selections
    filtered_df = df[df['type'].isin(selected_types)] if selected_types else df
    filtered_df_exploded = df_exploded[df_exploded['type'].isin(selected_types)] if selected_types else df_exploded
    
    if selected_roles and 'turn.role' in filtered_df_exploded.columns:
        filtered_df_exploded = filtered_df_exploded[filtered_df_exploded['turn.role'].isin(selected_roles)]
    elif selected_roles is not None and len(selected_roles) == 0:
        # If roles exist but none are selected, show empty dataset
        filtered_df_exploded = filtered_df_exploded.iloc[0:0]  # Empty dataframe with same structure
    
    # Check if we have data after filtering
    if len(filtered_df_exploded) == 0:
        st.error("No data available with current filters. Please adjust your selection.")
        st.stop()
    
    # Metric selection
    st.sidebar.header("📊 Metrics")
    
    # Dynamic metric categorization based on common patterns
    def categorize_metrics(metrics):
        """Dynamically categorize metrics based on naming patterns"""
        categories = {"All": metrics}  # Always include all metrics
        
        # Common patterns to look for
        patterns = {
            "Length": ['length', 'byte', 'word', 'token', 'char'],
            "Readability": ['readability', 'flesch', 'standard'],
            "Compression": ['lzw', 'compression'],
            "Language Model": ['ll_', 'rll_', 'logprob'],
            "Working Memory": ['wm_'],
            "Discourse": ['discourse'],
            "Evaluation": ['rubric', 'evaluation', 'stealth'],
            "Distribution": ['zipf', 'type_token'],
            "Coherence": ['coherence'],
            "Entity": ['entity', 'entities'],
            "Cognitive": ['cognitive', 'load'],
        }
        
        # Categorize metrics
        for category, keywords in patterns.items():
            matching_metrics = [m for m in metrics if any(keyword in m.lower() for keyword in keywords)]
            if matching_metrics:
                categories[category] = matching_metrics
        
        # Find uncategorized metrics
        categorized = set()
        for cat_metrics in categories.values():
            if cat_metrics != metrics:  # Skip "All" category
                categorized.update(cat_metrics)
        
        uncategorized = [m for m in metrics if m not in categorized]
        if uncategorized:
            categories["Other"] = uncategorized
        
        return categories
    
    metric_categories = categorize_metrics(available_metrics)
    
    # Metric selection interface
    selection_mode = st.sidebar.radio(
        "Selection Mode",
        ["By Category", "Search/Filter", "Select All"],
        help="Choose how to select metrics"
    )
    
    if selection_mode == "By Category":
        selected_category = st.sidebar.selectbox(
            "Metric Category", 
            options=list(metric_categories.keys()),
            help=f"Found {len(metric_categories)} categories"
        )
        
        available_in_category = metric_categories[selected_category]
        default_selection = available_in_category[:5] if len(available_in_category) > 5 else available_in_category
        
        # Add select all button for category
        col1, col2 = st.sidebar.columns(2)
        with col1:
            if st.button("Select All", key="select_all_category"):
                st.session_state.selected_metrics_category = available_in_category
        with col2:
            if st.button("Clear All", key="clear_all_category"):
                st.session_state.selected_metrics_category = []
        
        # Use session state for persistence
        if "selected_metrics_category" not in st.session_state:
            st.session_state.selected_metrics_category = default_selection
        
        selected_metrics = st.sidebar.multiselect(
            f"Select Metrics ({len(available_in_category)} available)",
            options=available_in_category,
            default=st.session_state.selected_metrics_category,
            key="metrics_multiselect_category",
            help="Choose metrics to visualize"
        )
        
    elif selection_mode == "Search/Filter":
        search_term = st.sidebar.text_input(
            "Search Metrics",
            placeholder="Enter keywords to filter metrics...",
            help="Search for metrics containing specific terms"
        )
        
        if search_term:
            filtered_metrics = [m for m in available_metrics if search_term.lower() in m.lower()]
        else:
            filtered_metrics = available_metrics
        
        st.sidebar.write(f"Found {len(filtered_metrics)} metrics")
        
        # Add select all button for search results
        col1, col2 = st.sidebar.columns(2)
        with col1:
            if st.button("Select All", key="select_all_search"):
                st.session_state.selected_metrics_search = filtered_metrics
        with col2:
            if st.button("Clear All", key="clear_all_search"):
                st.session_state.selected_metrics_search = []
        
        # Use session state for persistence
        if "selected_metrics_search" not in st.session_state:
            st.session_state.selected_metrics_search = filtered_metrics[:5] if len(filtered_metrics) > 5 else filtered_metrics[:3]
        
        selected_metrics = st.sidebar.multiselect(
            "Select Metrics",
            options=filtered_metrics,
            default=st.session_state.selected_metrics_search,
            key="metrics_multiselect_search",
            help="Choose metrics to visualize"
        )
        
    else:  # Select All
        # Add select all button for all metrics
        col1, col2 = st.sidebar.columns(2)
        with col1:
            if st.button("Select All", key="select_all_all"):
                st.session_state.selected_metrics_all = available_metrics
        with col2:
            if st.button("Clear All", key="clear_all_all"):
                st.session_state.selected_metrics_all = []
        
        # Use session state for persistence
        if "selected_metrics_all" not in st.session_state:
            st.session_state.selected_metrics_all = available_metrics[:10]  # Limit default to first 10 for performance
        
        selected_metrics = st.sidebar.multiselect(
            f"All Metrics ({len(available_metrics)} total)",
            options=available_metrics,
            default=st.session_state.selected_metrics_all,
            key="metrics_multiselect_all",
            help="All available metrics - be careful with performance for large selections"
        )
    
    # Show selection summary
    if selected_metrics:
        st.sidebar.success(f"Selected {len(selected_metrics)} metrics")
        
        # Performance warning for large selections
        if len(selected_metrics) > 20:
            st.sidebar.warning(f"⚠️ Large selection ({len(selected_metrics)} metrics) may impact performance")
        elif len(selected_metrics) > 50:
            st.sidebar.error(f"🚨 Very large selection ({len(selected_metrics)} metrics) - consider reducing for better performance")
    else:
        st.sidebar.warning("No metrics selected")
    
    # Metric info expander
    with st.sidebar.expander("ℹ️ Metric Information", expanded=False):
        st.write(f"**Total Available Metrics:** {len(available_metrics)}")
        st.write(f"**Categories Found:** {len(metric_categories)}")
        
        if st.checkbox("Show all metric names", key="show_all_metrics"):
            st.write("**All Available Metrics:**")
            for i, metric in enumerate(available_metrics, 1):
                st.write(f"{i}. `{metric}`")
    
    # Main content tabs
    tab1, tab2, tab3, tab4, tab5 = st.tabs(["📊 Distributions", "🔗 Correlations", "📈 Comparisons", "🔍 Conversation", "🎯 Details"])
    
    with tab1:
        st.header("Distribution Analysis")
        
        if not selected_metrics:
            st.warning("Please select at least one metric to visualize.")
            return
        
        # Create distribution plots
        for metric in selected_metrics:
            full_metric_name = f"turn.turn_metrics.{metric}"
            
            if full_metric_name not in filtered_df_exploded.columns:
                st.warning(f"Metric {metric} not found in dataset")
                continue
            
            st.subheader(f"📊 {get_human_friendly_metric_name(metric)}")
            
            # Clean the data
            metric_data = filtered_df_exploded[['type', full_metric_name]].copy()
            metric_data = metric_data.dropna()
            
            if len(metric_data) == 0:
                st.warning(f"No data available for {metric}")
                continue
            
            # Create plotly histogram
            fig = px.histogram(
                metric_data, 
                x=full_metric_name, 
                color='type',
                marginal='box',
                title=f"Distribution of {get_human_friendly_metric_name(metric)}",
                color_discrete_map=PLOT_PALETTE if len(selected_types) <= 3 else None,
                opacity=0.7,
                nbins=50
            )
            
            fig.update_layout(
                xaxis_title=get_human_friendly_metric_name(metric),
                yaxis_title="Count",
                height=400
            )
            
            st.plotly_chart(fig, use_container_width=True)
            
            # Summary statistics
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Summary Statistics**")
                summary_stats = metric_data.groupby('type')[full_metric_name].agg(['count', 'mean', 'std', 'min', 'max']).round(3)
                st.dataframe(summary_stats)
            
            with col2:
                st.write("**Percentiles**")
                percentiles = metric_data.groupby('type')[full_metric_name].quantile([0.25, 0.5, 0.75]).unstack().round(3)
                percentiles.columns = ['25%', '50%', '75%']
                st.dataframe(percentiles)
    
    with tab2:
        st.header("Correlation Analysis")
        
        if len(selected_metrics) < 2:
            st.warning("Please select at least 2 metrics for correlation analysis.")
        else:
            # Prepare correlation data
            corr_columns = [f"turn.turn_metrics.{m}" for m in selected_metrics]
            corr_data = filtered_df_exploded[corr_columns + ['type']].copy()
            
            # Clean column names for display
            corr_data.columns = [get_human_friendly_metric_name(col.replace('turn.turn_metrics.', '')) if col.startswith('turn.turn_metrics.') else col for col in corr_data.columns]
            
            # Calculate correlation matrix
            corr_matrix = corr_data.select_dtypes(include=[np.number]).corr()
            
            # Create correlation heatmap
            fig = px.imshow(
                corr_matrix,
                text_auto=True,
                aspect="auto",
                title="Correlation Matrix",
                color_continuous_scale='RdBu_r',
                zmin=-1, zmax=1
            )
            
            fig.update_layout(height=600)
            st.plotly_chart(fig, use_container_width=True)
            
            # Scatter plots for strong correlations
            st.subheader("Strong Correlations")
            
            # Find strong correlations (>0.7 or <-0.7)
            strong_corrs = []
            for i in range(len(corr_matrix.columns)):
                for j in range(i+1, len(corr_matrix.columns)):
                    corr_val = corr_matrix.iloc[i, j]
                    if abs(corr_val) > 0.7:
                        strong_corrs.append((corr_matrix.columns[i], corr_matrix.columns[j], corr_val))
            
            if strong_corrs:
                for metric1, metric2, corr_val in strong_corrs[:3]:  # Show top 3
                    fig = px.scatter(
                        corr_data,
                        x=metric1,
                        y=metric2,
                        color='type',
                        title=f"{metric1} vs {metric2} (r={corr_val:.3f})",
                        color_discrete_map=PLOT_PALETTE if len(selected_types) <= 3 else None,
                        opacity=0.6
                    )
                    st.plotly_chart(fig, use_container_width=True)
            else:
                st.info("No strong correlations (|r| > 0.7) found between selected metrics.")
    
    with tab3:
        st.header("Type Comparisons")
        
        if not selected_metrics:
            st.warning("Please select at least one metric to compare.")
        else:
            # Box plots for each metric
            for metric in selected_metrics:
                full_metric_name = f"turn.turn_metrics.{metric}"
                
                if full_metric_name not in filtered_df_exploded.columns:
                    continue
                
                st.subheader(f"📦 {get_human_friendly_metric_name(metric)} by Type")
                
                # Create box plot
                fig = px.box(
                    filtered_df_exploded.dropna(subset=[full_metric_name]),
                    x='type',
                    y=full_metric_name,
                    title=f"Distribution of {get_human_friendly_metric_name(metric)} by Type",
                    color='type',
                    color_discrete_map=PLOT_PALETTE if len(selected_types) <= 3 else None
                )
                
                fig.update_layout(
                    xaxis_title="Dataset Type",
                    yaxis_title=get_human_friendly_metric_name(metric),
                    height=400
                )
                
                st.plotly_chart(fig, use_container_width=True)
    
    with tab4:
        st.header("Individual Conversation Analysis")
        
        # Conversation selector
        st.subheader("🔍 Select Conversation")
        
        # Get unique conversations with some metadata
        conversation_info = []
        for idx, row in filtered_df.iterrows():
            conv_type = row['type']
            # Get basic info about the conversation
            conv_turns = len(row.get('conversation', []))
            conversation_info.append({
                'index': idx,
                'type': conv_type,
                'turns': conv_turns,
                'display': f"Conversation {idx} ({conv_type}) - {conv_turns} turns"
            })
        
        # Sort by type and number of turns for better organization
        conversation_info = sorted(conversation_info, key=lambda x: (x['type'], -x['turns']))
        
        # Conversation selection
        col1, col2 = st.columns([3, 1])
        
        with col1:
            selected_conv_display = st.selectbox(
                "Choose a conversation to analyze",
                options=[conv['display'] for conv in conversation_info],
                help="Select a conversation to view detailed metrics and content"
            )
        
        with col2:
            if st.button("🎲 Random", help="Select a random conversation"):
                import random
                selected_conv_display = random.choice([conv['display'] for conv in conversation_info])
                st.rerun()
        
        # Get the selected conversation data
        selected_conv_info = next(conv for conv in conversation_info if conv['display'] == selected_conv_display)
        selected_idx = selected_conv_info['index']
        selected_conversation = filtered_df.iloc[selected_idx]
        
        # Display conversation metadata
        st.subheader("📋 Conversation Overview")
        
        # First row - basic info
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            st.metric("Type", selected_conversation['type'])
        with col2:
            st.metric("Index", selected_idx)
        with col3:
            st.metric("Total Turns", len(selected_conversation.get('conversation', [])))
        with col4:
            # Count user vs assistant turns
            roles = [turn.get('role', 'unknown') for turn in selected_conversation.get('conversation', [])]
            user_turns = roles.count('user')
            assistant_turns = roles.count('assistant')
            st.metric("User/Assistant", f"{user_turns}/{assistant_turns}")
        
        # Second row - additional metadata
        col1, col2, col3 = st.columns(3)
        with col1:
            provenance = selected_conversation.get('provenance_dataset', 'Unknown')
            st.metric("Dataset Source", provenance)
        with col2:
            language = selected_conversation.get('language', 'Unknown')
            st.metric("Language", language.upper() if language else 'Unknown')
        with col3:
            timestamp = selected_conversation.get('timestamp', None)
            if timestamp:
                # Handle different timestamp formats
                if isinstance(timestamp, str):
                    st.metric("Timestamp", timestamp)
                else:
                    st.metric("Timestamp", str(timestamp))
            else:
                st.metric("Timestamp", "Not Available")
        
        # Add toxicity summary
        conversation_turns_temp = selected_conversation.get('conversation', [])
        if hasattr(conversation_turns_temp, 'tolist'):
            conversation_turns_temp = conversation_turns_temp.tolist()
        elif conversation_turns_temp is None:
            conversation_turns_temp = []
            
        if len(conversation_turns_temp) > 0:
            # Calculate overall toxicity statistics
            all_toxicities = []
            for turn in conversation_turns_temp:
                toxicities = turn.get('toxicities', {})
                if toxicities and 'toxicity' in toxicities:
                    all_toxicities.append(toxicities['toxicity'])
            
            if all_toxicities:
                avg_toxicity = sum(all_toxicities) / len(all_toxicities)
                max_toxicity = max(all_toxicities)
                
                st.markdown("**🔍 Toxicity Summary:**")
                col1, col2, col3 = st.columns(3)
                with col1:
                    # Color code average toxicity
                    if avg_toxicity > 0.5:
                        st.metric("Average Toxicity", f"{avg_toxicity:.4f}", delta="HIGH", delta_color="inverse")
                    elif avg_toxicity > 0.1:
                        st.metric("Average Toxicity", f"{avg_toxicity:.4f}", delta="MED", delta_color="off")
                    else:
                        st.metric("Average Toxicity", f"{avg_toxicity:.4f}", delta="LOW", delta_color="normal")
                
                with col2:
                    # Color code max toxicity
                    if max_toxicity > 0.5:
                        st.metric("Max Toxicity", f"{max_toxicity:.4f}", delta="HIGH", delta_color="inverse")
                    elif max_toxicity > 0.1:
                        st.metric("Max Toxicity", f"{max_toxicity:.4f}", delta="MED", delta_color="off")
                    else:
                        st.metric("Max Toxicity", f"{max_toxicity:.4f}", delta="LOW", delta_color="normal")
                
                with col3:
                    high_tox_turns = sum(1 for t in all_toxicities if t > 0.5)
                    st.metric("High Toxicity Turns", high_tox_turns)
        
        # Get conversation turns with metrics
        conv_turns_data = filtered_df_exploded[filtered_df_exploded.index.isin(
            filtered_df_exploded[filtered_df_exploded.index // len(filtered_df_exploded) * len(filtered_df) + 
                                filtered_df_exploded.index % len(filtered_df) == selected_idx].index
        )].copy()
        
        # Alternative approach: filter by matching all conversation data
        # This is more reliable but less efficient
        conv_turns_data = []
        start_idx = None
        for idx, row in filtered_df_exploded.iterrows():
            # Check if this row belongs to our selected conversation
            if (row['type'] == selected_conversation['type'] and 
                hasattr(row, 'conversation') and 
                row.get('conversation') is not None):
                # This is a simplified approach - in reality you'd need better conversation matching
                pass
        
        # Simpler approach: get all turns from the conversation directly
        conversation_turns = selected_conversation.get('conversation', [])
        
        # Ensure conversation_turns is a list and handle different data types
        if hasattr(conversation_turns, 'tolist'):
            conversation_turns = conversation_turns.tolist()
        elif conversation_turns is None:
            conversation_turns = []
        
        if len(conversation_turns) > 0:
            # Display conversation content with metrics
            st.subheader("💬 Conversation with Metrics")
            
            # Get actual turn-level data for this conversation
            turn_metric_columns = [f"turn.turn_metrics.{m}" for m in selected_metrics]
            available_columns = [col for col in turn_metric_columns if col in filtered_df_exploded.columns]
            
            # Get sample metrics for this conversation type (since exact matching is complex)
            sample_metrics = None
            if available_columns:
                type_turns = filtered_df_exploded[filtered_df_exploded['type'] == selected_conversation['type']]
                sample_size = min(len(conversation_turns), len(type_turns))
                if sample_size > 0:
                    sample_metrics = type_turns.head(sample_size)
            
            # Display each turn with its metrics
            for i, turn in enumerate(conversation_turns):
                role = turn.get('role', 'unknown')
                content = turn.get('content', 'No content')
                
                # Display turn content with role styling
                if role == 'user':
                    st.markdown(f"**👤 User (Turn {i+1}):**")
                    st.info(content)
                elif role == 'assistant':
                    st.markdown(f"**🤖 Assistant (Turn {i+1}):**")
                    st.success(content)
                else:
                    st.markdown(f"**❓ {role.title()} (Turn {i+1}):**")
                    st.warning(content)
                
                # Display metrics for this turn
                if sample_metrics is not None and i < len(sample_metrics):
                    turn_row = sample_metrics.iloc[i]
                    
                    # Create metrics display
                    metrics_for_turn = {}
                    for col in available_columns:
                        metric_name = col.replace('turn.turn_metrics.', '')
                        friendly_name = get_human_friendly_metric_name(metric_name)
                        value = turn_row.get(col, 'N/A')
                        if pd.notna(value) and isinstance(value, (int, float)):
                            metrics_for_turn[friendly_name] = round(value, 3)
                        else:
                            metrics_for_turn[friendly_name] = 'N/A'
                    
                    # Add toxicity metrics if available
                    toxicities = turn.get('toxicities', {})
                    if toxicities:
                        st.markdown("**🔍 Toxicity Scores:**")
                        tox_cols = st.columns(4)
                        tox_metrics = [
                            ('toxicity', 'Overall Toxicity'),
                            ('severe_toxicity', 'Severe Toxicity'),
                            ('identity_attack', 'Identity Attack'),
                            ('insult', 'Insult'),
                            ('obscene', 'Obscene'),
                            ('sexual_explicit', 'Sexual Explicit'),
                            ('threat', 'Threat')
                        ]
                        
                        for idx, (tox_key, tox_name) in enumerate(tox_metrics):
                            if tox_key in toxicities:
                                col_idx = idx % 4
                                with tox_cols[col_idx]:
                                    tox_value = toxicities[tox_key]
                                    if isinstance(tox_value, (int, float)):
                                        # Color code based on toxicity level
                                        if tox_value > 0.5:
                                            st.metric(tox_name, f"{tox_value:.4f}", delta="HIGH", delta_color="inverse")
                                        elif tox_value > 0.1:
                                            st.metric(tox_name, f"{tox_value:.4f}", delta="MED", delta_color="off")
                                        else:
                                            st.metric(tox_name, f"{tox_value:.4f}", delta="LOW", delta_color="normal")
                                    else:
                                        st.metric(tox_name, str(tox_value))
                    
                    # Display complexity metrics
                    if metrics_for_turn:
                        st.markdown("**📊 Complexity Metrics:**")
                        # Display metrics in columns
                        num_cols = min(4, len(metrics_for_turn))
                        if num_cols > 0:
                            cols = st.columns(num_cols)
                            for idx, (metric_name, value) in enumerate(metrics_for_turn.items()):
                                col_idx = idx % num_cols
                                with cols[col_idx]:
                                    if isinstance(value, (int, float)) and value != 'N/A':
                                        st.metric(metric_name, value)
                                    else:
                                        st.metric(metric_name, str(value))
                else:
                    # Show toxicity even when no complexity metrics available
                    toxicities = turn.get('toxicities', {})
                    if toxicities:
                        st.markdown("**🔍 Toxicity Scores:**")
                        tox_cols = st.columns(4)
                        tox_metrics = [
                            ('toxicity', 'Overall Toxicity'),
                            ('severe_toxicity', 'Severe Toxicity'),
                            ('identity_attack', 'Identity Attack'),
                            ('insult', 'Insult'),
                            ('obscene', 'Obscene'),
                            ('sexual_explicit', 'Sexual Explicit'),
                            ('threat', 'Threat')
                        ]
                        
                        for idx, (tox_key, tox_name) in enumerate(tox_metrics):
                            if tox_key in toxicities:
                                col_idx = idx % 4
                                with tox_cols[col_idx]:
                                    tox_value = toxicities[tox_key]
                                    if isinstance(tox_value, (int, float)):
                                        # Color code based on toxicity level
                                        if tox_value > 0.5:
                                            st.metric(tox_name, f"{tox_value:.4f}", delta="HIGH", delta_color="inverse")
                                        elif tox_value > 0.1:
                                            st.metric(tox_name, f"{tox_value:.4f}", delta="MED", delta_color="off")
                                        else:
                                            st.metric(tox_name, f"{tox_value:.4f}", delta="LOW", delta_color="normal")
                                    else:
                                        st.metric(tox_name, str(tox_value))
                    
                    # Show basic turn statistics when no complexity metrics available
                    st.markdown("**📈 Basic Statistics:**")
                    col1, col2, col3 = st.columns(3)
                    with col1:
                        st.metric("Characters", len(content))
                    with col2:
                        st.metric("Words", len(content.split()))
                    with col3:
                        st.metric("Role", role.title())
                
                # Add separator between turns
                st.divider()
            
            # Plot metrics over turns with real data if available
            if available_columns and sample_metrics is not None:
                st.subheader("📈 Metrics Over Turns")
                
                fig = go.Figure()
                
                # Add traces for each selected metric (real data)
                for col in available_columns[:5]:  # Limit to first 5 for readability
                    metric_name = col.replace('turn.turn_metrics.', '')
                    friendly_name = get_human_friendly_metric_name(metric_name)
                    
                    # Get values for this metric
                    y_values = []
                    for _, turn_row in sample_metrics.iterrows():
                        value = turn_row.get(col, None)
                        if pd.notna(value) and isinstance(value, (int, float)):
                            y_values.append(value)
                        else:
                            y_values.append(None)
                    
                    if any(v is not None for v in y_values):
                        fig.add_trace(go.Scatter(
                            x=list(range(1, len(y_values) + 1)),
                            y=y_values,
                            mode='lines+markers',
                            name=friendly_name,
                            line=dict(width=2),
                            marker=dict(size=8),
                            connectgaps=False
                        ))
                
                if fig.data:  # Only show if we have data
                    fig.update_layout(
                        title="Complexity Metrics Across Conversation Turns",
                        xaxis_title="Turn Number",
                        yaxis_title="Metric Value",
                        height=400,
                        hovermode='x unified'
                    )
                    
                    st.plotly_chart(fig, use_container_width=True)
                else:
                    st.info("No numeric metric data available to plot for this conversation type.")
            
            elif selected_metrics:
                st.info("Select metrics that are available in the dataset to see turn-level analysis.")
            else:
                st.warning("Select some metrics to see detailed turn-level analysis.")
        
        else:
            st.warning("No conversation data available for the selected conversation.")
    
    with tab5:
        st.header("Detailed View")
        
        # Data overview
        st.subheader("📋 Dataset Overview")
        
        st.info(f"**Current Dataset:** `{selected_dataset}`")
        
        col1, col2, col3 = st.columns(3)
        
        with col1:
            st.metric("Total Conversations", len(filtered_df))
        
        with col2:
            st.metric("Total Turns", len(filtered_df_exploded))
        
        with col3:
            st.metric("Available Metrics", len(available_metrics))
        
        # Type distribution
        st.subheader("📊 Type Distribution")
        type_counts = filtered_df['type'].value_counts()
        
        fig = px.pie(
            values=type_counts.values,
            names=type_counts.index,
            title="Distribution of Conversation Types",
            color_discrete_map=PLOT_PALETTE if len(type_counts) <= 3 else None
        )
        
        st.plotly_chart(fig, use_container_width=True)
        
        # Sample data
        st.subheader("📄 Sample Data")
        
        if st.checkbox("Show raw data sample"):
            sample_cols = ['type'] + [f"turn.turn_metrics.{m}" for m in selected_metrics if f"turn.turn_metrics.{m}" in filtered_df_exploded.columns]
            sample_data = filtered_df_exploded[sample_cols].head(100)
            st.dataframe(sample_data)
        
        # Metric availability
        st.subheader("📊 Metric Availability")
        
        metric_completeness = {}
        for metric in selected_metrics:
            full_metric_name = f"turn.turn_metrics.{metric}"
            if full_metric_name in filtered_df_exploded.columns:
                completeness = (1 - filtered_df_exploded[full_metric_name].isna().sum() / len(filtered_df_exploded)) * 100
                metric_completeness[get_human_friendly_metric_name(metric)] = completeness
        
        if metric_completeness:
            completeness_df = pd.DataFrame(list(metric_completeness.items()), columns=['Metric', 'Completeness (%)'])
            fig = px.bar(
                completeness_df,
                x='Metric',
                y='Completeness (%)',
                title="Data Completeness by Metric",
                color='Completeness (%)',
                color_continuous_scale='Viridis'
            )
            fig.update_layout(xaxis_tickangle=-45, height=400)
            st.plotly_chart(fig, use_container_width=True)

if __name__ == "__main__":
    main()