Update app.py

app.py

@@ -539,3 +539,155 @@ def combined_generation(name, strength, flexibility, speed, defense, size, abili
         )
 
 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"):
+                    # 히어로 기본 정보
+                    hero_name = gr.Textbox(label="히어로 이름", placeholder="당신의 히어로 이름을 지어주세요!")
+                    
+                    # 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)
+                        flexibility = gr.Slider(minimum=0.0, maximum=0.05, label="🤸‍♂️ 유연성", value=0.02)
+                    
+                    with gr.Row():
+                        speed = gr.Slider(minimum=0.0, maximum=0.20, label="⚡ 스피드", value=0.1)
+                        defense = gr.Slider(minimum=-10, maximum=10, label="🛡️ 방어력", value=0)
+                    
+                    # 히어로 크기 설정
+                    hero_size = gr.Slider(minimum=50, maximum=200, label="히어로 크기", value=100)
+                    
+                    # 특수 능력 설정
+                    with gr.Accordion("🌟 특수 능력", open=False):
+                        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")
+
+                with gr.TabItem("Inputs"):
+                    gr.Markdown("""## INPUTS""")
+                    gr.Markdown("""#### Start Sequence
+                                Specify the protein length for complete unconditional generation, or scaffold a motif (or your name) using the custom sequence input""")
+                    seq_opt = gr.Radio(["protein length","custom sequence"], label="How would you like to specify the starting sequence?", value='protein length')
+
+                    sequence = gr.Textbox(label="custom sequence", lines=1, placeholder='AMINO ACIDS: A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y\n  MASK TOKEN: X', visible=False)
+                    seq_len = gr.Slider(minimum=5.0, maximum=250.0, label="protein length", value=100, visible=True)
+                    
+                    seq_opt.change(fn=toggle_seq_input,
+                                            inputs=[seq_opt],
+                                            outputs=[seq_len, sequence],
+                                            queue=False)
+
+                    gr.Markdown("""### Optional Parameters""")
+                    with gr.Accordion(label='Secondary Structure',open=True):
+                        gr.Markdown("""Try changing the sliders or inputing explicit secondary structure conditioning for each residue""")
+                        sec_str_opt = gr.Radio(["sliders","explicit"], label="How would you like to specify secondary structure?", value='sliders')
+
+                        secondary_structure = gr.Textbox(label="secondary structure", lines=1, placeholder='HELIX = H  STRAND = S  LOOP = L  MASK = X(must be the same length as input sequence)', visible=False)
+                        
+                        with gr.Column():
+                            helix_bias = gr.Slider(minimum=0.0, maximum=0.05, label="helix bias", visible=True)
+                            strand_bias = gr.Slider(minimum=0.0, maximum=0.05, label="strand bias", visible=True)
+                            loop_bias = gr.Slider(minimum=0.0, maximum=0.20, label="loop bias", visible=True)
+                    
+                        sec_str_opt.change(fn=toggle_secondary_structure,
+                                                inputs=[sec_str_opt],
+                                                outputs=[helix_bias,strand_bias,loop_bias,secondary_structure],
+                                                queue=False)
+                        
+                    with gr.Accordion(label='Amino Acid Compositional Bias',open=False):
+                        gr.Markdown("""Bias sequence composition for particular amino acids by specifying the one letter code followed by the fraction to bias. This can be input as a list for example: W0.2,E0.1""")
+                        with gr.Row():
+                            aa_bias = gr.Textbox(label="aa bias", lines=1, placeholder='specify one letter AA and fraction to bias, for example W0.1 or M0.1,K0.1' )
+                            aa_bias_potential = gr.Textbox(label="aa bias scale", lines=1, placeholder='AA Bias potential scale (recomended range 1.0-5.0)')
+
+                    with gr.Accordion(label='Hydrophobic Bias',open=False):
+                        gr.Markdown("""Bias for or against hydrophobic composition, to get more soluble proteins, bias away with a negative target score (ex. -5)""")
+                        with gr.Row():
+                            hydrophobic_target_score = gr.Textbox(label="hydrophobic score", lines=1, placeholder='hydrophobic score to target (negative score is good for solublility)')
+                            hydrophobic_potential = gr.Textbox(label="hydrophobic potential scale", lines=1, placeholder='hydrophobic potential scale (recomended range 1.0-2.0)')
+                    
+                    with gr.Accordion(label='Diffusion Params',open=False):
+                        gr.Markdown("""Increasing T to more steps can be helpful for harder design challenges, sampling from different distributions can change the sequence and structural composition""")
+                        with gr.Row():
+                            num_steps = gr.Textbox(label="T", lines=1, placeholder='number of diffusion steps (25 or less will speed things up)')
+                            noise = gr.Dropdown(['normal','gmm2 [-1,1]','gmm3 [-1,0,1]'], label='noise type', value='normal')
+
+                with gr.TabItem("Motif Selection"):
+                    gr.Markdown("""### Motif Selection Preview""")
+                    gr.Markdown('Contigs explained: to grab residues (seq and str) on a pdb chain you will provide the chain letter followed by a range of residues as indexed in the pdb file for example (A3-10) is the syntax to select residues 3-10 on chain A (the chain always needs to be specified). To add diffused residues to either side of this motif you can specify a range or discrete value without a chain letter infront. To add 15 residues before the motif and 20-30 residues (randomly sampled) after use the following syntax: 15,A3-10,20-30 commas are used to separate regions selected from the pdb and designed (diffused) resiudes which will be added. ')
+                    pdb_id_code = gr.Textbox(label="PDB ID", lines=1, placeholder='INPUT PDB ID TO FETCH (ex. 1DPX)', visible=True)
+                    contigs = gr.Textbox(label="contigs", lines=1, placeholder='specify contigs to grab particular residues from pdb ()', visible=True)
+                    gr.Markdown('Using the same contig syntax, seq or str of input motif residues can be masked, allowing the model to hold strucutre fixed and design sequence or vice-versa')
+                    with gr.Row():
+                        seq_mask = gr.Textbox(label='seq mask',lines=1,placeholder='input residues to mask sequence')
+                        str_mask = gr.Textbox(label='str mask',lines=1,placeholder='input residues to mask structure')
+                    preview_viewer = gr.HTML()
+                    rewrite_pdb = gr.File(label='PDB file')
+                    preview_btn = gr.Button("Preview Motif")
+
+                with gr.TabItem("MSA to PSSM"):
+                    gr.Markdown("""### MSA to PSSM Generation""")
+                    gr.Markdown('input either an MSA or PSSM to guide the model toward generating samples within your family of interest')
+                    with gr.Row():
+                        fasta_msa = gr.File(label='MSA')
+                        input_pssm = gr.File(label='PSSM (.csv)')
+                    pssm = gr.File(label='Generated PSSM')
+                    pssm_view = gr.Plot(label='PSSM Viewer')
+                    pssm_gen_btn = gr.Button("Generate PSSM")
+
+        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)
+            
+            # 다운로드 버튼
+            download_btn = gr.Button("히어로 데이터 다운로드")
+
+            gr.Markdown("""## OUTPUTS""")
+            gr.Markdown("""#### Confidence score for generated structure at each timestep""")
+            plddt_plot = gr.Plot(label='plddt at step t')
+            gr.Markdown("""#### Output protein sequnece""")
+            output_seq = gr.Textbox(label="sequence")
+            gr.Markdown("""#### Download PDB file""")
+            output_pdb = gr.File(label="PDB file")
+            gr.Markdown("""#### Structure viewer""")
+            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)