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

# 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)
        
        # Download and cache data if needed
        if not self._cache_exists() or not use_cache:
            self._download_repository()
        
        self.languages = self._get_available_languages()
        self.relation_types = None
    
    def _cache_exists(self):
        """Check if cached data exists"""
        return (self.cache_dir / "results_en").exists()
    
    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 _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:
            # Download file content
            response = requests.get(file_info['download_url'])
            response.raise_for_status()
            
            # 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(response.text)
            else:  # Binary files like PDFs
                with open(local_file, 'wb') as f:
                    f.write(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"""
        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 _get_experimental_configs(self, language):
        """Get all experimental configurations for a language"""
        lang_dir = self.base_path / f"results_{language}"
        if not lang_dir.exists():
            return []
        configs = [d.name for d in lang_dir.iterdir() if d.is_dir()]
        return sorted(configs)
    
    def _get_models(self, language, config):
        """Get all models for a language and configuration"""
        config_dir = self.base_path / f"results_{language}" / config
        if not config_dir.exists():
            return []
        models = [d.name for d in config_dir.iterdir() if d.is_dir()]
        return sorted(models)
    
    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"""
        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"""
        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:
            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))
            
            progress_bar.empty()
            status_text.empty()
        
        return uas_data
    
    def _load_head_matching(self, language, config, model):
        """Load head matching data"""
        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:
            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))
            
            progress_bar.empty()
            status_text.empty()
        
        return heads_data
    
    def _load_variability(self, language, config, model):
        """Load variability data"""
        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"""
        figures_dir = self.base_path / f"results_{language}" / config / model / "figures"
        if not figures_dir.exists():
            return []
        return list(figures_dir.glob("*.pdf"))

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("🔄 Refresh Data", help="Download fresh data from GitHub"):
        # Clear cache and re-download
        cache_dir = Path(tempfile.gettempdir()) / "attention_results_cache"
        if cache_dir.exists():
            shutil.rmtree(cache_dir)
        st.rerun()
    
    # Show cache status
    cache_dir = Path(tempfile.gettempdir()) / "attention_results_cache"
    if cache_dir.exists():
        st.sidebar.success("✅ Data cached locally")
    else:
        st.sidebar.info("📥 Will download data from GitHub")
    
    st.sidebar.markdown("---")
    
    # Initialize explorer with error handling
    try:
        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.")
        return
    
    # Check if any languages are available
    if not explorer.languages:
        st.error("❌ No result data found. Please check the GitHub repository.")
        return
    
    # Language selection
    selected_language = st.sidebar.selectbox(
        "Select Language",
        options=explorer.languages,
        help="Choose the language dataset to explore"
    )
    
    # Get configurations for selected language
    configs = explorer._get_experimental_configs(selected_language)
    if not configs:
        st.error(f"No configurations found for language: {selected_language}")
        return
    
    # Configuration selection
    selected_config = st.sidebar.selectbox(
        "Select Experimental Configuration",
        options=configs,
        help="Choose the experimental configuration"
    )
    
    # Parse and display configuration details
    config_details = explorer._parse_config_name(selected_config)
    st.sidebar.markdown("**Configuration Details:**")
    for key, value in config_details.items():
        st.sidebar.markdown(f"- **{key}**: {value}")
    
    # Get models for selected language and config
    models = explorer._get_models(selected_language, selected_config)
    if not models:
        st.error(f"No models found for {selected_language}/{selected_config}")
        return
    
    # 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)
        
        # Load metadata
        metadata = explorer._load_metadata(selected_language, selected_config, selected_model)
        if metadata:
            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}%")
            
            st.markdown("**Random Seed:**", metadata.get('random_seed', 'N/A'))
            
            if metadata.get('errored_phrases'):
                st.markdown("**Errored Phrase IDs:**")
                st.write(metadata['errored_phrases'])
        else:
            st.warning("No metadata available for this configuration.")
        
        # Quick stats about available data
        st.markdown('<div class="section-header">Available Data</div>', unsafe_allow_html=True)
        
        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))
    
    # 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()