streamlit_app.py

import streamlit as st
import pandas as pd
import json
import os
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np
from pathlib import Path
import glob
import requests
from io import StringIO
import zipfile
import tempfile
import shutil
import time
from datetime import datetime, timezone

# Set page config
st.set_page_config(
    page_title="Attention Analysis Results Explorer",
    page_icon="🔍",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for better styling
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem;
        font-weight: bold;
        color: #1f77b4;
        text-align: center;
        margin-bottom: 2rem;
    }
    .section-header {
        font-size: 1.5rem;
        font-weight: bold;
        color: #ff7f0e;
        margin-top: 2rem;
        margin-bottom: 1rem;
    }
    .metric-container {
        background-color: #f0f2f6;
        padding: 1rem;
        border-radius: 0.5rem;
        margin: 0.5rem 0;
    }
    .stSelectbox > div > div {
        background-color: white;
    }
</style>
""", unsafe_allow_html=True)

class AttentionResultsExplorer:
    def __init__(self, github_repo="ACMCMC/attention", use_cache=True):
        self.github_repo = github_repo
        self.use_cache = use_cache
        self.cache_dir = Path(tempfile.gettempdir()) / "attention_results_cache"
        self.base_path = self.cache_dir
        
        # Initialize cache directory
        if not self.cache_dir.exists():
            self.cache_dir.mkdir(parents=True, exist_ok=True)
        
        # Get available languages from GitHub without downloading
        self.available_languages = self._get_available_languages_from_github()
        self.relation_types = None
    
    def _get_available_languages_from_github(self):
        """Get available languages from GitHub API without downloading"""
        api_url = f"https://api.github.com/repos/{self.github_repo}/contents"
        
        response = self._make_github_request(api_url, "available languages")
        if response is None:
            # Rate limit hit or other error, fallback to local cache
            return self._get_available_languages_local()
        
        try:
            contents = response.json()
            result_dirs = [item['name'] for item in contents 
                          if item['type'] == 'dir' and item['name'].startswith('results_')]
            
            languages = [d.replace("results_", "") for d in result_dirs]
            return sorted(languages)
            
        except Exception as e:
            st.warning(f"Could not parse language list from GitHub: {str(e)}")
            # Fallback to local cache if available
            return self._get_available_languages_local()
    
    def _get_available_languages_local(self):
        """Get available languages from local cache"""
        if not self.base_path.exists():
            return []
        result_dirs = [d.name for d in self.base_path.iterdir() 
                      if d.is_dir() and d.name.startswith("results_")]
        languages = [d.replace("results_", "") for d in result_dirs]
        return sorted(languages)
    
    def _ensure_specific_data_downloaded(self, language, config, model):
        """Download specific files for a language/config/model combination if not cached"""
        base_path = f"results_{language}/{config}/{model}"
        local_path = self.base_path / f"results_{language}" / config / model
        
        # Check if we already have this specific combination cached
        if local_path.exists() and self.use_cache:
            # Quick check if essential files exist
            metadata_path = local_path / "metadata" / "metadata.json"
            if metadata_path.exists():
                return  # Already have the data
        
        with st.spinner(f"📥 Downloading data for {language.upper()}/{config}/{model}..."):
            try:
                self._download_specific_model_data(language, config, model)
                st.success(f"✅ Downloaded {language.upper()}/{model} data!")
            except Exception as e:
                st.error(f"❌ Failed to download specific data: {str(e)}")
                raise
    
    def _download_specific_model_data(self, language, config, model):
        """Download only the specific model data needed"""
        base_remote_path = f"results_{language}/{config}/{model}"
        
        # List of essential directories to download for a model
        essential_dirs = ["metadata", "uas_scores", "number_of_heads_matching", "variability", "figures"]
        
        for dir_name in essential_dirs:
            remote_path = f"{base_remote_path}/{dir_name}"
            try:
                self._download_directory_targeted(dir_name, remote_path, language, config, model)
            except Exception as e:
                st.warning(f"Could not download {dir_name} for {model}: {str(e)}")
    
    def _download_directory_targeted(self, dir_name, remote_path, language, config, model):
        """Download a specific directory for a model"""
        api_url = f"https://api.github.com/repos/{self.github_repo}/contents/{remote_path}"
        
        response = self._make_github_request(api_url, f"directory {dir_name}", silent_404=True)
        if response is None:
            return  # Rate limit, 404, or other error
        
        try:
            contents = response.json()
            
            # Create local directory
            local_dir = self.base_path / f"results_{language}" / config / model / dir_name
            local_dir.mkdir(parents=True, exist_ok=True)
            
            # Download all files in this directory
            for item in contents:
                if item['type'] == 'file':
                    self._download_file(item, local_dir)
                    
        except Exception as e:
            st.warning(f"Could not download directory {dir_name}: {str(e)}")
    
    def _get_available_configs_from_github(self, language):
        """Get available configurations for a language from GitHub"""
        api_url = f"https://api.github.com/repos/{self.github_repo}/contents/results_{language}"
        
        response = self._make_github_request(api_url, f"configurations for {language}")
        if response is None:
            return []
        
        try:
            contents = response.json()
            configs = [item['name'] for item in contents if item['type'] == 'dir']
            return sorted(configs)
            
        except Exception as e:
            st.warning(f"Could not parse configurations for {language}: {str(e)}")
            return []
    
    def _discover_config_parameters(self, language=None):
        """Dynamically discover configuration parameters from available configs
        
        For performance optimization, we only inspect the first language since
        configurations are consistent across all languages and models.
        """
        try:
            # Use first available language if none specified (optimization)
            if language is None:
                if not self.available_languages:
                    return {}
                language = self.available_languages[0]
            
            available_configs = self._get_experimental_configs(language)
            if not available_configs:
                return {}
            
            # Parse all configurations to extract unique parameters
            all_params = set()
            param_values = {}
            
            for config in available_configs:
                params = self._parse_config_params(config)
                for param, value in params.items():
                    all_params.add(param)
                    if param not in param_values:
                        param_values[param] = set()
                    param_values[param].add(value)
            
            # Convert sets to sorted lists for consistent UI
            return {param: sorted(list(values)) for param, values in param_values.items()}
            
        except Exception as e:
            st.warning(f"Could not discover configuration parameters: {str(e)}")
            return {}
    
    def _build_config_from_params(self, param_dict):
        """Build configuration string from parameter dictionary"""
        config_parts = []
        for param, value in sorted(param_dict.items()):
            config_parts.append(f"{param}_{value}")
        return "+".join(config_parts)
    
    def _find_best_matching_config(self, language, target_params):
        """Find the configuration that best matches the target parameters"""
        available_configs = self._get_experimental_configs(language)
        
        best_match = None
        best_score = -1
        
        for config in available_configs:
            config_params = self._parse_config_params(config)
            
            # Calculate match score
            score = 0
            total_params = len(target_params)
            
            for param, target_value in target_params.items():
                if param in config_params and config_params[param] == target_value:
                    score += 1
            
            # Prefer configs with exact parameter count
            if len(config_params) == total_params:
                score += 0.5
            
            if score > best_score:
                best_score = score
                best_match = config
        
        return best_match, best_score == len(target_params)
    
    def _download_repository(self):
        """Download repository data from GitHub"""
        st.info("🔄 Downloading results data from GitHub... This may take a moment.")
        
        # GitHub API to get the repository contents
        api_url = f"https://api.github.com/repos/{self.github_repo}/contents"
        
        try:
            # Get list of result directories
            response = requests.get(api_url)
            response.raise_for_status()
            contents = response.json()
            
            result_dirs = [item['name'] for item in contents 
                          if item['type'] == 'dir' and item['name'].startswith('results_')]
            
            st.write(f"Found {len(result_dirs)} result directories: {', '.join(result_dirs)}")
            
            # Download each result directory
            progress_bar = st.progress(0)
            for i, result_dir in enumerate(result_dirs):
                st.write(f"Downloading {result_dir}...")
                self._download_directory(result_dir)
                progress_bar.progress((i + 1) / len(result_dirs))
            
            st.success("✅ Download completed!")
            
        except Exception as e:
            st.error(f"❌ Error downloading repository: {str(e)}")
            st.error("Please check the repository URL and your internet connection.")
            raise
    
    def _parse_config_params(self, config_name):
        """Parse configuration parameters into a dictionary"""
        parts = config_name.split('+')
        params = {}
        for part in parts:
            if '_' in part:
                key_parts = part.split('_')
                if len(key_parts) >= 2:
                    key = '_'.join(key_parts[:-1])
                    value = key_parts[-1]
                    params[key] = value == 'True'
        return params
    
    def _download_directory(self, dir_name, path=""):
        """Recursively download a directory from GitHub"""
        url = f"https://api.github.com/repos/{self.github_repo}/contents/{path}{dir_name}"
        
        try:
            response = requests.get(url)
            response.raise_for_status()
            contents = response.json()
            
            local_dir = self.cache_dir / path / dir_name
            local_dir.mkdir(parents=True, exist_ok=True)
            
            for item in contents:
                if item['type'] == 'file':
                    self._download_file(item, local_dir)
                elif item['type'] == 'dir':
                    self._download_directory(item['name'], f"{path}{dir_name}/")
                    
        except Exception as e:
            st.warning(f"Could not download {dir_name}: {str(e)}")
    
    def _download_file(self, file_info, local_dir):
        """Download a single file from GitHub"""
        try:
            # Use the rate limit handling for file downloads too
            file_response = self._make_github_request(file_info['download_url'], f"file {file_info['name']}")
            if file_response is None:
                return  # Rate limit or other error
            
            # Save to local cache
            local_file = local_dir / file_info['name']
            
            # Handle different file types
            if file_info['name'].endswith(('.csv', '.json')):
                with open(local_file, 'w', encoding='utf-8') as f:
                    f.write(file_response.text)
            else:  # Binary files like PDFs
                with open(local_file, 'wb') as f:
                    f.write(file_response.content)
                    
        except Exception as e:
            st.warning(f"Could not download file {file_info['name']}: {str(e)}")
        
    def _get_available_languages(self):
        """Get all available language directories"""
        return self.available_languages
    
    def _get_experimental_configs(self, language):
        """Get all experimental configurations for a language from GitHub API"""
        api_url = f"https://api.github.com/repos/{self.github_repo}/contents/results_{language}"
        response = self._make_github_request(api_url, f"experimental configs for {language}")
        
        if response is not None:
            try:
                contents = response.json()
                configs = [item['name'] for item in contents if item['type'] == 'dir']
                return sorted(configs)
            except Exception as e:
                st.warning(f"Could not parse experimental configs for {language}: {str(e)}")
        
        # Fallback to local cache if available
        lang_dir = self.base_path / f"results_{language}"
        if lang_dir.exists():
            configs = [d.name for d in lang_dir.iterdir() if d.is_dir()]
            return sorted(configs)
        return []
    
    def _find_matching_config(self, language, target_params):
        """Find the first matching configuration from target parameters"""
        return self._find_best_matching_config(language, target_params)
    
    def _get_models(self, language, config):
        """Get all models for a language and configuration from GitHub API"""
        api_url = f"https://api.github.com/repos/{self.github_repo}/contents/results_{language}/{config}"
        response = self._make_github_request(api_url, f"models for {language}/{config}")
        
        if response is not None:
            try:
                contents = response.json()
                models = [item['name'] for item in contents if item['type'] == 'dir']
                return sorted(models)
            except Exception as e:
                st.warning(f"Could not parse models for {language}/{config}: {str(e)}")
        
        # Fallback to local cache if available
        config_dir = self.base_path / f"results_{language}" / config
        if config_dir.exists():
            models = [d.name for d in config_dir.iterdir() if d.is_dir()]
            return sorted(models)
        return []
    
    def _parse_config_name(self, config_name):
        """Parse configuration name into readable format"""
        parts = config_name.split('+')
        config_dict = {}
        for part in parts:
            if '_' in part:
                key, value = part.split('_', 1)
                config_dict[key.replace('_', ' ').title()] = value
        return config_dict
    
    def _load_metadata(self, language, config, model):
        """Load metadata for a specific combination"""
        # Ensure we have the specific data downloaded
        self._ensure_specific_data_downloaded(language, config, model)
        
        metadata_path = self.base_path / f"results_{language}" / config / model / "metadata" / "metadata.json"
        if metadata_path.exists():
            with open(metadata_path, 'r') as f:
                return json.load(f)
        return None
    
    def _load_uas_scores(self, language, config, model):
        """Load UAS scores data"""
        # Ensure we have the specific data downloaded
        self._ensure_specific_data_downloaded(language, config, model)
        
        uas_dir = self.base_path / f"results_{language}" / config / model / "uas_scores"
        if not uas_dir.exists():
            return {}
        
        uas_data = {}
        csv_files = list(uas_dir.glob("uas_*.csv"))
        
        if csv_files:
            with st.spinner("Loading UAS scores data..."):
                progress_bar = st.progress(0)
                status_text = st.empty()
                
                for i, csv_file in enumerate(csv_files):
                    relation = csv_file.stem.replace("uas_", "")
                    status_text.text(f"Loading UAS data: {relation}")
                    
                    try:
                        df = pd.read_csv(csv_file, index_col=0)
                        uas_data[relation] = df
                    except Exception as e:
                        st.warning(f"Could not load {csv_file.name}: {e}")
                    
                    progress_bar.progress((i + 1) / len(csv_files))
                    time.sleep(0.01)  # Small delay for smoother progress
                
                progress_bar.empty()
                status_text.empty()
        
        return uas_data
    
    def _load_head_matching(self, language, config, model):
        """Load head matching data"""
        # Ensure we have the specific data downloaded
        self._ensure_specific_data_downloaded(language, config, model)
        
        heads_dir = self.base_path / f"results_{language}" / config / model / "number_of_heads_matching"
        if not heads_dir.exists():
            return {}
        
        heads_data = {}
        csv_files = list(heads_dir.glob("heads_matching_*.csv"))
        
        if csv_files:
            with st.spinner("Loading head matching data..."):
                progress_bar = st.progress(0)
                status_text = st.empty()
                
                for i, csv_file in enumerate(csv_files):
                    relation = csv_file.stem.replace("heads_matching_", "").replace(f"_{model}", "")
                    status_text.text(f"Loading head matching data: {relation}")
                    
                    try:
                        df = pd.read_csv(csv_file, index_col=0)
                        heads_data[relation] = df
                    except Exception as e:
                        st.warning(f"Could not load {csv_file.name}: {e}")
                    
                    progress_bar.progress((i + 1) / len(csv_files))
                    time.sleep(0.01)  # Small delay for smoother progress
                
                progress_bar.empty()
                status_text.empty()
        
        return heads_data
    
    def _load_variability(self, language, config, model):
        """Load variability data"""
        # Ensure we have the specific data downloaded
        self._ensure_specific_data_downloaded(language, config, model)
        
        var_path = self.base_path / f"results_{language}" / config / model / "variability" / "variability_list.csv"
        if var_path.exists():
            try:
                return pd.read_csv(var_path, index_col=0)
            except Exception as e:
                st.warning(f"Could not load variability data: {e}")
        return None
    
    def _get_available_figures(self, language, config, model):
        """Get all available figure files"""
        # Ensure we have the specific data downloaded
        self._ensure_specific_data_downloaded(language, config, model)
        
        figures_dir = self.base_path / f"results_{language}" / config / model / "figures"
        if not figures_dir.exists():
            return []
        return list(figures_dir.glob("*.pdf"))
    
    def _handle_rate_limit_error(self, response):
        """Handle GitHub API rate limit errors with detailed user feedback"""
        if response.status_code in (403, 429):
            # Check if it's a rate limit error
            if 'rate limit' in response.text.lower() or 'api rate limit' in response.text.lower():
                # Extract rate limit information from headers
                remaining = response.headers.get('x-ratelimit-remaining', 'unknown')
                reset_timestamp = response.headers.get('x-ratelimit-reset')
                limit = response.headers.get('x-ratelimit-limit', 'unknown')
                
                # Calculate reset time
                reset_time_str = "unknown"
                if reset_timestamp:
                    try:
                        reset_time = datetime.fromtimestamp(int(reset_timestamp), tz=timezone.utc)
                        reset_time_str = reset_time.strftime("%Y-%m-%d %H:%M:%S UTC")
                        
                        # Calculate time until reset
                        now = datetime.now(timezone.utc)
                        time_until_reset = reset_time - now
                        minutes_until_reset = int(time_until_reset.total_seconds() / 60)
                        
                        if minutes_until_reset > 0:
                            reset_time_str += f" (in {minutes_until_reset} minutes)"
                    except (ValueError, TypeError):
                        pass
                
                # Display comprehensive rate limit information
                st.error("🚫 **GitHub API Rate Limit Exceeded**")
                
                with st.expander("📊 Rate Limit Details", expanded=True):
                    col1, col2 = st.columns(2)
                    
                    with col1:
                        st.metric("Requests Remaining", remaining)
                        st.metric("Rate Limit", limit)
                    
                    with col2:
                        st.metric("Reset Time", reset_time_str)
                        if reset_timestamp:
                            try:
                                reset_time = datetime.fromtimestamp(int(reset_timestamp), tz=timezone.utc)
                                now = datetime.now(timezone.utc)
                                time_until_reset = reset_time - now
                                if time_until_reset.total_seconds() > 0:
                                    st.metric("Time Until Reset", f"{int(time_until_reset.total_seconds() / 60)} minutes")
                            except (ValueError, TypeError):
                                pass
                
                return True  # Indicates rate limit error was handled
        
        return False  # Not a rate limit error
    
    def _make_github_request(self, url, description="GitHub API request", silent_404=False):
        """Make a GitHub API request with rate limit handling"""
        try:
            # Add GitHub token if available
            headers = {}
            github_token = os.environ.get('GITHUB_TOKEN')
            if github_token:
                headers['Authorization'] = f'token {github_token}'
            
            response = requests.get(url, headers=headers)
            
            # Check for rate limit before raising for status
            if self._handle_rate_limit_error(response):
                return None  # Rate limit handled, return None
            
            # Handle 404 errors silently if requested (for optional directories)
            if response.status_code == 404 and silent_404:
                return None
            
            response.raise_for_status()
            return response
            
        except requests.exceptions.RequestException as e:
            if hasattr(e, 'response') and e.response is not None:
                # Handle 404 silently if requested
                if e.response.status_code == 404 and silent_404:
                    return None
                    
                if not self._handle_rate_limit_error(e.response):
                    st.warning(f"Request failed for {description}: {str(e)}")
            else:
                st.warning(f"Network error for {description}: {str(e)}")
            return None
def main():
    # Title
    st.markdown('<div class="main-header">🔍 Attention Analysis Results Explorer</div>', unsafe_allow_html=True)
    
    # Sidebar for navigation
    st.sidebar.title("🔧 Configuration")
    
    # Cache management section
    st.sidebar.markdown("### 📁 Data Management")
    
    # Initialize explorer
    use_cache = st.sidebar.checkbox("Use cached data", value=True, 
                                   help="Use previously downloaded data if available")
    
    if st.sidebar.button("🔄 Clear Cache", help="Clear all cached data"):
        # Clear cache and re-download
        cache_dir = Path(tempfile.gettempdir()) / "attention_results_cache"
        if cache_dir.exists():
            shutil.rmtree(cache_dir)
            st.sidebar.success("✅ Cache cleared!")
        st.rerun()
    
    # Show cache status
    cache_dir = Path(tempfile.gettempdir()) / "attention_results_cache"
    if cache_dir.exists():
        # Get more detailed cache information
        cached_items = []
        for lang_dir in cache_dir.iterdir():
            if lang_dir.is_dir() and lang_dir.name.startswith("results_"):
                lang = lang_dir.name.replace("results_", "")
                configs = [d.name for d in lang_dir.iterdir() if d.is_dir()]
                if configs:
                    models_count = 0
                    for config_dir in lang_dir.iterdir():
                        if config_dir.is_dir():
                            models = [d.name for d in config_dir.iterdir() if d.is_dir()]
                            models_count += len(models)
                    cached_items.append(f"{lang} ({len(configs)} configs, {models_count} models)")
        
        if cached_items:
            st.sidebar.success("✅ **Cached Data:**")
            for item in cached_items[:3]:  # Show first 3
                st.sidebar.text(f"• {item}")
            if len(cached_items) > 3:
                st.sidebar.text(f"... and {len(cached_items) - 3} more")
        else:
            st.sidebar.info("📥 Cache exists but empty")
    else:
        st.sidebar.info("📥 No cached data")
    
    st.sidebar.markdown("---")
    
    # Initialize explorer with error handling
    try:
        with st.spinner("🔄 Initializing attention analysis explorer..."):
            explorer = AttentionResultsExplorer(use_cache=use_cache)
    except Exception as e:
        st.error(f"❌ Failed to initialize data explorer: {str(e)}")
        st.error("Please check your internet connection and try again.")
        
        # Show some debugging information
        with st.expander("🔍 Debugging Information"):
            st.code(f"Error details: {str(e)}")
            st.markdown("**Possible solutions:**")
            st.markdown("- Check your internet connection")
            st.markdown("- Try clearing the cache")
            st.markdown("- Wait a moment and refresh the page")
        return
    
    # Check if any languages are available
    if not explorer.available_languages:
        st.error("❌ No result data found. Please check the GitHub repository.")
        st.markdown("**Expected repository structure:**")
        st.markdown("- Repository should contain `results_*` directories")
        st.markdown("- Each directory should contain experimental configurations")
        return
    
    # Show success message
    st.sidebar.success(f"✅ Found {len(explorer.available_languages)} languages: {', '.join(explorer.available_languages)}")
    
    # Language selection
    selected_language = st.sidebar.selectbox(
        "Select Language",
        options=explorer.available_languages,
        help="Choose the language dataset to explore"
    )
    
    st.sidebar.markdown("---")
    
    # Configuration selection with dynamic discovery
    st.sidebar.markdown("### ⚙️ Experimental Configuration")
    
    # Discover available configuration parameters (optimized to use first language only)
    with st.spinner("🔍 Discovering configuration options..."):
        config_parameters = explorer._discover_config_parameters()
    
    if not config_parameters:
        st.sidebar.error("❌ Could not discover configuration parameters")
        st.stop()
    
    # Show discovered parameters
    st.sidebar.success(f"✅ Found {len(config_parameters)} configuration parameters")
    st.sidebar.info("💡 Configuration options are consistent across all languages - using optimized discovery")
    
    # Create UI elements for each discovered parameter
    selected_params = {}
    
    for param_name, possible_values in config_parameters.items():
        # Clean up parameter name for display
        display_name = param_name.replace('_', ' ').title()
        
        if len(possible_values) == 2 and set(possible_values) == {True, False}:
            # Boolean parameter - use checkbox
            default_value = False  # Default to False for boolean params
            selected_params[param_name] = st.sidebar.checkbox(
                display_name,
                value=default_value,
                help=f"Parameter: {param_name}"
            )
        else:
            # Multi-value parameter - use selectbox
            selected_params[param_name] = st.sidebar.selectbox(
                display_name,
                options=possible_values,
                help=f"Parameter: {param_name}"
            )
    
    # Find the best matching configuration
    selected_config, config_exists = explorer._find_matching_config(selected_language, selected_params)
    
    # Show current configuration
    st.sidebar.markdown("**Selected Parameters:**")
    for param, value in selected_params.items():
        emoji = "✅" if value else "❌" if isinstance(value, bool) else "🔹"
        st.sidebar.text(f"{emoji} {param}: {value}")
    
    st.sidebar.markdown("**Matched Configuration:**")
    st.sidebar.code(selected_config if selected_config else "No match found", language="text")
    
    # Show configuration status
    if config_exists:
        st.sidebar.success("✅ Exact configuration match found!")
    else:
        st.sidebar.warning("⚠️ Using best available match")
    
    st.sidebar.markdown("---")
    
    # Get models for selected language and config
    if not selected_config:
        st.error("❌ No valid configuration found")
        st.info("Please try different parameter combinations.")
        st.stop()
    
    models = explorer._get_models(selected_language, selected_config)
    if not models:
        st.warning(f"❌ No models found for {selected_language}/{selected_config}")
        st.info("This configuration may not exist for the selected language. Try adjusting the configuration parameters above.")
        st.stop()
    
    # Model selection
    selected_model = st.sidebar.selectbox(
        "Select Model",
        options=models,
        help="Choose the model to analyze"
    )
    
    # Main content area
    tab1, tab2, tab3, tab4, tab5 = st.tabs([
        "📊 Overview", 
        "🎯 UAS Scores", 
        "🧠 Head Matching", 
        "📈 Variability", 
        "🖼️ Figures"
    ])
    
    # Tab 1: Overview
    with tab1:
        st.markdown('<div class="section-header">Experiment Overview</div>', unsafe_allow_html=True)
        
        # Show current configuration in a friendly format
        st.markdown("### 🔧 Current Configuration")
        config_params = explorer._parse_config_params(selected_config)
        
        col1, col2 = st.columns(2)
        with col1:
            st.markdown("**Configuration Parameters:**")
            for param, value in config_params.items():
                emoji = "✅" if value else "❌" if isinstance(value, bool) else "🔹"
                readable_param = param.replace('_', ' ').title()
                st.markdown(f"{emoji} **{readable_param}**: {value}")
        
        with col2:
            st.markdown("**Selected Parameters vs Actual:**")
            for param in selected_params:
                selected_val = selected_params[param]
                actual_val = config_params.get(param, "N/A")
                match_emoji = "✅" if selected_val == actual_val else "⚠️"
                st.markdown(f"{match_emoji} **{param}**: {selected_val} → {actual_val}")
            
            st.markdown("**Raw Configuration String:**")
            st.code(selected_config, language="text")
        
        st.markdown("---")
        
        # Load metadata
        metadata = explorer._load_metadata(selected_language, selected_config, selected_model)
        if metadata:
            st.markdown("### 📊 Experiment Statistics")
            col1, col2, col3, col4 = st.columns(4)
            with col1:
                st.metric("Total Samples", metadata.get('total_number', 'N/A'))
            with col2:
                st.metric("Processed Correctly", metadata.get('number_processed_correctly', 'N/A'))
            with col3:
                st.metric("Errors", metadata.get('number_errored', 'N/A'))
            with col4:
                success_rate = (metadata.get('number_processed_correctly', 0) / 
                               metadata.get('total_number', 1)) * 100 if metadata.get('total_number') else 0
                st.metric("Success Rate", f"{success_rate:.1f}%")
            
            if metadata.get('random_seed'):
                st.markdown(f"**Random Seed:** {metadata.get('random_seed')}")
            
            if metadata.get('errored_phrases'):
                with st.expander("🔍 View Errored Phrase IDs"):
                    st.write(metadata['errored_phrases'])
        else:
            st.warning("No metadata available for this configuration.")
        
        # Quick stats about available data
        st.markdown("---")
        st.markdown('<div class="section-header">Available Data Summary</div>', unsafe_allow_html=True)
        
        # Show loading message since we're now loading on-demand
        with st.spinner("Loading data summary..."):
            uas_data = explorer._load_uas_scores(selected_language, selected_config, selected_model)
            heads_data = explorer._load_head_matching(selected_language, selected_config, selected_model)
            variability_data = explorer._load_variability(selected_language, selected_config, selected_model)
            figures = explorer._get_available_figures(selected_language, selected_config, selected_model)
        
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            st.metric("UAS Relations", len(uas_data))
        with col2:
            st.metric("Head Matching Relations", len(heads_data))
        with col3:
            st.metric("Variability Data", "✓" if variability_data is not None else "✗")
        with col4:
            st.metric("Figure Files", len(figures))
        
        # Show what was just downloaded
        if uas_data or heads_data or variability_data is not None or figures:
            st.success(f"✅ Successfully loaded data for {selected_language.upper()}/{selected_model}")
        else:
            st.warning("⚠️ No data files found for this configuration")
    
    # Tab 2: UAS Scores
    with tab2:
        st.markdown('<div class="section-header">UAS (Unlabeled Attachment Score) Analysis</div>', unsafe_allow_html=True)
        
        uas_data = explorer._load_uas_scores(selected_language, selected_config, selected_model)
        
        if uas_data:
            # Relation selection
            selected_relation = st.selectbox(
                "Select Dependency Relation",
                options=list(uas_data.keys()),
                help="Choose a dependency relation to visualize UAS scores"
            )
            
            if selected_relation and selected_relation in uas_data:
                df = uas_data[selected_relation]
                
                # Display the data table
                st.markdown("**UAS Scores Matrix (Layer × Head)**")
                st.dataframe(df, use_container_width=True)
                
                # Create heatmap
                fig = px.imshow(
                    df.values,
                    x=[f"Head {i}" for i in df.columns],
                    y=[f"Layer {i}" for i in df.index],
                    color_continuous_scale="Viridis",
                    title=f"UAS Scores Heatmap - {selected_relation}",
                    labels=dict(color="UAS Score")
                )
                fig.update_layout(height=600)
                st.plotly_chart(fig, use_container_width=True)
                
                # Statistics
                st.markdown("**Statistics**")
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    st.metric("Max Score", f"{df.values.max():.4f}")
                with col2:
                    st.metric("Min Score", f"{df.values.min():.4f}")
                with col3:
                    st.metric("Mean Score", f"{df.values.mean():.4f}")
                with col4:
                    st.metric("Std Dev", f"{df.values.std():.4f}")
        else:
            st.warning("No UAS score data available for this configuration.")
    
    # Tab 3: Head Matching
    with tab3:
        st.markdown('<div class="section-header">Attention Head Matching Analysis</div>', unsafe_allow_html=True)
        
        heads_data = explorer._load_head_matching(selected_language, selected_config, selected_model)
        
        if heads_data:
            # Relation selection
            selected_relation = st.selectbox(
                "Select Dependency Relation",
                options=list(heads_data.keys()),
                help="Choose a dependency relation to visualize head matching patterns",
                key="heads_relation"
            )
            
            if selected_relation and selected_relation in heads_data:
                df = heads_data[selected_relation]
                
                # Display the data table
                st.markdown("**Head Matching Counts Matrix (Layer × Head)**")
                st.dataframe(df, use_container_width=True)
                
                # Create heatmap
                fig = px.imshow(
                    df.values,
                    x=[f"Head {i}" for i in df.columns],
                    y=[f"Layer {i}" for i in df.index],
                    color_continuous_scale="Blues",
                    title=f"Head Matching Counts - {selected_relation}",
                    labels=dict(color="Match Count")
                )
                fig.update_layout(height=600)
                st.plotly_chart(fig, use_container_width=True)
                
                # Create bar chart of total matches per layer
                layer_totals = df.sum(axis=1)
                fig_bar = px.bar(
                    x=layer_totals.index,
                    y=layer_totals.values,
                    title=f"Total Matches per Layer - {selected_relation}",
                    labels={"x": "Layer", "y": "Total Matches"}
                )
                fig_bar.update_layout(height=400)
                st.plotly_chart(fig_bar, use_container_width=True)
                
                # Statistics
                st.markdown("**Statistics**")
                col1, col2, col3, col4 = st.columns(4)
                with col1:
                    st.metric("Total Matches", int(df.values.sum()))
                with col2:
                    st.metric("Max per Cell", int(df.values.max()))
                with col3:
                    best_layer = layer_totals.idxmax()
                    st.metric("Best Layer", f"Layer {best_layer}")
                with col4:
                    best_head_idx = np.unravel_index(df.values.argmax(), df.values.shape)
                    st.metric("Best Head", f"L{best_head_idx[0]}-H{best_head_idx[1]}")
        else:
            st.warning("No head matching data available for this configuration.")
    
    # Tab 4: Variability
    with tab4:
        st.markdown('<div class="section-header">Attention Variability Analysis</div>', unsafe_allow_html=True)
        
        variability_data = explorer._load_variability(selected_language, selected_config, selected_model)
        
        if variability_data is not None:
            # Display the data table
            st.markdown("**Variability Matrix (Layer × Head)**")
            st.dataframe(variability_data, use_container_width=True)
            
            # Create heatmap
            fig = px.imshow(
                variability_data.values,
                x=[f"Head {i}" for i in variability_data.columns],
                y=[f"Layer {i}" for i in variability_data.index],
                color_continuous_scale="Reds",
                title="Attention Variability Heatmap",
                labels=dict(color="Variability Score")
            )
            fig.update_layout(height=600)
            st.plotly_chart(fig, use_container_width=True)
            
            # Create line plot for variability trends
            fig_line = go.Figure()
            for col in variability_data.columns:
                fig_line.add_trace(go.Scatter(
                    x=variability_data.index,
                    y=variability_data[col],
                    mode='lines+markers',
                    name=f'Head {col}',
                    line=dict(width=2)
                ))
            
            fig_line.update_layout(
                title="Variability Trends Across Layers",
                xaxis_title="Layer",
                yaxis_title="Variability Score",
                height=500
            )
            st.plotly_chart(fig_line, use_container_width=True)
            
            # Statistics
            st.markdown("**Statistics**")
            col1, col2, col3, col4 = st.columns(4)
            with col1:
                st.metric("Max Variability", f"{variability_data.values.max():.4f}")
            with col2:
                st.metric("Min Variability", f"{variability_data.values.min():.4f}")
            with col3:
                st.metric("Mean Variability", f"{variability_data.values.mean():.4f}")
            with col4:
                most_variable_idx = np.unravel_index(variability_data.values.argmax(), variability_data.values.shape)
                st.metric("Most Variable", f"L{most_variable_idx[0]}-H{most_variable_idx[1]}")
        else:
            st.warning("No variability data available for this configuration.")
    
    # Tab 5: Figures
    with tab5:
        st.markdown('<div class="section-header">Generated Figures</div>', unsafe_allow_html=True)
        
        figures = explorer._get_available_figures(selected_language, selected_config, selected_model)
        
        if figures:
            st.markdown(f"**Available Figures: {len(figures)}**")
            
            # Group figures by relation type
            figure_groups = {}
            for fig_path in figures:
                # Extract relation from filename
                filename = fig_path.stem
                relation = filename.replace("heads_matching_", "").replace(f"_{selected_model}", "")
                if relation not in figure_groups:
                    figure_groups[relation] = []
                figure_groups[relation].append(fig_path)
            
            # Select relation to view
            selected_fig_relation = st.selectbox(
                "Select Relation for Figure View",
                options=list(figure_groups.keys()),
                help="Choose a dependency relation to view its figure"
            )
            
            if selected_fig_relation and selected_fig_relation in figure_groups:
                fig_path = figure_groups[selected_fig_relation][0]
                
                st.markdown(f"**Figure: {fig_path.name}**")
                st.markdown(f"**Path:** `{fig_path}`")
                
                # Note about PDF viewing
                st.info(
                    "📄 PDF figures are available in the results directory. "
                    "Due to Streamlit limitations, PDF files cannot be displayed directly in the browser. "
                    "You can download or view them locally."
                )
                
                # Provide download link
                try:
                    with open(fig_path, "rb") as file:
                        st.download_button(
                            label=f"📥 Download {fig_path.name}",
                            data=file.read(),
                            file_name=fig_path.name,
                            mime="application/pdf"
                        )
                except Exception as e:
                    st.error(f"Could not load figure: {e}")
            
            # List all available figures
            st.markdown("**All Available Figures:**")
            for relation, paths in figure_groups.items():
                with st.expander(f"📊 {relation} ({len(paths)} files)"):
                    for path in paths:
                        st.markdown(f"- `{path.name}`")
        else:
            st.warning("No figures available for this configuration.")
    
    # Footer
    st.markdown("---")
    
    # Data source information
    col1, col2 = st.columns([2, 1])
    with col1:
        st.markdown(
            "🔬 **Attention Analysis Results Explorer** | "
            f"Currently viewing: {selected_language.upper()} - {selected_model} | "
            "Built with Streamlit"
        )
    with col2:
        st.markdown(
            f"📊 **Data Source**: [GitHub Repository](https://github.com/{explorer.github_repo})"
        )

if __name__ == "__main__":
    main()