#!/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 def render_metric_distribution(metric, filtered_df_exploded, selected_types): """Render distribution plot for a single metric.""" 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") return 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}") return # 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) # 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("🗂ī¸ 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("🎛ī¸ 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("đŸ‘Ĩ 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() # Main content tabs tab1, tab2, tab3, tab4, tab5 = st.tabs( [ "📊 Distributions", "🔗 Correlations", "📈 Comparisons", "🔍 Conversation", "đŸŽ¯ Details", ] ) # Make available metrics accessible to all tabs available_metrics_for_analysis = available_metrics with tab1: st.header("Distribution Analysis") # Simple metric selection - just show all metrics with checkboxes st.subheader("📊 Select Metrics to Plot") st.info(f"**{len(available_metrics)} metrics available** - Check the boxes below to plot their distributions") # Optional: Add search functionality to help users find metrics search_term = st.text_input( "🔍 Search metrics (optional)", 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() ] st.write(f"**{len(filtered_metrics)} metrics** match your search") else: filtered_metrics = available_metrics # Create checkboxes for each metric to allow multiple selections if not filtered_metrics: st.warning("No metrics found. Try adjusting your search.") else: # Organize checkboxes in columns for better layout cols_per_row = 3 selected_for_plotting = [] for i in range(0, len(filtered_metrics), cols_per_row): cols = st.columns(cols_per_row) for j, metric in enumerate(filtered_metrics[i : i + cols_per_row]): with cols[j]: friendly_name = get_human_friendly_metric_name(metric) # Truncate checkbox text if too long checkbox_text = ( friendly_name[:25] + "..." if len(friendly_name) > 25 else friendly_name ) if st.checkbox( f"📈 {checkbox_text}", key=f"plot_{metric}", help=f"Plot distribution for {friendly_name}", ): selected_for_plotting.append(metric) # Render selected metrics if selected_for_plotting: st.success(f"Plotting {len(selected_for_plotting)} selected metrics...") for metric in selected_for_plotting: render_metric_distribution( metric, filtered_df_exploded, selected_types ) else: st.info("👆 Check the boxes above to plot metric distributions") with tab2: st.header("Correlation Analysis") if len(available_metrics_for_analysis) < 2: st.warning("Please select at least 2 metrics for correlation analysis.") else: # Add button to trigger correlation analysis st.info( f"🔗 Ready to analyze correlations between {len(available_metrics_for_analysis)} metrics" ) col1, col2 = st.columns([1, 3]) with col1: run_correlation = st.button( "🔍 Run Correlation Analysis", help="Calculate and display correlation matrix and scatter plots", ) with col2: if len(available_metrics_for_analysis) > 10: st.warning( f"⚠ī¸ Large analysis ({len(available_metrics_for_analysis)} metrics) - may take some time" ) if run_correlation: with st.spinner("Calculating correlations..."): # Prepare correlation data corr_columns = [f"turn.turn_metrics.{m}" for m in available_metrics_for_analysis] 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 available_metrics_for_analysis: st.warning("Please select at least one metric to compare.") else: # Box plots for each metric for metric in available_metrics_for_analysis: full_metric_name = f"turn.turn_metrics.{metric}" if full_metric_name not in filtered_df_exploded.columns: continue st.subheader(f"đŸ“Ļ {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 total number of conversations and basic info total_conversations = len(filtered_df) available_indices = list(filtered_df.index) st.info(f"📊 Dataset contains {total_conversations:,} conversations (indices: {min(available_indices)} to {max(available_indices)})") # Conversation selection with number input col1, col2, col3 = st.columns([2, 1, 1]) with col1: selected_idx = st.number_input( "Conversation Index", min_value=min(available_indices), max_value=max(available_indices), value=available_indices[0], # Default to first available step=1, help=f"Enter a conversation index between {min(available_indices)} and {max(available_indices)}" ) with col2: if st.button("🎲 Random", help="Select a random conversation"): import random selected_idx = random.choice(available_indices) st.rerun() with col3: if st.button("ℹī¸ Info", help="Show conversation preview"): if selected_idx in available_indices: preview_row = filtered_df.loc[selected_idx] st.info(f"**Type:** {preview_row['type']} | **Turns:** {len(preview_row.get('conversation', []))}") else: st.error("Invalid conversation index") # Validate and get the selected conversation data if selected_idx not in available_indices: st.error(f"❌ Conversation index {selected_idx} not found in filtered dataset. Available range: {min(available_indices)} to {max(available_indices)}") st.stop() selected_conversation = filtered_df.loc[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("đŸ’Ŧ Conversation with Metrics") # Get actual turn-level data for this conversation turn_metric_columns = [f"turn.turn_metrics.{m}" for m in available_metrics_for_analysis] 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 available_metrics_for_analysis: 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") # Add button to trigger detailed analysis st.info("đŸŽ¯ Generate detailed dataset analysis and visualizations") col1, col2 = st.columns([1, 3]) with col1: show_details = st.button( "📊 Show Detailed Analysis", help="Generate comprehensive dataset overview and metric analysis", ) with col2: if len(available_metrics_for_analysis) > 20: st.warning("⚠ī¸ Large metric selection - analysis may take some time") if show_details: with st.spinner("Generating detailed analysis..."): # 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 available_metrics_for_analysis 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 available_metrics_for_analysis: 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()