app_old.py

"""
RNA Motif Structure Comparison Tool
Streamlit app for comparing RNA motif structures with flexible residue selection
"""

import streamlit as st
import numpy as np
import pandas as pd
from pathlib import Path
import io
import tempfile
import os

# Import our RMSD calculation functions
from rmsd_utils import (
    parse_residue_atoms,
    get_backbone_sugar_and_selectbase_coords_fixed,
    calculate_COM,
    calculate_rotation_rmsd,
    translate_rotate_coords
)

from visualization import create_structure_visualization

# Page configuration
st.set_page_config(
    page_title="RNA Motif Structure Comparison",
    page_icon="🧬",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem;
        font-weight: bold;
        color: #1f77b4;
        margin-bottom: 1rem;
    }
    .sub-header {
        font-size: 1.2rem;
        color: #666;
        margin-bottom: 2rem;
    }
    .metric-box {
        background-color: #f0f2f6;
        padding: 1rem;
        border-radius: 0.5rem;
        margin: 0.5rem 0;
    }
</style>
""", unsafe_allow_html=True)


def save_uploaded_file(uploaded_file, directory):
    """Save an uploaded file to a temporary directory"""
    file_path = os.path.join(directory, uploaded_file.name)
    with open(file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
    return file_path


def get_structure_info(pdb_path):
    """
    Get information about a structure's residues.
    
    Args:
        pdb_path: Path to PDB file
    
    Returns:
        List of dicts with residue info: [{index, resnum, resname, full_name}, ...]
    """
    residues = parse_residue_atoms(pdb_path)
    
    structure_info = []
    for idx, res in enumerate(residues):
        structure_info.append({
            'index': idx,
            'resnum': res['resnum'],
            'resname': res['resname'],
            'full_name': f"{idx+1}. {res['resname']} (residue #{res['resnum']})"
        })
    
    return structure_info


def display_structure_selector(files, temp_dir, set_name):
    """
    Display structure information and allow users to select residues.
    
    Args:
        files: List of uploaded files
        temp_dir: Temporary directory containing files
        set_name: Name of the set (e.g., "Reference" or "Query")
    
    Returns:
        Dict mapping filename to list of selected residue indices
    """
    if not files:
        return {}
    
    st.subheader(f"📋 {set_name} Structure Preview & Selection")
    
    selections = {}
    
    for file in files:
        file_path = os.path.join(temp_dir, file.name)
        structure_info = get_structure_info(file_path)
        
        with st.expander(f"🔍 {file.name} ({len(structure_info)} residues)"):
            # Display residue table
            info_df = pd.DataFrame(structure_info)[['index', 'resnum', 'resname']]
            info_df.columns = ['Index (0-based)', 'Residue Number', 'Base Type']
            info_df['Index (1-based)'] = info_df['Index (0-based)'] + 1
            info_df = info_df[['Index (1-based)', 'Index (0-based)', 'Residue Number', 'Base Type']]
            
            st.dataframe(info_df, use_container_width=True, height=min(300, len(structure_info) * 35 + 38))
            
            # Selection method
            selection_method = st.radio(
                f"Selection method for {file.name}",
                ["Select by range", "Select specific residues", "Use all residues"],
                key=f"method_{set_name}_{file.name}",
                horizontal=True
            )
            
            selected_indices = []
            
            if selection_method == "Select by range":
                col1, col2 = st.columns(2)
                with col1:
                    start_idx = st.number_input(
                        "Start index (1-based)",
                        min_value=1,
                        max_value=len(structure_info),
                        value=1,
                        key=f"start_{set_name}_{file.name}"
                    )
                with col2:
                    end_idx = st.number_input(
                        "End index (1-based, inclusive)",
                        min_value=1,
                        max_value=len(structure_info),
                        value=min(4, len(structure_info)),
                        key=f"end_{set_name}_{file.name}"
                    )
                
                if start_idx <= end_idx:
                    selected_indices = list(range(start_idx - 1, end_idx))
                    st.info(f"✓ Selected residues: {[i+1 for i in selected_indices]}")
                else:
                    st.error("Start index must be ≤ end index")
                    
            elif selection_method == "Select specific residues":
                # Multi-select for specific residues
                selected_names = st.multiselect(
                    "Select residues",
                    options=[info['full_name'] for info in structure_info],
                    default=[structure_info[i]['full_name'] for i in range(min(4, len(structure_info)))],
                    key=f"specific_{set_name}_{file.name}"
                )
                
                # Map back to indices
                name_to_idx = {info['full_name']: info['index'] for info in structure_info}
                selected_indices = [name_to_idx[name] for name in selected_names]
                selected_indices.sort()
                
                if selected_indices:
                    st.info(f"✓ Selected {len(selected_indices)} residues: {[i+1 for i in selected_indices]}")
                    
            else:  # Use all residues
                selected_indices = list(range(len(structure_info)))
                st.info(f"✓ Using all {len(selected_indices)} residues")
            
            # Show selected residues details
            if selected_indices:
                selected_df = info_df[info_df['Index (0-based)'].isin(selected_indices)]
                st.markdown("**Selected residues:**")
                st.dataframe(selected_df, use_container_width=True)
            
            selections[file.name] = selected_indices
    
    return selections


def save_uploaded_file(uploaded_file, directory):
    """Save an uploaded file to a temporary directory"""
    file_path = os.path.join(directory, uploaded_file.name)
    with open(file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
    return file_path


def extract_window_coords(residues, window_indices):
    """
    Extract coordinates for a specific window of residues.
    
    Args:
        residues: List of all residues
        window_indices: List of indices to extract
    
    Returns:
        numpy array of coordinates
    """
    from rmsd_utils import get_backbone_sugar_coords_from_residue, get_base_coords_from_residue
    
    all_coords = []
    for idx in window_indices:
        if idx < len(residues):
            residue = residues[idx]
            # Get backbone and sugar coordinates
            backbone_coords = get_backbone_sugar_coords_from_residue(residue)
            all_coords.extend(backbone_coords)
            # Get base coordinates
            base_coords = get_base_coords_from_residue(residue)
            all_coords.extend(base_coords)
    
    return np.asarray(all_coords)


def compare_structures_with_selection(reference_files, query_files, ref_selections, query_selections, temp_dir):
    """
    Compare reference and query structures using user-selected residues (direct comparison).
    Only compares structures with matching selection sizes.
    
    Args:
        reference_files: List of reference motif files
        query_files: List of query motif files
        ref_selections: Dict mapping filename to selected residue indices
        query_selections: Dict mapping filename to selected residue indices
        temp_dir: Temporary directory containing files
    
    Returns:
        DataFrame with comparison results
    """
    results = []
    
    # Count valid comparisons
    total_comparisons = 0
    for ref_file in reference_files:
        ref_indices = ref_selections.get(ref_file.name, [])
        if len(ref_indices) < 2:
            continue
        for query_file in query_files:
            query_indices = query_selections.get(query_file.name, [])
            if len(query_indices) < 2:
                continue
            # Only compare if they have the same number of selected residues
            if len(ref_indices) == len(query_indices):
                total_comparisons += 1
    
    if total_comparisons == 0:
        st.error("No valid comparisons found. Ensure selected regions have matching sizes.")
        return pd.DataFrame()
    
    progress_bar = st.progress(0)
    status_text = st.empty()
    
    comparison_count = 0
    
    for ref_file in reference_files:
        ref_name = ref_file.name
        ref_path = os.path.join(temp_dir, ref_name)
        ref_indices = ref_selections.get(ref_name, [])
        
        if len(ref_indices) < 2:
            continue
        
        # Parse reference motif
        ref_residues = parse_residue_atoms(ref_path)
        
        # Extract coordinates for selected residues
        ref_coords = extract_window_coords(ref_residues, ref_indices)
        ref_com = calculate_COM(ref_coords)
        
        # Get residue description
        ref_residue_desc = f"[{','.join([str(i+1) for i in ref_indices])}]"
        ref_sequence = ''.join([ref_residues[i]['resname'] for i in ref_indices if i < len(ref_residues)])
        
        for query_file in query_files:
            query_name = query_file.name
            query_path = os.path.join(temp_dir, query_name)
            query_indices = query_selections.get(query_name, [])
            
            if len(query_indices) < 2:
                continue
            
            # Only compare if same number of residues
            if len(ref_indices) != len(query_indices):
                continue
            
            # Parse query motif
            query_residues = parse_residue_atoms(query_path)
            
            # Extract coordinates for selected residues
            query_coords = extract_window_coords(query_residues, query_indices)
            query_com = calculate_COM(query_coords)
            
            # Get residue description
            query_residue_desc = f"[{','.join([str(i+1) for i in query_indices])}]"
            query_sequence = ''.join([query_residues[i]['resname'] for i in query_indices if i < len(query_residues)])
            
            # Calculate RMSD
            U, RMSD = calculate_rotation_rmsd(ref_coords, query_coords, ref_com, query_com)
            
            if U is None or RMSD is None:
                RMSD = 999.0
                U = np.eye(3)
            
            # Store results
            results.append({
                'Reference': ref_name,
                'Ref_Residues': ref_residue_desc,
                'Ref_Sequence': ref_sequence,
                'Ref_Indices': ref_indices,
                'Query': query_name,
                'Query_Residues': query_residue_desc,
                'Query_Sequence': query_sequence,
                'Query_Indices': query_indices,
                'Num_Residues': len(ref_indices),
                'RMSD': RMSD,
                'Rotation_Matrix': U,
                'Ref_COM': ref_com,
                'Query_COM': query_com,
                'Ref_Path': ref_path,
                'Query_Path': query_path
            })
            
            comparison_count += 1
            progress = comparison_count / total_comparisons
            progress_bar.progress(progress)
            status_text.text(f"Processing: {ref_name}{ref_residue_desc} vs {query_name}{query_residue_desc}")
    
    progress_bar.empty()
    status_text.empty()
    
    return pd.DataFrame(results)


def compare_structures_with_windows(reference_files, query_files, ref_selections, query_selections, 
                                    window_size, window_type, temp_dir):
    """
    Compare reference and query structures using sliding windows on selected residues.
    Allows comparison of different-sized selections.
    
    Args:
        reference_files: List of reference motif files
        query_files: List of query motif files
        ref_selections: Dict mapping filename to selected residue indices
        query_selections: Dict mapping filename to selected residue indices
        window_size: Size of comparison window
        window_type: "contiguous" or "non-contiguous"
        temp_dir: Temporary directory containing files
    
    Returns:
        DataFrame with comparison results
    """
    from itertools import combinations
    
    results = []
    
    def generate_windows_from_selection(selected_indices, win_size, win_type):
        """Generate windows from selected indices"""
        if len(selected_indices) < win_size:
            return []
        
        if win_type == "contiguous":
            windows = []
            for i in range(len(selected_indices) - win_size + 1):
                windows.append(selected_indices[i:i + win_size])
            return windows
        else:  # non-contiguous
            return [list(combo) for combo in combinations(selected_indices, win_size)]
    
    # Count total comparisons
    total_comparisons = 0
    for ref_file in reference_files:
        ref_indices = ref_selections.get(ref_file.name, [])
        ref_windows = generate_windows_from_selection(ref_indices, window_size, window_type)
        if not ref_windows:
            continue
            
        for query_file in query_files:
            query_indices = query_selections.get(query_file.name, [])
            query_windows = generate_windows_from_selection(query_indices, window_size, window_type)
            if not query_windows:
                continue
            total_comparisons += len(ref_windows) * len(query_windows)
    
    if total_comparisons == 0:
        st.error(f"No valid comparisons found. Ensure selected regions have at least {window_size} residues.")
        return pd.DataFrame()
    
    progress_bar = st.progress(0)
    status_text = st.empty()
    comparison_count = 0
    
    for ref_file in reference_files:
        ref_name = ref_file.name
        ref_path = os.path.join(temp_dir, ref_name)
        ref_indices = ref_selections.get(ref_name, [])
        
        # Generate windows from selected residues
        ref_windows = generate_windows_from_selection(ref_indices, window_size, window_type)
        
        if not ref_windows:
            st.warning(f"Skipping {ref_name}: selected {len(ref_indices)} residues, need at least {window_size}")
            continue
        
        # Parse reference motif
        ref_residues = parse_residue_atoms(ref_path)
        
        for ref_window in ref_windows:
            # Extract coordinates for this window
            ref_coords = extract_window_coords(ref_residues, ref_window)
            ref_com = calculate_COM(ref_coords)
            
            # Get descriptions
            ref_window_desc = f"[{','.join([str(i+1) for i in ref_window])}]"
            ref_sequence = ''.join([ref_residues[i]['resname'] for i in ref_window if i < len(ref_residues)])
            
            for query_file in query_files:
                query_name = query_file.name
                query_path = os.path.join(temp_dir, query_name)
                query_indices = query_selections.get(query_name, [])
                
                # Generate windows from selected residues
                query_windows = generate_windows_from_selection(query_indices, window_size, window_type)
                
                if not query_windows:
                    continue
                
                # Parse query motif
                query_residues = parse_residue_atoms(query_path)
                
                for query_window in query_windows:
                    # Extract coordinates for this window
                    query_coords = extract_window_coords(query_residues, query_window)
                    query_com = calculate_COM(query_coords)
                    
                    # Get descriptions
                    query_window_desc = f"[{','.join([str(i+1) for i in query_window])}]"
                    query_sequence = ''.join([query_residues[i]['resname'] for i in query_window if i < len(query_residues)])
                    
                    # Calculate RMSD
                    U, RMSD = calculate_rotation_rmsd(ref_coords, query_coords, ref_com, query_com)
                    
                    if U is None or RMSD is None:
                        RMSD = 999.0
                        U = np.eye(3)
                    
                    # Store results
                    results.append({
                        'Reference': ref_name,
                        'Ref_Residues': ref_window_desc,
                        'Ref_Sequence': ref_sequence,
                        'Ref_Indices': ref_window,
                        'Query': query_name,
                        'Query_Residues': query_window_desc,
                        'Query_Sequence': query_sequence,
                        'Query_Indices': query_window,
                        'Num_Residues': window_size,
                        'RMSD': RMSD,
                        'Rotation_Matrix': U,
                        'Ref_COM': ref_com,
                        'Query_COM': query_com,
                        'Ref_Path': ref_path,
                        'Query_Path': query_path
                    })
                    
                    comparison_count += 1
                    progress = comparison_count / total_comparisons
                    progress_bar.progress(progress)
                    status_text.text(f"Processing: {ref_name}{ref_window_desc} vs {query_name}{query_window_desc}")
    
    progress_bar.empty()
    status_text.empty()
    
    return pd.DataFrame(results)


def main():
    # Header
    st.markdown('<p class="main-header">🧬 RNA Motif Structure Comparison</p>', unsafe_allow_html=True)
    st.markdown('<p class="sub-header">Compare RNA motifs with flexible residue selection</p>', unsafe_allow_html=True)
    
    # Sidebar
    st.sidebar.header("⚙️ Configuration")
    
    # File upload
    st.sidebar.subheader("1️⃣ Upload Structures")
    reference_files = st.sidebar.file_uploader(
        "Upload Reference Motif PDB files (Set A)",
        type=['pdb', 'PDB'],
        accept_multiple_files=True,
        key="reference",
        help="Upload RNA motif structures to use as reference"
    )
    
    query_files = st.sidebar.file_uploader(
        "Upload Query Motif PDB files (Set B)",
        type=['pdb', 'PDB'],
        accept_multiple_files=True,
        key="query",
        help="Upload RNA motif structures to compare against reference"
    )
    
    # Main content area
    if not reference_files or not query_files:
        st.info("👈 Please upload reference and query motif PDB files to begin analysis")
        
        # Show example info
        with st.expander("ℹ️ About this tool"):
            st.markdown("""
            ### Purpose
            This tool compares the 3D structures of RNA motifs with **flexible residue selection** and **multiple comparison modes**.
            
            ### Workflow
            1. **Upload PDB files** for reference and query motifs
            2. **Preview structures** and see all residues in each file
            3. **Select residues** to include in comparison (e.g., exclude stem bases, keep only loop)
            4. **Choose comparison mode**:
               - **Direct comparison**: Compare selected regions directly (must be same size)
               - **Window-based comparison**: Generate windows from selections (handles different sizes)
            5. **Run analysis** using RMSD-based structural alignment
            
            ### Comparison Modes
            
            #### Direct Comparison (Same Size)
            - Compares your exact selections
            - Example: You select 4 loop residues from each structure
            - Result: Direct 4-residue vs 4-residue comparison
            - Best for: When all structures have same-sized regions of interest
            
            #### Window-Based Comparison (Different Sizes)
            - Generates sliding windows from your selections
            - Example: You select 4 loop residues from ref, 6 loop residues from query
            - Set window size to 4
            - Result: Ref's 4 residues compared against all 4-residue windows from query's 6
            - Best for: When structures have different-sized regions but you want to find similar sub-regions
            
            ### Selection Methods
            - **By range**: Select consecutive residues (e.g., residues 3-6 for a tetraloop)
            - **Specific residues**: Pick any combination of residues (e.g., 1,3,5,7)
            - **All residues**: Use the entire structure
            
            ### Method Details
            - RMSD calculated using backbone, sugar, and select base atoms
            - Base atoms mapped: purines (N9,C8,C4) ↔ pyrimidines (N1,C2,C6)
            - Kabsch algorithm for optimal structural alignment
            
            ### Example Use Cases
            
            **Case 1: Extract loops from 2+4+2 structures (Direct)**
            - All structures have 8 residues (2 stem + 4 loop + 2 stem)
            - Select residues 3-6 for all structures (the 4-residue loop)
            - Use "Direct comparison"
            - Result: Compare loop vs loop directly
            
            **Case 2: Compare 4-mer loop vs 6-mer loop (Window-based)**
            - Structure A: Select residues 3-6 (4 loop residues)
            - Structure B: Select residues 2-7 (6 loop residues)
            - Use "Window-based comparison" with window size = 4
            - Result: Structure A compared against 3 windows from Structure B
            
            **Case 3: Find similar regions in different structures (Window-based)**
            - Reference: Select 5 residues of interest
            - Query: Select 10 residues from larger region
            - Use "Window-based comparison" with window size = 5
            - Result: Find which 5-residue window in query best matches reference
            
            ### Output
            - RMSD values for all comparisons
            - Interactive 3D visualization of aligned structures
            - Rotation and translation matrices
            - Sequence information for compared regions
            """)
        
        return
    
    # Create temporary directory for file processing
    temp_dir = tempfile.mkdtemp()
    
    # Save uploaded files
    for file in reference_files:
        save_uploaded_file(file, temp_dir)
    for file in query_files:
        save_uploaded_file(file, temp_dir)
    
    # Display file info
    st.markdown("---")
    col1, col2 = st.columns(2)
    with col1:
        st.metric("Reference Motifs", len(reference_files))
    with col2:
        st.metric("Query Motifs", len(query_files))
    
    # Structure preview and selection
    st.markdown("---")
    
    # Get residue selections for reference and query sets
    ref_selections = display_structure_selector(reference_files, temp_dir, "Reference")
    
    st.markdown("---")
    
    query_selections = display_structure_selector(query_files, temp_dir, "Query")
    
    # Validate selections
    st.markdown("---")
    valid_selections = True
    min_residues = 2
    
    for filename, indices in ref_selections.items():
        if len(indices) < min_residues:
            st.error(f"❌ {filename}: Need at least {min_residues} residues selected, got {len(indices)}")
            valid_selections = False
    
    for filename, indices in query_selections.items():
        if len(indices) < min_residues:
            st.error(f"❌ {filename}: Need at least {min_residues} residues selected, got {len(indices)}")
            valid_selections = False
    
    # Check if all selections have the same number of residues
    ref_lengths = set(len(indices) for indices in ref_selections.values())
    query_lengths = set(len(indices) for indices in query_selections.values())
    all_lengths = ref_lengths.union(query_lengths)
    
    if len(all_lengths) > 1:
        st.warning(f"⚠️ Selected regions have different sizes: {sorted(all_lengths)} residues. Only structures with matching sizes will be compared.")
    
    # Run analysis button
    st.sidebar.markdown("---")
    st.sidebar.subheader("2️⃣ Comparison Method")
    
    comparison_mode = st.sidebar.radio(
        "How to compare structures?",
        ["Direct comparison (same size)", "Window-based comparison (different sizes)"],
        help="""
        Direct: Compare selected regions directly (must have same size)
        Window-based: Generate sliding windows for flexible comparison
        """
    )
    
    window_size = None
    window_type = None
    
    if comparison_mode == "Window-based comparison (different sizes)":
        st.sidebar.markdown("**Window Configuration**")
        
        window_size = st.sidebar.number_input(
            "Window Size",
            min_value=2,
            max_value=20,
            value=4,
            step=1,
            help="Number of residues per comparison window"
        )
        
        window_type = st.sidebar.radio(
            "Window Type",
            ["contiguous", "non-contiguous"],
            help="Contiguous: sliding windows. Non-contiguous: all combinations"
        )
    
    st.sidebar.markdown("---")
    st.sidebar.subheader("3️⃣ Run Analysis")
    
    if st.sidebar.button("🚀 Run Analysis", type="primary", disabled=not valid_selections):
        if not valid_selections:
            st.error("Please fix selection errors before running analysis")
            return
            
        with st.spinner("Analyzing structures..."):
            if comparison_mode == "Direct comparison (same size)":
                results_df = compare_structures_with_selection(
                    reference_files, 
                    query_files, 
                    ref_selections,
                    query_selections,
                    temp_dir
                )
            else:  # Window-based comparison
                results_df = compare_structures_with_windows(
                    reference_files, 
                    query_files, 
                    ref_selections,
                    query_selections,
                    window_size,
                    window_type,
                    temp_dir
                )
            
            # Store results in session state
            st.session_state['results_df'] = results_df
            st.session_state['ref_selections'] = ref_selections
            st.session_state['query_selections'] = query_selections
            st.session_state['comparison_mode'] = comparison_mode
            
            if len(results_df) > 0:
                st.success(f"✅ Analysis complete! {len(results_df)} comparisons performed.")
            else:
                st.warning("⚠️ No comparisons could be performed. Check that structures meet comparison requirements.")
    
    # Display results if available
    if 'results_df' in st.session_state and len(st.session_state['results_df']) > 0:
        results_df = st.session_state['results_df']
        
        # Add RMSD threshold filter
        st.sidebar.markdown("---")
        st.sidebar.subheader("4️⃣ Filter Results")
        rmsd_threshold = st.sidebar.slider(
            "RMSD Threshold (Å)",
            min_value=0.0,
            max_value=5.0,
            value=2.0,
            step=0.1,
            help="Only show results below this RMSD value"
        )
        
        # Show comparison mode
        if 'comparison_mode' in st.session_state:
            mode_display = "Direct" if "Direct" in st.session_state['comparison_mode'] else "Window-based"
            st.sidebar.info(f"**Mode**: {mode_display}")
        
        # Filter by threshold
        filtered_df = results_df[results_df['RMSD'] <= rmsd_threshold].copy()
        
        # Summary statistics
        st.markdown("---")
        st.subheader("📊 Summary Statistics")
        
        col1, col2, col3, col4 = st.columns(4)
        with col1:
            st.metric("Total Comparisons", len(results_df))
        with col2:
            st.metric("Below Threshold", len(filtered_df))
        with col3:
            st.metric("Best RMSD", f"{results_df['RMSD'].min():.3f} Å")
        with col4:
            st.metric("Mean RMSD", f"{results_df['RMSD'].mean():.3f} Å")
        
        # Results table
        st.markdown("---")
        st.subheader("🔍 Comparison Results")
        
        # Prepare display dataframe
        display_df = filtered_df[['Reference', 'Ref_Residues', 'Ref_Sequence', 'Query', 'Query_Residues', 'Query_Sequence', 'Num_Residues', 'RMSD']].copy()
        display_df = display_df.sort_values('RMSD').reset_index(drop=True)
        display_df['RMSD'] = display_df['RMSD'].round(3)
        
        # Display with selection
        st.dataframe(
            display_df,
            use_container_width=True,
            height=300
        )
        
        # Structure selection for visualization
        st.markdown("---")
        st.subheader("🔬 3D Structure Visualization")
        
        if len(filtered_df) > 0:
            # Select a comparison to visualize
            selected_idx = st.selectbox(
                "Select a comparison to visualize:",
                range(len(filtered_df)),
                format_func=lambda i: f"{filtered_df.iloc[i]['Reference']}{filtered_df.iloc[i]['Ref_Residues']} ({filtered_df.iloc[i]['Ref_Sequence']}) vs {filtered_df.iloc[i]['Query']}{filtered_df.iloc[i]['Query_Residues']} ({filtered_df.iloc[i]['Query_Sequence']}) | RMSD: {filtered_df.iloc[i]['RMSD']:.3f} Å"
            )
            
            selected_row = filtered_df.iloc[selected_idx]
            
            # Display RMSD info
            st.info(f"**RMSD: {selected_row['RMSD']:.3f} Å** ({selected_row['Num_Residues']} residues) | Reference: {selected_row['Reference']}{selected_row['Ref_Residues']} ({selected_row['Ref_Sequence']}) | Query: {selected_row['Query']}{selected_row['Query_Residues']} ({selected_row['Query_Sequence']})")
            
            # Create visualization - wider display
            col1, col2, col3 = st.columns([0.5, 4, 0.5])
            
            with col2:
                try:
                    viz_html = create_structure_visualization(
                        selected_row['Ref_Path'],
                        selected_row['Query_Path'],
                        selected_row['Ref_Indices'],
                        selected_row['Query_Indices'],
                        selected_row['Rotation_Matrix'],
                        selected_row['Ref_COM'],
                        selected_row['Query_COM'],
                        selected_row['RMSD']
                    )
                    st.components.v1.html(viz_html, height=700, scrolling=False)
                except Exception as e:
                    st.error(f"Error creating visualization: {str(e)}")
            
            # Show transformation details
            with st.expander("🔧 Transformation Details"):
                col1, col2 = st.columns(2)
                
                with col1:
                    st.markdown("**Rotation Matrix (U):**")
                    st.dataframe(
                        pd.DataFrame(selected_row['Rotation_Matrix']).round(4),
                        use_container_width=True
                    )
                
                with col2:
                    st.markdown("**Translation Vectors:**")
                    st.write(f"Reference COM: [{selected_row['Ref_COM'][0]:.3f}, {selected_row['Ref_COM'][1]:.3f}, {selected_row['Ref_COM'][2]:.3f}]")
                    st.write(f"Query COM: [{selected_row['Query_COM'][0]:.3f}, {selected_row['Query_COM'][1]:.3f}, {selected_row['Query_COM'][2]:.3f}]")
            
            # Download aligned structures
            with st.expander("💾 Download Structure Files"):
                st.markdown("**Download extracted and aligned structures for external visualization**")
                
                from visualization import extract_window_pdb, transform_pdb_string
                
                # Extract reference window
                ref_pdb = extract_window_pdb(
                    selected_row['Ref_Path'],
                    selected_row['Ref_Indices']
                )
                
                # Extract and transform query window
                query_pdb = extract_window_pdb(
                    selected_row['Query_Path'],
                    selected_row['Query_Indices']
                )
                
                query_aligned_pdb = transform_pdb_string(
                    query_pdb,
                    selected_row['Rotation_Matrix'],
                    selected_row['Query_COM'],
                    selected_row['Ref_COM']
                )
                
                col1, col2, col3 = st.columns(3)
                
                with col1:
                    # Reference structure
                    ref_filename = f"ref_{selected_row['Reference'].replace('.pdb', '')}_{'_'.join(map(str, [i+1 for i in selected_row['Ref_Indices']]))}.pdb"
                    st.download_button(
                        label="📥 Reference PDB",
                        data=ref_pdb,
                        file_name=ref_filename,
                        mime="chemical/x-pdb",
                        help="Original reference structure (selected residues only)"
                    )
                
                with col2:
                    # Query structure (original position)
                    query_filename = f"query_{selected_row['Query'].replace('.pdb', '')}_{'_'.join(map(str, [i+1 for i in selected_row['Query_Indices']]))}.pdb"
                    st.download_button(
                        label="📥 Query PDB (Original)",
                        data=query_pdb,
                        file_name=query_filename,
                        mime="chemical/x-pdb",
                        help="Original query structure (selected residues only)"
                    )
                
                with col3:
                    # Query structure (aligned)
                    query_aligned_filename = f"query_aligned_{selected_row['Query'].replace('.pdb', '')}_{'_'.join(map(str, [i+1 for i in selected_row['Query_Indices']]))}.pdb"
                    st.download_button(
                        label="📥 Query PDB (Aligned)",
                        data=query_aligned_pdb,
                        file_name=query_aligned_filename,
                        mime="chemical/x-pdb",
                        help="Query structure aligned to reference"
                    )
                
                st.info("💡 **Tip:** Load reference and aligned query together in PyMOL/Chimera to examine the superposition")
        else:
            st.warning("No comparisons below the RMSD threshold. Try increasing the threshold.")
        
        # Download results
        st.markdown("---")
        st.subheader("💾 Export Results")
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.markdown("**Export Results Table**")
            # Prepare CSV - make sure all columns exist
            export_columns = ['Reference', 'Ref_Residues', 'Ref_Sequence', 'Query', 'Query_Residues', 'Query_Sequence', 'Num_Residues', 'RMSD']
            export_df = results_df[export_columns].copy()
            export_df = export_df.sort_values('RMSD').reset_index(drop=True)
            
            csv = export_df.to_csv(index=False)
            st.download_button(
                label="📥 Download Results (CSV)",
                data=csv,
                file_name="rna_motif_comparison_results.csv",
                mime="text/csv"
            )
        
        with col2:
            st.markdown("**Export All Aligned Structures**")
            if st.button("📦 Generate PDB Archive", help="Create a ZIP file with all aligned structure pairs"):
                with st.spinner("Generating PDB files..."):
                    import zipfile
                    import io
                    from visualization import extract_window_pdb, transform_pdb_string
                    
                    # Create ZIP file in memory
                    zip_buffer = io.BytesIO()
                    
                    with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
                        # Process each comparison
                        for idx, row in filtered_df.iterrows():
                            # Create a directory name for this comparison
                            comp_name = f"comparison_{idx:03d}_rmsd_{row['RMSD']:.3f}"
                            
                            # Extract reference
                            ref_pdb = extract_window_pdb(row['Ref_Path'], row['Ref_Indices'])
                            ref_filename = f"{comp_name}/reference_{row['Reference'].replace('.pdb', '')}.pdb"
                            zip_file.writestr(ref_filename, ref_pdb)
                            
                            # Extract query (original)
                            query_pdb = extract_window_pdb(row['Query_Path'], row['Query_Indices'])
                            query_filename = f"{comp_name}/query_original_{row['Query'].replace('.pdb', '')}.pdb"
                            zip_file.writestr(query_filename, query_pdb)
                            
                            # Extract and align query
                            query_aligned_pdb = transform_pdb_string(
                                query_pdb,
                                row['Rotation_Matrix'],
                                row['Query_COM'],
                                row['Ref_COM']
                            )
                            query_aligned_filename = f"{comp_name}/query_aligned_{row['Query'].replace('.pdb', '')}.pdb"
                            zip_file.writestr(query_aligned_filename, query_aligned_pdb)
                            
                            # Add a README for this comparison
                            readme_content = f"""Comparison #{idx}
RMSD: {row['RMSD']:.3f} Å
Residues Compared: {row['Num_Residues']}

Reference:
  File: {row['Reference']}
  Residues: {row['Ref_Residues']}
  Sequence: {row['Ref_Sequence']}

Query:
  File: {row['Query']}
  Residues: {row['Query_Residues']}
  Sequence: {row['Query_Sequence']}

Files:
  - reference_*.pdb: Reference structure (selected residues)
  - query_original_*.pdb: Query structure (original position)
  - query_aligned_*.pdb: Query structure (aligned to reference)

To visualize in PyMOL:
  load reference_*.pdb
  load query_aligned_*.pdb
  
To visualize in Chimera:
  File → Open → Select both reference and query_aligned PDB files
"""
                            readme_filename = f"{comp_name}/README.txt"
                            zip_file.writestr(readme_filename, readme_content)
                    
                    zip_buffer.seek(0)
                    
                    st.download_button(
                        label="📥 Download PDB Archive (ZIP)",
                        data=zip_buffer.getvalue(),
                        file_name="aligned_structures.zip",
                        mime="application/zip",
                        help=f"Contains {len(filtered_df)} comparison sets with reference, original query, and aligned query PDBs"
                    )
                    
                    st.success(f"✅ Archive ready! Contains {len(filtered_df)} comparisons with 3 PDB files each.")


if __name__ == "__main__":
    main()