app.py

import gradio as gr
import re
import pandas as pd
from io import StringIO
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem, Draw
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from io import BytesIO

import re
from rdkit import Chem

class PeptideAnalyzer:
    def __init__(self):
        self.bond_patterns = [
            r'OC\(=O\)',  # ester bond
            r'N\(C\)C\(=O\)',  # N-methylated peptide bond
            r'N[12]?C\(=O\)',  # peptide bond (including Pro N1/N2)
            r'C\(=O\)N\(C\)',  # N-methylated peptide bond reverse
            r'C\(=O\)N'  # peptide bond reverse
        ]
    
    def is_peptide(self, smiles):
        """Check if the SMILES represents a peptide structure"""
        mol = Chem.MolFromSmiles(smiles)
        if mol is None:
            return False
            
        # Look for peptide bonds: NC(=O) pattern
        peptide_bond_pattern = Chem.MolFromSmarts('[NH][C](=O)')
        if mol.HasSubstructMatch(peptide_bond_pattern):
            return True
            
        # Look for N-methylated peptide bonds: N(C)C(=O) pattern
        n_methyl_pattern = Chem.MolFromSmarts('[N;H0;$(NC)](C)[C](=O)')
        if mol.HasSubstructMatch(n_methyl_pattern):
            return True
            
        # Look for ester bonds in cyclic depsipeptides: OC(=O) pattern
        ester_bond_pattern = Chem.MolFromSmarts('O[C](=O)')
        if mol.HasSubstructMatch(ester_bond_pattern):
            return True
            
        return False

    def is_cyclic(self, smiles):
        """
        Determine if SMILES represents a cyclic peptide
        Returns: (is_cyclic, peptide_cycles, aromatic_cycles)
        """
        cycle_info = {}
        
        # Find all cycle numbers and their contexts
        for match in re.finditer(r'(\d)', smiles):
            number = match.group(1)
            position = match.start(1)
            
            if number not in cycle_info:
                cycle_info[number] = []
            cycle_info[number].append({
                'position': position,
                'full_context': smiles[max(0, position-3):min(len(smiles), position+4)]
            })
        
        # Check each cycle
        peptide_cycles = []
        aromatic_cycles = []
        
        for number, occurrences in cycle_info.items():
            if len(occurrences) != 2:
                continue
                
            start, end = occurrences[0]['position'], occurrences[1]['position']
            segment = smiles[start:end+1]
            
            # Check for aromatic rings
            full_context = smiles[max(0,start-10):min(len(smiles),end+10)]
            is_aromatic = ('c2ccccc2' in full_context and len(segment) < 20) or \
                         ('c1ccccc1' in full_context and len(segment) < 20)
            
            # Check for peptide bonds
            peptide_patterns = [
                'C(=O)N',  # Regular peptide bond
                'C(=O)N(C)',  # N-methylated peptide bond
                'C(=O)N1',  # Cyclic peptide bond
                'C(=O)N2'   # Cyclic peptide bond
            ]
            
            has_peptide_bond = any(pattern in segment for pattern in peptide_patterns) and \
                              len(segment) > 20
            
            if is_aromatic and len(segment) < 20:
                aromatic_cycles.append(number)
            elif has_peptide_bond:
                peptide_cycles.append(number)
        
        return len(peptide_cycles) > 0, peptide_cycles, aromatic_cycles

    
    def split_on_bonds(self, smiles):
        """Split SMILES into segments with simplified Pro handling"""
        positions = []
        used = set()
        
        # Find Gly pattern first
        gly_pattern = r'NCC\(=O\)'
        for match in re.finditer(gly_pattern, smiles):
            if not any(p in range(match.start(), match.end()) for p in used):
                positions.append({
                    'start': match.start(),
                    'end': match.end(),
                    'type': 'gly',
                    'pattern': match.group()
                })
                used.update(range(match.start(), match.end()))

        # Then find all bonds, including N2C(=O)
        bond_patterns = [
            (r'OC\(=O\)', 'ester'),
            (r'N\(C\)C\(=O\)', 'n_methyl'),
            (r'N[12]C\(=O\)', 'peptide'),  # Pro peptide bonds
            (r'NC\(=O\)', 'peptide'),  # Regular peptide bonds
            (r'C\(=O\)N\(C\)', 'n_methyl'),
            (r'C\(=O\)N[12]?', 'peptide')
        ]
        
        for pattern, bond_type in bond_patterns:
            for match in re.finditer(pattern, smiles):
                if not any(p in range(match.start(), match.end()) for p in used):
                    positions.append({
                        'start': match.start(),
                        'end': match.end(),
                        'type': bond_type,
                        'pattern': match.group()
                    })
                    used.update(range(match.start(), match.end()))

        # Sort by position
        positions.sort(key=lambda x: x['start'])
        
        # Create segments
        segments = []
        
        if positions:
            # First segment
            if positions[0]['start'] > 0:
                segments.append({
                    'content': smiles[0:positions[0]['start']],
                    'bond_after': positions[0]['pattern']
                })
            
            # Process segments
            for i in range(len(positions)-1):
                current = positions[i]
                next_pos = positions[i+1]
                
                if current['type'] == 'gly':
                    segments.append({
                        'content': 'NCC(=O)',
                        'bond_before': positions[i-1]['pattern'] if i > 0 else None,
                        'bond_after': next_pos['pattern']
                    })
                else:
                    content = smiles[current['end']:next_pos['start']]
                    if content:
                        segments.append({
                            'content': content,
                            'bond_before': current['pattern'],
                            'bond_after': next_pos['pattern']
                        })
            
            # Last segment
            if positions[-1]['end'] < len(smiles):
                segments.append({
                    'content': smiles[positions[-1]['end']:],
                    'bond_before': positions[-1]['pattern']
                })
        
        return segments

    def identify_residue(self, segment):
        """Identify residue with Pro reconstruction"""
        content = segment['content']
        mods = self.get_modifications(segment)
        
        # Special handling for Pro: reconstruct the complete pattern
        if (segment.get('bond_after') == 'N2C(=O)' and 'CCC' in content) or \
            ('CCCN2' in content and content.endswith('=O')):  # End case
        # Reconstruct the complete Pro pattern
            if '[C@@H]2' in content or '[C@H]2' in content:
                return 'Pro', mods
        
        if ('C[C@H](CCCC)' in content or 'C[C@@H](CCCC)' in content) and 'CC(C)' not in content:
            return 'Nle', mods
            
        # Ornithine (Orn) - 3-carbon chain with NH2
        if ('C[C@H](CCCN)' in content or 'C[C@@H](CCCN)' in content) and 'CC(C)' not in content:
            return 'Orn', mods
            
        # 2-Naphthylalanine (2Nal) - distinct from Phe pattern
        if ('Cc3cc2ccccc2c3' in content) and ('C[C@H]' in content or 'C[C@@H]' in content):
            return '2Nal', mods
            
        # Cyclohexylalanine (Cha) - already in your code but moved here for clarity
        if 'N2CCCCC2' in content or 'CCCCC2' in content:
            return 'Cha', mods
            
        # Aminobutyric acid (Abu) - 2-carbon chain
        if ('C[C@H](CC)' in content or 'C[C@@H](CC)' in content) and not any(p in content for p in ['CC(C)', 'CCCC', 'CCC(C)']):
            return 'Abu', mods
            
        # Pipecolic acid (Pip) - 6-membered ring like Pro
        if ('N3CCCCC3' in content or 'CCCCC3' in content) and ('C[C@H]' in content or 'C[C@@H]' in content):
            return 'Pip', mods

        # Cyclohexylglycine (Chg) - direct cyclohexyl without CH2
        if ('C[C@H](C1CCCCC1)' in content or 'C[C@@H](C1CCCCC1)' in content):
            return 'Chg', mods
            
        # 4-Fluorophenylalanine (4F-Phe)
        if ('Cc2ccc(F)cc2' in content) and ('C[C@H]' in content or 'C[C@@H]' in content):
            return '4F-Phe', mods
        
        # Regular residue identification
        if 'NCC(=O)' in content:
            return 'Gly', mods
            
        if 'CC(C)C[C@H]' in content or 'CC(C)C[C@@H]' in content:
            return 'Leu', mods
        if '[C@@H](CC(C)C)' in content or '[C@H](CC(C)C)' in content:
            return 'Leu', mods
        
        if ('C(C)C[C@H]' in content or 'C(C)C[C@@H]' in content) and 'CC(C)C' not in content:
            return 'Ile', mods
        
        if '[C@@H]([C@@H](C)O)' in content or '[C@H]([C@H](C)O)' in content:
            return 'Thr', mods
        
        if '[C@H](Cc2ccccc2)' in content or '[C@@H](Cc2ccccc2)' in content:
            return 'Phe', mods
            
        if '[C@H](C(C)C)' in content or '[C@@H](C(C)C)' in content:
            if not any(p in content for p in ['CC(C)C[C@H]', 'CC(C)C[C@@H]']):
                return 'Val', mods
        
        if '[C@H](COC(C)(C)C)' in content or '[C@@H](COC(C)(C)C)' in content:
            return 'O-tBu', mods
        
        if ('[C@H](C)' in content or '[C@@H](C)' in content):
            if not any(p in content for p in ['C(C)C', 'COC', 'CN(', 'C(C)O']):
                return 'Ala', mods
        
        return None, mods

    def get_modifications(self, segment):
        """Get modifications based on bond types"""
        mods = []
        if segment.get('bond_after'):
            if 'N(C)' in segment['bond_after'] or segment['bond_after'].startswith('C(=O)N(C)'):
                mods.append('N-Me')
            if 'OC(=O)' in segment['bond_after']:
                mods.append('O-linked')
        return mods

    def analyze_structure(self, smiles):
        """Main analysis function"""
        print("\nAnalyzing structure:", smiles)
        
        # Split into segments
        segments = self.split_on_bonds(smiles)
        
        print("\nSegment Analysis:")
        sequence = []
        for i, segment in enumerate(segments):
            print(f"\nSegment {i}:")
            print(f"Content: {segment['content']}")
            print(f"Bond before: {segment.get('bond_before', 'None')}")
            print(f"Bond after: {segment.get('bond_after', 'None')}")
            
            residue, mods = self.identify_residue(segment)
            if residue:
                if mods:
                    sequence.append(f"{residue}({','.join(mods)})")
                else:
                    sequence.append(residue)
                print(f"Identified as: {residue}")
                print(f"Modifications: {mods}")
            else:
                print(f"Warning: Could not identify residue in segment: {segment['content']}")
        
        # Check if cyclic
        is_cyclic = 'N1' in smiles or 'N2' in smiles
        final_sequence = f"cyclo({'-'.join(sequence)})" if is_cyclic else '-'.join(sequence)
        
        print(f"\nFinal sequence: {final_sequence}")
        return final_sequence

"""
def annotate_cyclic_structure(mol, sequence):
    '''Create annotated 2D structure with clear, non-overlapping residue labels'''
    # Generate 2D coordinates
    # Generate 2D coordinates
    AllChem.Compute2DCoords(mol)
    
    # Create drawer with larger size for annotations
    drawer = Draw.rdMolDraw2D.MolDraw2DCairo(2000, 2000)  # Even larger size
    
    # Get residue list and reverse it to match structural representation
    if sequence.startswith('cyclo('):
        residues = sequence[6:-1].split('-')
    else:
        residues = sequence.split('-')
    residues = list(reversed(residues))  # Reverse the sequence
    
    # Draw molecule first to get its bounds
    drawer.drawOptions().addAtomIndices = False
    drawer.DrawMolecule(mol)
    drawer.FinishDrawing()
    
    # Convert to PIL Image
    img = Image.open(BytesIO(drawer.GetDrawingText()))
    draw = ImageDraw.Draw(img)
    
    try:
        # Try to use DejaVuSans as it's commonly available on Linux systems
        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 60)
        small_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 60)
    except OSError:
        try:
            # Fallback to Arial if available (common on Windows)
            font = ImageFont.truetype("arial.ttf", 60)
            small_font = ImageFont.truetype("arial.ttf", 60)
        except OSError:
            # If no TrueType fonts are available, fall back to default
            print("Warning: TrueType fonts not available, using default font")
            font = ImageFont.load_default()
            small_font = ImageFont.load_default()
    # Get molecule bounds
    conf = mol.GetConformer()
    positions = []
    for i in range(mol.GetNumAtoms()):
        pos = conf.GetAtomPosition(i)
        positions.append((pos.x, pos.y))
    
    x_coords = [p[0] for p in positions]
    y_coords = [p[1] for p in positions]
    min_x, max_x = min(x_coords), max(x_coords)
    min_y, max_y = min(y_coords), max(y_coords)
    
    # Calculate scaling factors
    scale = 150  # Increased scale factor
    center_x = 1000  # Image center
    center_y = 1000
    
    # Add residue labels in a circular arrangement around the structure
    n_residues = len(residues)
    radius = 700  # Distance of labels from center
    
    # Start from the rightmost point (3 o'clock position) and go counterclockwise
    # Offset by -3 positions to align with structure
    offset = 0  # Adjust this value to match the structure alignment
    for i, residue in enumerate(residues):
        # Calculate position in a circle around the structure
        # Start from 0 (3 o'clock) and go counterclockwise
        angle = -(2 * np.pi * ((i + offset) % n_residues) / n_residues)
        
        # Calculate label position
        label_x = center_x + radius * np.cos(angle)
        label_y = center_y + radius * np.sin(angle)
        
        # Draw residue label
        text = f"{i+1}. {residue}"
        bbox = draw.textbbox((label_x, label_y), text, font=font)
        padding = 10
        draw.rectangle([bbox[0]-padding, bbox[1]-padding, 
                       bbox[2]+padding, bbox[3]+padding], 
                      fill='white', outline='white')
        draw.text((label_x, label_y), text, 
                 font=font, fill='black', anchor="mm")
    
    # Add sequence at the top with white background
    seq_text = f"Sequence: {sequence}"
    bbox = draw.textbbox((center_x, 100), seq_text, font=small_font)
    padding = 10
    draw.rectangle([bbox[0]-padding, bbox[1]-padding, 
                   bbox[2]+padding, bbox[3]+padding], 
                  fill='white', outline='white')
    draw.text((center_x, 100), seq_text, 
             font=small_font, fill='black', anchor="mm")
    
    return img

"""
def annotate_cyclic_structure(mol, sequence):
    """Create structure visualization with just the sequence header"""
    # Generate 2D coordinates
    AllChem.Compute2DCoords(mol)
    
    # Create drawer with larger size for annotations
    drawer = Draw.rdMolDraw2D.MolDraw2DCairo(2000, 2000)
    
    # Draw molecule first
    drawer.drawOptions().addAtomIndices = False
    drawer.DrawMolecule(mol)
    drawer.FinishDrawing()
    
    # Convert to PIL Image
    img = Image.open(BytesIO(drawer.GetDrawingText()))
    draw = ImageDraw.Draw(img)
    try:
        small_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 60)
    except OSError:
        try:
            small_font = ImageFont.truetype("arial.ttf", 60)
        except OSError:
            print("Warning: TrueType fonts not available, using default font")
            small_font = ImageFont.load_default()
    
    # Add just the sequence header at the top
    seq_text = f"Sequence: {sequence}"
    bbox = draw.textbbox((1000, 100), seq_text, font=small_font)
    padding = 10
    draw.rectangle([bbox[0]-padding, bbox[1]-padding, 
                   bbox[2]+padding, bbox[3]+padding], 
                  fill='white', outline='white')
    draw.text((1000, 100), seq_text, 
             font=small_font, fill='black', anchor="mm")
    
    return img

def create_enhanced_linear_viz(sequence, smiles):
    """Create an enhanced linear representation using PeptideAnalyzer"""
    analyzer = PeptideAnalyzer()  # Create analyzer instance
    
    # Create figure with two subplots
    fig = plt.figure(figsize=(15, 10))
    gs = fig.add_gridspec(2, 1, height_ratios=[1, 2])
    ax_struct = fig.add_subplot(gs[0])
    ax_detail = fig.add_subplot(gs[1])
    
    # Parse sequence and get residues
    if sequence.startswith('cyclo('):
        residues = sequence[6:-1].split('-')
    else:
        residues = sequence.split('-')
    
    # Get segments using analyzer
    segments = analyzer.split_on_bonds(smiles)
    
    # Debug print
    print(f"Number of residues: {len(residues)}")
    print(f"Number of segments: {len(segments)}")
    
    # Top subplot - Basic structure
    ax_struct.set_xlim(0, 10)
    ax_struct.set_ylim(0, 2)
    
    num_residues = len(residues)
    spacing = 9.0 / (num_residues - 1) if num_residues > 1 else 9.0
    
    # Draw basic structure
    y_pos = 1.5
    for i in range(num_residues):
        x_pos = 0.5 + i * spacing
        
        # Draw amino acid box
        rect = patches.Rectangle((x_pos-0.3, y_pos-0.2), 0.6, 0.4, 
                               facecolor='lightblue', edgecolor='black')
        ax_struct.add_patch(rect)
        
        # Draw connecting bonds if not the last residue
        if i < num_residues - 1:
            segment = segments[i] if i < len(segments) else None
            if segment:
                # Determine bond type from segment info
                bond_type = 'ester' if 'O-linked' in segment.get('bond_after', '') else 'peptide'
                is_n_methylated = 'N-Me' in segment.get('bond_after', '')
                
                bond_color = 'red' if bond_type == 'ester' else 'black'
                linestyle = '--' if bond_type == 'ester' else '-'
                
                # Draw bond line
                ax_struct.plot([x_pos+0.3, x_pos+spacing-0.3], [y_pos, y_pos], 
                             color=bond_color, linestyle=linestyle, linewidth=2)
                
                # Add bond type label
                mid_x = x_pos + spacing/2
                bond_label = f"{bond_type}"
                if is_n_methylated:
                    bond_label += "\n(N-Me)"
                ax_struct.text(mid_x, y_pos+0.1, bond_label, 
                             ha='center', va='bottom', fontsize=10, 
                             color=bond_color)
        
        # Add residue label
        ax_struct.text(x_pos, y_pos-0.5, residues[i], 
                      ha='center', va='top', fontsize=14)
    
    # Bottom subplot - Detailed breakdown
    ax_detail.set_ylim(0, len(segments)+1)
    ax_detail.set_xlim(0, 1)
    
    # Create detailed breakdown
    segment_y = len(segments)  # Start from top
    for i, segment in enumerate(segments):
        y = segment_y - i
        
        # Check if this is a bond or residue
        residue, mods = analyzer.identify_residue(segment)
        if residue:
            text = f"Residue {i+1}: {residue}"
            if mods:
                text += f" ({', '.join(mods)})"
            color = 'blue'
        else:
            # Must be a bond
            text = f"Bond {i}: "
            if 'O-linked' in segment.get('bond_after', ''):
                text += "ester"
            elif 'N-Me' in segment.get('bond_after', ''):
                text += "peptide (N-methylated)"
            else:
                text += "peptide"
            color = 'red'
        
        # Add segment analysis
        ax_detail.text(0.05, y, text, fontsize=12, color=color)
        ax_detail.text(0.5, y, f"SMILES: {segment.get('content', '')}", fontsize=10, color='gray')
    
    # If cyclic, add connection indicator
    if sequence.startswith('cyclo('):
        ax_struct.annotate('', xy=(9.5, y_pos), xytext=(0.5, y_pos),
                          arrowprops=dict(arrowstyle='<->', color='red', lw=2))
        ax_struct.text(5, y_pos+0.3, 'Cyclic Connection', 
                      ha='center', color='red', fontsize=14)
    
    # Add titles and adjust layout
    ax_struct.set_title("Peptide Structure Overview", pad=20)
    ax_detail.set_title("Segment Analysis Breakdown", pad=20)
    
    # Remove axes
    for ax in [ax_struct, ax_detail]:
        ax.set_xticks([])
        ax.set_yticks([])
        ax.axis('off')
    
    plt.tight_layout()
    return fig

def process_input(smiles_input=None, file_obj=None, show_linear=False):
    """Process input and create visualizations using PeptideAnalyzer"""
    analyzer = PeptideAnalyzer()
    
    # Handle direct SMILES input
    if smiles_input:
        smiles = smiles_input.strip()
        
        # First check if it's a peptide using analyzer's method
        if not analyzer.is_peptide(smiles):
            return "Error: Input SMILES does not appear to be a peptide structure.", None, None
            
        try:
            # Create molecule
            mol = Chem.MolFromSmiles(smiles)
            if mol is None:
                return "Error: Invalid SMILES notation.", None, None
            
            # Use analyzer to get sequence
            segments = analyzer.split_on_bonds(smiles)
            
            # Process segments and build sequence
            sequence_parts = []
            output_text = "Segment Analysis:\n"
            for i, segment in enumerate(segments):
                output_text += f"\nSegment {i}:\n"
                output_text += f"Content: {segment['content']}\n"
                output_text += f"Bond before: {segment.get('bond_before', 'None')}\n"
                output_text += f"Bond after: {segment.get('bond_after', 'None')}\n"
                
                residue, mods = analyzer.identify_residue(segment)
                if residue:
                    if mods:
                        sequence_parts.append(f"{residue}({','.join(mods)})")
                    else:
                        sequence_parts.append(residue)
                    output_text += f"Identified as: {residue}\n"
                    output_text += f"Modifications: {mods}\n"
                else:
                    output_text += f"Warning: Could not identify residue in segment: {segment['content']}\n"
            
            # Check if cyclic using analyzer's method
            is_cyclic, peptide_cycles, aromatic_cycles = analyzer.is_cyclic(smiles)
            sequence = f"cyclo({'-'.join(sequence_parts)})" if is_cyclic else '-'.join(sequence_parts)
            
            # Create cyclic structure visualization
            img_cyclic = annotate_cyclic_structure(mol, sequence)
            
            # Create linear representation if requested
            img_linear = None
            if show_linear:
                fig_linear = create_enhanced_linear_viz(sequence, smiles)
                buf = BytesIO()
                fig_linear.savefig(buf, format='png', bbox_inches='tight', dpi=300)
                buf.seek(0)
                img_linear = Image.open(buf)
                plt.close(fig_linear)
            
            # Add summary to output
            summary = f"\nSummary:\n"
            summary += f"Sequence: {sequence}\n"
            summary += f"Is Cyclic: {'Yes' if is_cyclic else 'No'}\n"
            if is_cyclic:
                summary += f"Peptide Cycles: {', '.join(peptide_cycles)}\n"
                summary += f"Aromatic Cycles: {', '.join(aromatic_cycles)}\n"
            
            return summary + "\n" + output_text, img_cyclic, img_linear
            
        except Exception as e:
            return f"Error processing SMILES: {str(e)}", None, None
    
    # Handle file input
    if file_obj is not None:
        try:
            # Handle file content
            if hasattr(file_obj, 'name'):
                with open(file_obj.name, 'r') as f:
                    content = f.read()
            else:
                content = file_obj.decode('utf-8') if isinstance(file_obj, bytes) else str(file_obj)
            
            output_text = ""
            for line in content.splitlines():
                smiles = line.strip()
                if smiles:
                    # Check if it's a peptide
                    if not analyzer.is_peptide(smiles):
                        output_text += f"Skipping non-peptide SMILES: {smiles}\n"
                        continue
                    
                    # Process this SMILES
                    segments = analyzer.split_on_bonds(smiles)
                    sequence_parts = []
                    for segment in segments:
                        residue, mods = analyzer.identify_residue(segment)
                        if residue:
                            if mods:
                                sequence_parts.append(f"{residue}({','.join(mods)})")
                            else:
                                sequence_parts.append(residue)
                    
                    # Get cyclicity and create sequence
                    is_cyclic, peptide_cycles, aromatic_cycles = analyzer.is_cyclic(smiles)
                    sequence = f"cyclo({'-'.join(sequence_parts)})" if is_cyclic else '-'.join(sequence_parts)
                    
                    output_text += f"SMILES: {smiles}\n"
                    output_text += f"Sequence: {sequence}\n"
                    output_text += f"Is Cyclic: {'Yes' if is_cyclic else 'No'}\n"
                    if is_cyclic:
                        output_text += f"Peptide Cycles: {', '.join(peptide_cycles)}\n"
                        output_text += f"Aromatic Cycles: {', '.join(aromatic_cycles)}\n"
                    output_text += "-" * 50 + "\n"
            
            return output_text, None, None
            
        except Exception as e:
            return f"Error processing file: {str(e)}", None, None
    
    return "No input provided.", None, None

iface = gr.Interface(
    fn=process_input,
    inputs=[
        gr.Textbox(
            label="Enter SMILES string",
            placeholder="Enter SMILES notation of peptide...",
            lines=2
        ),
        gr.File(
            label="Or upload a text file with SMILES",
            file_types=[".txt"],
            type="binary"
        ),
        gr.Checkbox(
            label="Show linear representation"
        )
    ],
    outputs=[
        gr.Textbox(
            label="Analysis Results",
            lines=10
        ),
        gr.Image(
            label="2D Structure with Annotations"
        ),
        gr.Image(
            label="Linear Representation"
        )
    ],
    title="Peptide Structure Analyzer and Visualizer",
    description="""
    Analyze and visualize peptide structures from SMILES notation:
    1. Validates if the input is a peptide structure
    2. Determines if the peptide is cyclic
    3. Parses the amino acid sequence
    4. Creates 2D structure visualization with residue annotations
    5. Optional linear representation
    
    Input: Either enter a SMILES string directly or upload a text file containing SMILES strings
    
    Example SMILES strings (copy and paste):
    ```
    CC(C)C[C@@H]1NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H]2CCCN2C1=O
    ```
    ```
    C(C)C[C@@H]1NC(=O)[C@@H]2CCCN2C(=O)[C@@H](CC(C)C)NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@H](C)NC(=O)[C@H](Cc2ccccc2)NC1=O
    ```
    ```
    CC(C)C[C@H]1C(=O)N(C)[C@@H](Cc2ccccc2)C(=O)NCC(=O)N[C@H](C(=O)N2CCCCC2)CC(=O)N(C)CC(=O)N[C@@H]([C@@H](C)O)C(=O)N(C)[C@@H](C)C(=O)N[C@@H](COC(C)(C)C)C(=O)N(C)[C@@H](Cc2ccccc2)C(=O)N1C
    ```
    """,
    flagging_mode="never"
)

# Launch the app
if __name__ == "__main__":
    iface.launch()