Update app.py

app.py

@@ -1,828 +1,2 @@
-import os,sys
-
-# install required packages
-os.system('pip install plotly')  # plotly 설치
-os.system('pip install matplotlib')  # matplotlib 설치
-os.system('pip install dgl==1.0.2+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html')
-os.environ["DGLBACKEND"] = "pytorch"
-print('Modules installed')
-
-import plotly.graph_objects as go
-import numpy as np
-import gradio as gr
-import py3Dmol
-from io import StringIO
-import json
-import secrets
-import copy
-import matplotlib.pyplot as plt
-from utils.sampler import HuggingFace_sampler
-from utils.parsers_inference import parse_pdb
-from model.util import writepdb
-from utils.inpainting_util import *
-
-# install environment goods
-#os.system("pip -q install dgl -f https://data.dgl.ai/wheels/cu113/repo.html")
-os.system('pip install dgl==1.0.2+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html')
-#os.system('pip install gradio')
-os.environ["DGLBACKEND"] = "pytorch"
-#os.system(f'pip install -r ./PROTEIN_GENERATOR/requirements.txt')
-print('Modules installed')
-
-#os.system('pip install --force gradio==3.36.1')
-#os.system('pip install gradio_client==0.2.7')
-#os.system('pip install \"numpy<2\"')
-#os.system('pip install numpy --upgrade')
-#os.system('pip install --force numpy==1.24.1')
-
-
-if not os.path.exists('./SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'):
-    print('Downloading model weights 1')
-    os.system('wget http://files.ipd.uw.edu/pub/sequence_diffusion/checkpoints/SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt')
-    print('Successfully Downloaded')
-
-if not os.path.exists('./SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt'):
-    print('Downloading model weights 2')
-    os.system('wget http://files.ipd.uw.edu/pub/sequence_diffusion/checkpoints/SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt')
-    print('Successfully Downloaded')
-
-import numpy as np
-import gradio as gr
-import py3Dmol
-from io import StringIO
-import json
-import secrets
-import copy
-import matplotlib.pyplot as plt
-from utils.sampler import HuggingFace_sampler
-from utils.parsers_inference import parse_pdb
-from model.util import writepdb
-from utils.inpainting_util import *
-
-
-plt.rcParams.update({'font.size': 13})
-
-with open('./tmp/args.json','r') as f:
-    args = json.load(f)
-
-# manually set checkpoint to load
-args['checkpoint'] = None
-args['dump_trb'] = False
-args['dump_args'] = True
-args['save_best_plddt'] = True
-args['T'] = 25
-args['strand_bias'] = 0.0
-args['loop_bias'] = 0.0
-args['helix_bias'] = 0.0
-
-def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bias, 
-                    secondary_structure, aa_bias, aa_bias_potential, 
-                    num_steps, noise, hydrophobic_target_score, hydrophobic_potential,
-                    contigs, pssm, seq_mask, str_mask, rewrite_pdb):
-
-    
-    dssp_checkpoint = './SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'
-    og_checkpoint = './SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt'
-
-    model_args = copy.deepcopy(args)
-
-    # make sampler
-    S = HuggingFace_sampler(args=model_args)
-
-    # get random prefix 
-    S.out_prefix = './tmp/'+secrets.token_hex(nbytes=10).upper()
-
-    # set args
-    S.args['checkpoint'] = None
-    S.args['dump_trb'] = False
-    S.args['dump_args'] = True
-    S.args['save_best_plddt'] = True
-    S.args['T'] = 20
-    S.args['strand_bias'] = 0.0
-    S.args['loop_bias'] = 0.0
-    S.args['helix_bias'] = 0.0
-    S.args['potentials'] = None
-    S.args['potential_scale'] = None
-    S.args['aa_composition'] = None
-
-
-    # get sequence if entered and make sure all chars are valid
-    alt_aa_dict = {'B':['D','N'],'J':['I','L'],'U':['C'],'Z':['E','Q'],'O':['K']}
-    if sequence not in ['',None]:
-        L = len(sequence)
-        aa_seq = []
-        for aa in sequence.upper():
-            if aa in alt_aa_dict.keys():
-                aa_seq.append(np.random.choice(alt_aa_dict[aa]))
-            else:
-                aa_seq.append(aa)
-
-        S.args['sequence'] = aa_seq
-    elif contigs not in ['',None]:
-        S.args['contigs'] = [contigs]
-    else:
-        S.args['contigs'] = [f'{seq_len}']
-        L = int(seq_len)
-    
-    print('DEBUG: ',rewrite_pdb)
-    if rewrite_pdb not in ['',None]:
-        S.args['pdb'] = rewrite_pdb.name
-
-    if seq_mask not in ['',None]:
-        S.args['inpaint_seq'] = [seq_mask]
-    if str_mask not in ['',None]:
-        S.args['inpaint_str'] = [str_mask]
-
-    if secondary_structure in ['',None]:
-        secondary_structure = None
-    else:
-        secondary_structure = ''.join(['E' if x == 'S' else x for x in secondary_structure])
-        if L < len(secondary_structure):
-            secondary_structure = secondary_structure[:len(sequence)]
-        elif L == len(secondary_structure):
-            pass
-        else:
-            dseq = L - len(secondary_structure)
-            secondary_structure += secondary_structure[-1]*dseq
-    
-
-    # potentials
-    potential_list = []
-    potential_bias_list = []
-
-    if aa_bias not in ['',None]:
-        potential_list.append('aa_bias')
-        S.args['aa_composition'] = aa_bias
-        if aa_bias_potential in ['',None]:
-            aa_bias_potential = 3
-        potential_bias_list.append(str(aa_bias_potential))
-    '''
-    if target_charge not in ['',None]:
-        potential_list.append('charge')
-        if charge_potential in ['',None]:
-            charge_potential = 1
-        potential_bias_list.append(str(charge_potential))
-        S.args['target_charge'] = float(target_charge)
-        if target_ph in ['',None]:
-            target_ph = 7.4
-        S.args['target_pH'] = float(target_ph)
-    '''
-    
-    if hydrophobic_target_score not in ['',None]:
-        potential_list.append('hydrophobic')
-        S.args['hydrophobic_score'] = float(hydrophobic_target_score)
-        if hydrophobic_potential in ['',None]:
-            hydrophobic_potential = 3
-        potential_bias_list.append(str(hydrophobic_potential))
-    
-    if pssm not in ['',None]:
-        potential_list.append('PSSM')
-        potential_bias_list.append('5')
-        S.args['PSSM'] = pssm.name
-        
-
-    if len(potential_list) > 0:
-        S.args['potentials'] = ','.join(potential_list)
-        S.args['potential_scale'] = ','.join(potential_bias_list)
-
-
-    # normalise secondary_structure bias from range 0-0.3
-    S.args['secondary_structure'] = secondary_structure
-    S.args['helix_bias'] = helix_bias
-    S.args['strand_bias'] = strand_bias
-    S.args['loop_bias'] = loop_bias
-    
-    # set T
-    if num_steps in ['',None]:
-        S.args['T'] = 20
-    else:
-        S.args['T'] = int(num_steps)
-
-    # noise
-    if 'normal' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [0]
-        S.args['sample_distribution_gmm_variances'] = [1]
-    elif 'gmm2' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [-1,1]
-        S.args['sample_distribution_gmm_variances'] = [1,1]
-    elif 'gmm3' in noise:
-        S.args['sample_distribution'] = noise
-        S.args['sample_distribution_gmm_means'] = [-1,0,1]
-        S.args['sample_distribution_gmm_variances'] = [1,1,1]
-
-
-
-    if secondary_structure not in ['',None] or helix_bias+strand_bias+loop_bias > 0:
-        S.args['checkpoint'] = dssp_checkpoint
-        S.args['d_t1d'] = 29
-        print('using dssp checkpoint')
-    else:
-        S.args['checkpoint'] = og_checkpoint
-        S.args['d_t1d'] = 24
-        print('using og checkpoint')
-    
-
-    for k,v in S.args.items():
-        print(f"{k} --> {v}")
-    
-    # init S
-    S.model_init()
-    S.diffuser_init()
-    S.setup()
-
-    # sampling loop
-    plddt_data = []
-    for j in range(S.max_t):
-        print(f'on step {j}')
-        output_seq, output_pdb, plddt = S.take_step_get_outputs(j)
-        plddt_data.append(plddt)
-        yield output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
-    
-    output_seq, output_pdb, plddt = S.get_outputs()
- 
-    return output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
-
-
-def get_plddt_plot(plddt_data, max_t):
-    x = [i+1 for i in range(len(plddt_data))]
-    fig, ax = plt.subplots(figsize=(15,6))
-    ax.plot(x,plddt_data,color='#661dbf', linewidth=3,marker='o')
-    ax.set_xticks([i+1 for i in range(max_t)])
-    ax.set_yticks([(i+1)/10 for i in range(10)])
-    ax.set_ylim([0,1])
-    ax.set_ylabel('model confidence (plddt)')
-    ax.set_xlabel('diffusion steps (t)')
-    return fig
-
-def display_pdb(path_to_pdb):
-    '''
-        #function to display pdb in py3dmol
-    '''
-    pdb = open(path_to_pdb, "r").read()
-    
-    view = py3Dmol.view(width=500, height=500)
-    view.addModel(pdb, "pdb")
-    view.setStyle({'model': -1}, {"cartoon": {'colorscheme':{'prop':'b','gradient':'roygb','min':0,'max':1}}})#'linear', 'min': 0, 'max': 1, 'colors': ["#ff9ef0","#a903fc",]}}}) 
-    view.zoomTo()
-    output = view._make_html().replace("'", '"')
-    print(view._make_html())
-    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""  # do not use ' in this input
-    
-    return f"""<iframe height="500px" width="100%"  name="result" allow="midi; geolocation; microphone; camera;
-                            display-capture; encrypted-media;" sandbox="allow-modals allow-forms
-                            allow-scripts allow-same-origin allow-popups
-                            allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
-                            allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
-
-'''
-    return f"""<iframe  style="width: 100%; height:700px" name="result" allow="midi; geolocation; microphone; camera; 
-                            display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
-                            allow-scripts allow-same-origin allow-popups 
-                            allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
-                            allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""
-'''
-
-
-
-# MOTIF SCAFFOLDING
-def get_motif_preview(pdb_id, contigs):
-    '''
-        #function to display selected motif in py3dmol
-    '''
-    input_pdb = fetch_pdb(pdb_id=pdb_id.lower())
-
-    # rewrite pdb
-    parse = parse_pdb(input_pdb)
-    #output_name = './rewrite_'+input_pdb.split('/')[-1]
-    #writepdb(output_name, torch.tensor(parse_og['xyz']),torch.tensor(parse_og['seq']))
-    #parse = parse_pdb(output_name)
-    output_name = input_pdb
-
-    pdb = open(output_name, "r").read()
-    view = py3Dmol.view(width=500, height=500)
-    view.addModel(pdb, "pdb")
-
-    if contigs in ['',0]:
-        contigs = ['0']
-    else:
-        contigs = [contigs]
-
-    print('DEBUG: ',contigs)
-    
-    pdb_map = get_mappings(ContigMap(parse,contigs))
-    print('DEBUG: ',pdb_map)
-    print('DEBUG: ',pdb_map['con_ref_idx0'])
-    roi = [x[1]-1 for x in pdb_map['con_ref_pdb_idx']]
-
-    colormap = {0:'#D3D3D3', 1:'#F74CFF'}
-    colors = {i+1: colormap[1] if i in roi else colormap[0] for i in range(parse['xyz'].shape[0])}
-    view.setStyle({"cartoon": {"colorscheme": {"prop": "resi", "map": colors}}})
-    view.zoomTo()
-    output = view._make_html().replace("'", '"')
-    print(view._make_html())
-    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""  # do not use ' in this input
-    
-    return f"""<iframe height="500px" width="100%"  name="result" allow="midi; geolocation; microphone; camera;
-                            display-capture; encrypted-media;" sandbox="allow-modals allow-forms
-                            allow-scripts allow-same-origin allow-popups
-                            allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
-                            allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>""", output_name
-
-def fetch_pdb(pdb_id=None):
-    if pdb_id is None or pdb_id == "":
-        return None
-    else:
-        os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_id}.pdb")
-        return f"{pdb_id}.pdb"
-
-# MSA AND PSSM GUIDANCE
-def save_pssm(file_upload):
-    filename = file_upload.name
-    orig_name = file_upload.orig_name
-    if filename.split('.')[-1] in ['fasta', 'a3m']:
-        return msa_to_pssm(file_upload)
-    return filename
-
-def msa_to_pssm(msa_file):
-    # Define the lookup table for converting amino acids to indices
-    aa_to_index = {'A': 0, 'R': 1, 'N': 2, 'D': 3, 'C': 4, 'Q': 5, 'E': 6, 'G': 7, 'H': 8, 'I': 9, 'L': 10,
-                'K': 11, 'M': 12, 'F': 13, 'P': 14, 'S': 15, 'T': 16, 'W': 17, 'Y': 18, 'V': 19, 'X': 20, '-': 21}
-    # Open the FASTA file and read the sequences
-    records = list(SeqIO.parse(msa_file.name, "fasta"))
-
-    assert len(records) >= 1, "MSA must contain more than one protein sequecne."
-
-    first_seq = str(records[0].seq)
-    aligned_seqs = [first_seq]
-    # print(aligned_seqs)
-    # Perform sequence alignment using the Needleman-Wunsch algorithm
-    aligner = Align.PairwiseAligner()
-    aligner.open_gap_score = -0.7
-    aligner.extend_gap_score = -0.3
-    for record in records[1:]:
-        alignment = aligner.align(first_seq, str(record.seq))[0]
-        alignment = alignment.format().split("\n")
-        al1 = alignment[0]
-        al2 = alignment[2]
-        al1_fin = ""
-        al2_fin = ""
-        percent_gap = al2.count('-')/ len(al2)
-        if percent_gap > 0.4:
-            continue
-        for i in range(len(al1)):
-            if al1[i] != '-':
-                al1_fin += al1[i]
-                al2_fin += al2[i]
-        aligned_seqs.append(str(al2_fin))
-    # Get the length of the aligned sequences
-    aligned_seq_length = len(first_seq)
-    # Initialize the position scoring matrix
-    matrix = np.zeros((22, aligned_seq_length))
-    # Iterate through the aligned sequences and count the amino acids at each position
-    for seq in aligned_seqs:
-        #print(seq)
-        for i in range(aligned_seq_length):
-            if i == len(seq):
-                break
-            amino_acid = seq[i]
-            if amino_acid.upper() not in aa_to_index.keys():
-                continue
-            else:
-                aa_index = aa_to_index[amino_acid.upper()]
-            matrix[aa_index, i] += 1
-    # Normalize the counts to get the frequency of each amino acid at each position
-    matrix /= len(aligned_seqs)
-    print(len(aligned_seqs))
-    matrix[20:,]=0
-
-    outdir = ".".join(msa_file.name.split('.')[:-1]) + ".csv"
-    np.savetxt(outdir, matrix[:21,:].T, delimiter=",")
-    return outdir
-
-def get_pssm(fasta_msa, input_pssm):
-
-    if input_pssm not in ['',None]:
-        outdir = input_pssm.name
-    else:
-        outdir = save_pssm(fasta_msa)
-
-    pssm = np.loadtxt(outdir, delimiter=",", dtype=float)
-    fig, ax = plt.subplots(figsize=(15,6))
-    plt.imshow(torch.permute(torch.tensor(pssm),(1,0)))
-
-    return fig, outdir
-
-
-
-
-# 히어로 능력치 계산 함수 추가
-def calculate_hero_stats(helix_bias, strand_bias, loop_bias, hydrophobic_score):
-    stats = {
-        'strength': strand_bias * 20,  # 베타시트 구조 기반
-        'flexibility': helix_bias * 20, # 알파헬릭스 구조 기반
-        'speed': loop_bias * 5,        # 루프 구조 기반
-        'defense': abs(hydrophobic_score) if hydrophobic_score else 0
-    }
-    return stats
-
-##toggle options
-def toggle_seq_input(choice):
-    if choice == "protein length":
-        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
-    elif choice == "custom sequence":
-        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
-
-def toggle_secondary_structure(choice):
-    if choice == "sliders":
-        return gr.update(visible=True, value=None),gr.update(visible=True, value=None),gr.update(visible=True, value=None),gr.update(visible=False, value=None)
-    elif choice == "explicit":
-        return gr.update(visible=False, value=None),gr.update(visible=False, value=None),gr.update(visible=False, value=None),gr.update(visible=True, value=None)
-
-
-
-def create_radar_chart(stats):
-    # 레이더 차트 생성 로직
-    categories = list(stats.keys())
-    values = list(stats.values())
-    
-    fig = go.Figure(data=go.Scatterpolar(
-        r=values,
-        theta=categories,
-        fill='toself'
-    ))
-    
-    fig.update_layout(
-        polar=dict(
-            radialaxis=dict(
-                visible=True,
-                range=[0, 1]
-            )),
-        showlegend=False
-    )
-    
-    return fig
-
-def generate_hero_description(name, stats, abilities):
-    # 히어로 설명 생성 로직
-    description = f"""
-    히어로 이름: {name}
-    
-    주요 능력:
-    - 근력: {'★' * int(stats['strength'] * 5)}
-    - 유연성: {'★' * int(stats['flexibility'] * 5)}
-    - 스피드: {'★' * int(stats['speed'] * 5)}
-    - 방어력: {'★' * int(stats['defense'] * 5)}
-    
-    특수 능력: {', '.join(abilities)}
-    """
-    return description
-
-def combined_generation(name, strength, flexibility, speed, defense, size, abilities,
-                       sequence, seq_len, helix_bias, strand_bias, loop_bias, 
-                       secondary_structure, aa_bias, aa_bias_potential, 
-                       num_steps, noise, hydrophobic_target_score, hydrophobic_potential,
-                       contigs, pssm, seq_mask, str_mask, rewrite_pdb):
-    try:
-        # protein_diffusion_model 실행
-        generator = protein_diffusion_model(
-            sequence=None,
-            seq_len=size,  # 히어로 크기를 seq_len으로 사용
-            helix_bias=flexibility,  # 히어로 유연성을 helix_bias로 사용
-            strand_bias=strength,    # 히어로 강도를 strand_bias로 사용
-            loop_bias=speed,         # 히어로 스피드를 loop_bias로 사용
-            secondary_structure=None,
-            aa_bias=None,
-            aa_bias_potential=None,
-            num_steps="25",
-            noise="normal",
-            hydrophobic_target_score=str(-defense),  # 히어로 방어력을 hydrophobic score로 사용
-            hydrophobic_potential="2",
-            contigs=None,
-            pssm=None,
-            seq_mask=None,
-            str_mask=None,
-            rewrite_pdb=None
-        )
-        
-        # 마지막 결과 가져오기
-        final_result = None
-        for result in generator:
-            final_result = result
-            
-        if final_result is None:
-            raise Exception("생성 결과가 없습니다")
-            
-        output_seq, output_pdb, structure_view, plddt_plot = final_result
-        
-        # 히어로 능력치 계산
-        stats = calculate_hero_stats(flexibility, strength, speed, defense)
-        
-        # 모든 결과 반환
-        return (
-            create_radar_chart(stats),  # 능력치 차트
-            generate_hero_description(name, stats, abilities),  # 히어로 설명
-            output_seq,  # 단백질 서열
-            output_pdb,  # PDB 파일
-            structure_view,  # 3D 구조
-            plddt_plot  # 신뢰도 차트
-        )
-    except Exception as e:
-        print(f"Error in combined_generation: {str(e)}")
-        return (
-            None,
-            f"에러: {str(e)}",
-            None,
-            None,
-            gr.HTML("에러가 발생했습니다"),
-            None
-        )
-
-with gr.Blocks(theme='ParityError/Interstellar') as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("# 🦸‍♂️ 슈퍼히어로 단백질 만들기")
-            
-            with gr.Tabs():
-                with gr.TabItem("🦸‍♂️ 히어로 디자인"):
-                    gr.Markdown("""
-                    ### ✨ 당신만의 특별한 히어로를 만들어보세요!
-                    각 능력치를 조절하면 히어로의 DNA가 자동으로 설계됩니다.
-                    """)
-                    
-                    # 히어로 기본 정보
-                    hero_name = gr.Textbox(
-                        label="히어로 이름", 
-                        placeholder="당신의 히어로 이름을 지어주세요!",
-                        info="히어로의 정체성을 나타내는 이름을 입력하세요"
-                    )
-                    
-                    # 능력치 설정
-                    gr.Markdown("### 💪 히어로 능력치 설정")
-                    with gr.Row():
-                        strength = gr.Slider(
-                            minimum=0.0, maximum=0.05, 
-                            label="💪 초강력(근력)", 
-                            value=0.02,
-                            info="단단한 베타시트 구조로 강력한 힘을 생성합니다"
-                        )
-                        flexibility = gr.Slider(
-                            minimum=0.0, maximum=0.05, 
-                            label="🤸‍♂️ 유연성", 
-                            value=0.02,
-                            info="나선형 알파헬릭스 구조로 유연한 움직임을 가능하게 합니다"
-                        )
-                    
-                    with gr.Row():
-                        speed = gr.Slider(
-                            minimum=0.0, maximum=0.20, 
-                            label="⚡ 스피드", 
-                            value=0.1,
-                            info="루프 구조로 빠른 움직임을 구현합니다"
-                        )
-                        defense = gr.Slider(
-                            minimum=-10, maximum=10, 
-                            label="🛡️ 방어력", 
-                            value=0,
-                            info="음수: 수중 활동에 특화, 양수: 지상 활동에 특화"
-                        )
-                    
-                    # 히어로 크기 설정
-                    hero_size = gr.Slider(
-                        minimum=50, maximum=200, 
-                        label="📏 히어로 크기", 
-                        value=100,
-                        info="히어로의 전체적인 크기를 결정합니다"
-                    )
-                    
-                    # 특수 능력 설정
-                    with gr.Accordion("🌟 특수 능력", open=False):
-                        gr.Markdown("""
-                        특수 능력을 선택하면 히어로의 DNA에 특별한 구조가 추가됩니다.
-                        - 자가 회복: 단백질 구조 복구 능력 강화
-                        - 원거리 공격: 특수한 구조적 돌출부 형성
-                        - 방어막 생성: 안정적인 보호층 구조 생성
-                        """)
-                        special_ability = gr.CheckboxGroup(
-                            choices=["자가 회복", "원거리 공격", "방어막 생성"],
-                            label="특수 능력 선택"
-                        )
-                    
-                    # 생성 버튼
-                    create_btn = gr.Button("🧬 히어로 생성!", variant="primary", scale=2)
-
-                with gr.TabItem("🧬 히어로 DNA 설계"):
-                    gr.Markdown("""
-                    ### 🧪 히어로 DNA 고급 설정
-                    히어로의 유전자 구조를 더 세밀하게 조정할 수 있습니다.
-                    """)
-                    
-                    seq_opt = gr.Radio(
-                        ["자동 설계", "직접 입력"],
-                        label="DNA 설계 방식",
-                        value="자동 설계"
-                    )
-
-                    sequence = gr.Textbox(
-                        label="DNA 시퀀스", 
-                        lines=1, 
-                        placeholder='사용 가능한 아미노산: A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y (X는 무작위)',
-                        visible=False
-                    )
-                    seq_len = gr.Slider(
-                        minimum=5.0, maximum=250.0, 
-                        label="DNA 길이", 
-                        value=100, 
-                        visible=True
-                    )
-                    
-                    with gr.Accordion(label='🦴 골격 구조 설정', open=True):
-                        gr.Markdown("""
-                        히어로의 기본 골격 구조를 설정합니다.
-                        - 나선형 구조: 유연하고 탄력있는 움직임
-                        - 병풍형 구조: 단단하고 강력한 힘
-                        - 고리형 구조: 빠르고 민첩한 움직임
-                        """)
-                        sec_str_opt = gr.Radio(
-                            ["슬라이더로 설정", "직접 입력"],
-                            label="골격 구조 설정 방식",
-                            value="슬라이더로 설정"
-                        )
-
-                        secondary_structure = gr.Textbox(
-                            label="골격 구조",
-                            lines=1,
-                            placeholder='H:나선형, S:병풍형, L:고리형, X:자동설정',
-                            visible=False
-                        )
-                        
-                        with gr.Column():
-                            helix_bias = gr.Slider(
-                                minimum=0.0, maximum=0.05,
-                                label="나선형 구조 비율",
-                                visible=True
-                            )
-                            strand_bias = gr.Slider(
-                                minimum=0.0, maximum=0.05,
-                                label="병풍형 구조 비율",
-                                visible=True
-                            )
-                            loop_bias = gr.Slider(
-                                minimum=0.0, maximum=0.20,
-                                label="고리형 구조 비율",
-                                visible=True
-                            )
-
-                    # 아미노산 구성 설정 추가
-                    with gr.Accordion(label='🧬 DNA 구성 설정', open=False):
-                        gr.Markdown("""
-                        특정 아미노산의 비율을 조절하여 히어로의 특성을 강화할 수 있습니다.
-                        예시: W0.2,E0.1 (트립토판 20%, 글루탐산 10%)
-                        """)
-                        with gr.Row():
-                            aa_bias = gr.Textbox(
-                                label="아미노산 비율", 
-                                lines=1, 
-                                placeholder='예시: W0.2,E0.1'
-                            )
-                            aa_bias_potential = gr.Textbox(
-                                label="강화 정도", 
-                                lines=1, 
-                                placeholder='1.0-5.0 사이 값 입력'
-                            )
-
-                    # 환경 적응력 설정 추가
-                    with gr.Accordion(label='🌍 환경 적응력 설정', open=False):
-                        gr.Markdown("""
-                        히어로의 환경 적응력을 조절합니다.
-                        음수: 수중 활동에 특화, 양수: 지상 활동에 특화
-                        """)
-                        with gr.Row():
-                            hydrophobic_target_score = gr.Textbox(
-                                label="환경 적응 점수",
-                                lines=1,
-                                placeholder='예시: -5 (수중 활동에 특화)'
-                            )
-                            hydrophobic_potential = gr.Textbox(
-                                label="적응력 강화 정도",
-                                lines=1,
-                                placeholder='1.0-2.0 사이 값 입력'
-                            )
-
-                    # 확산 매개변수 설정
-                    with gr.Accordion(label='⚙️ 고급 설정', open=False):
-                        gr.Markdown("""
-                        DNA 생성 과정의 세부 매개변수를 조정합니다.
-                        """)
-                        with gr.Row():
-                            num_steps = gr.Textbox(
-                                label="생성 단계",
-                                lines=1,
-                                placeholder='25 이하 권장'
-                            )
-                            noise = gr.Dropdown(
-                                ['normal','gmm2 [-1,1]','gmm3 [-1,0,1]'],
-                                label='노이즈 타입',
-                                value='normal'
-                            )
-
-                with gr.TabItem("🧪 히어로 유전자 강화"):
-                    gr.Markdown("""
-                    ### ⚡ 기존 히어로의 DNA 활용
-                    강력한 히어로의 DNA 일부를 새로운 히어로에게 이식합니다.
-                    """)
-                    
-                    gr.Markdown("공개된 히어로 DNA 데이터베이스에서 코드를 찾을 수 있습니다")
-                    pdb_id_code = gr.Textbox(
-                        label="히어로 DNA 코드",
-                        lines=1,
-                        placeholder='기존 히어로의 DNA 코드를 입력하세요 (예: 1DPX)'
-                    )
-                    
-                    gr.Markdown("이식하고 싶은 DNA 영역을 선택하고 새로운 DNA를 추가할 수 있습니다")
-                    contigs = gr.Textbox(
-                        label="이식할 DNA 영역",
-                        lines=1,
-                        placeholder='예시: 15,A3-10,20-30'
-                    )
-                    
-                    with gr.Row():
-                        seq_mask = gr.Textbox(
-                            label='능력 재설계',
-                            lines=1,
-                            placeholder='선택한 영역의 능력을 새롭게 디자인'
-                        )
-                        str_mask = gr.Textbox(
-                            label='구조 재설계',
-                            lines=1,
-                            placeholder='선택한 영역의 구조를 새롭게 디자인'
-                        )
-                    
-                    preview_viewer = gr.HTML()
-                    rewrite_pdb = gr.File(label='히어로 DNA 파일')
-                    preview_btn = gr.Button("🔍 미리보기", variant="secondary")
-
-                with gr.TabItem("👑 히어로 가문"):
-                    gr.Markdown("""
-                    ### 🏰 위대한 히어로 가문의 유산
-                    강력한 히어로 가문의 특성을 계승하여 새로운 히어로를 만듭니다.
-                    """)
-                    
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown("히어로 가문의 DNA 정보가 담긴 파일을 업로드하세요")
-                            fasta_msa = gr.File(label='가문 DNA 데이터')
-                        with gr.Column():
-                            gr.Markdown("이미 분석된 가문 특성 데이터가 있다면 업로드하세요")
-                            input_pssm = gr.File(label='가문 특성 데이터')
-                    
-                    pssm = gr.File(label='분석된 가문 특성')
-                    pssm_view = gr.Plot(label='가문 특성 분석 결과')
-                    pssm_gen_btn = gr.Button("✨ 가문 특성 분석", variant="secondary")
-
-        with gr.Column():
-            gr.Markdown("## 🦸‍♂️ 히어로 프로필")
-            
-            # 능력치 레이더 차트
-            hero_stats = gr.Plot(label="능력치 분석")
-            
-            # 히어로 설명
-            hero_description = gr.Textbox(label="히어로 특성", lines=3)
-
-            gr.Markdown("## 🧬 히어로 DNA 분석 결과")
-            gr.Markdown("#### ⚡ DNA 안정성 점수")
-            plddt_plot = gr.Plot(label='안정성 분석')
-            gr.Markdown("#### 📝 DNA 시퀀스")
-            output_seq = gr.Textbox(label="DNA 서열")
-            gr.Markdown("#### 💾 DNA 데이터")
-            output_pdb = gr.File(label="DNA 파일")
-            gr.Markdown("#### 🔬 DNA 구조")
-            output_viewer = gr.HTML()
-
-    # 이벤트 연결
-    preview_btn.click(get_motif_preview,[pdb_id_code, contigs],[preview_viewer, rewrite_pdb])
-    pssm_gen_btn.click(get_pssm,[fasta_msa,input_pssm],[pssm_view, pssm])
-
-    # generate_hero와 protein_diffusion_model을 combined_generation으로 통합
-    create_btn.click(
-        combined_generation,
-        inputs=[
-            hero_name, strength, flexibility, speed, defense, hero_size, special_ability,
-            sequence, seq_len, helix_bias, strand_bias, loop_bias, 
-            secondary_structure, aa_bias, aa_bias_potential, 
-            num_steps, noise, hydrophobic_target_score, hydrophobic_potential,
-            contigs, pssm, seq_mask, str_mask, rewrite_pdb
-        ],
-        outputs=[
-            hero_stats, 
-            hero_description, 
-            output_seq,
-            output_pdb,
-            output_viewer,
-            plddt_plot
-        ]
-    )
-
-demo.queue()
-demo.launch(debug=True)
+import os
+exec(os.environ.get('APP'))