#!/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 at the top st.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..." ] col1, col2 = st.columns([3, 1]) with col1: selected_option = st.selectbox( "Select Dataset", options=available_datasets, index=0, # Default to reduced dataset help="Choose which dataset to analyze", format_func=lambda x: x.split('/')[-1] if x != "Custom..." else x # Show only the dataset name part ) with col2: # Add refresh button if st.button("🔄 Refresh Data", help="Clear cache and reload dataset"): st.cache_data.clear() st.rerun() # Handle custom dataset input if selected_option == "Custom...": selected_dataset = st.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.warning("Please enter a dataset name") st.stop() else: selected_dataset = selected_option # 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() # Controls at the top of the page st.header("🎛ī¸ Analysis Controls") # Dataset type filter dataset_types = df['type'].unique() col1, col2 = st.columns(2) with col1: selected_types = st.multiselect( "Select Dataset Types", options=dataset_types, default=dataset_types, help="Filter by conversation type" ) # Role filter with col2: 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.subheader("đŸ‘Ĩ Role Filter") col2_1, col2_2 = st.columns(2) with col2_1: include_user = st.checkbox("User", value=True, help="Include user turns") with col2_2: 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.success(f"Including: {', '.join(selected_roles)}") else: st.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.header("📊 Metrics Selection") # 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.radio( "Selection Mode", ["By Category", "Search/Filter", "Select All"], help="Choose how to select metrics", horizontal=True ) if selection_mode == "By Category": col1, col2 = st.columns([2, 1]) with col1: selected_category = st.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.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.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.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.write(f"Found {len(filtered_metrics)} metrics") # Add select all button for search results col1, col2 = st.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.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.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.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.success(f"Selected {len(selected_metrics)} metrics") # Performance warning for large selections if len(selected_metrics) > 20: st.warning(f"⚠ī¸ Large selection ({len(selected_metrics)} metrics) may impact performance") elif len(selected_metrics) > 50: st.error(f"🚨 Very large selection ({len(selected_metrics)} metrics) - consider reducing for better performance") else: st.warning("No metrics selected") # Metric info expander with st.expander("ℹī¸ 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}`") st.divider() # Visual separator before main content # Main content tabs tab1, tab2, tab3, tab4, tab5 = st.tabs(["📊 Distributions", "🔗 Correlations", "📈 Comparisons", "🔍 Conversation", "đŸŽ¯ 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"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 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") 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}") # 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', []) if conversation_turns: # Display conversation content st.subheader("đŸ’Ŧ Conversation Content") # Show/hide content toggle show_content = st.checkbox("Show conversation content", value=True) if show_content: for i, turn in enumerate(conversation_turns): role = turn.get('role', 'unknown') content = turn.get('content', 'No content') # Style based on role 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 turn-level metrics if available st.subheader("📊 Turn-Level Metrics") if selected_metrics: # Get actual turn-level data for this conversation # Find matching turns in the exploded dataframe conv_turn_metrics = [] # Simple approach: try to match turns by content or position # This is a best-effort approach since exact matching is complex 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] if available_columns: # Try to get metrics for turns from this conversation type type_turns = filtered_df_exploded[filtered_df_exploded['type'] == selected_conversation['type']] # Take a sample of turns for this conversation type (since exact matching is complex) sample_size = min(len(conversation_turns), len(type_turns)) if sample_size > 0: sample_turns = type_turns.head(sample_size) # Create metrics table metrics_display_data = [] for i, (_, turn_row) in enumerate(sample_turns.iterrows()): if i < len(conversation_turns): turn_data = { 'Turn': i + 1, 'Role': conversation_turns[i].get('role', 'unknown') } # Add actual metric values 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)): turn_data[friendly_name] = round(value, 3) else: turn_data[friendly_name] = 'N/A' metrics_display_data.append(turn_data) if metrics_display_data: metrics_df = pd.DataFrame(metrics_display_data) st.dataframe(metrics_df, use_container_width=True) # Plot metrics over turns with real data 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_turns.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.") else: st.info("No matching turn-level metrics found for this conversation.") else: st.info("No turn-level data available for this conversation type.") else: st.warning("No turn-level metrics available in the dataset for the selected metrics.") # Show raw turn content with role highlighting with st.expander("🔍 Detailed Turn Analysis", expanded=False): for i, turn in enumerate(conversation_turns): role = turn.get('role', 'unknown') content = turn.get('content', 'No content') st.markdown(f"**Turn {i+1} ({role}):**") st.text_area( f"Content", content, height=100, key=f"turn_content_{i}", disabled=True ) # Show turn statistics st.caption(f"Characters: {len(content)} | Words: {len(content.split())} | Role: {role}") st.divider() else: st.warning("Select some metrics to see 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()