Upgrade to Gradio 6.9.0 with full API migration

- README: sdk_version 3.36.1 -> 6.9.0, keep python_version 3.10
- requirements: torch 2.5.0, dgl 2.2.1, pydantic>=2.0, numpy<2
(e3nn stays at 0.3.3 for checkpoint compatibility)
- app.py Gradio migration:
- .style() calls -> direct constructor params (Row equal_height)
- gr.update() -> component constructors (Slider, Textbox)
- gr.TabItem -> gr.Tab
- theme moved from Blocks() to launch()
- Removed redundant demo.queue()
- File upload .name -> helper for path extraction
- Switched to gr.themes.Soft() (ParityError/Interstellar incompatible)
- Removed dead commented-out code blocks
- General cleanup

Made-with: Cursor

.gitignore

@@ -0,0 +1 @@
+.venv/

README.md

@@ -5,7 +5,7 @@ thumbnail: http://files.ipd.uw.edu/pub/sequence_diffusion/figs/diffusion_landsca
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 6.9.0
 python_version: "3.10"
 app_file: app.py
 pinned: false

app.py

@@ -32,7 +32,6 @@ 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
@@ -43,25 +42,27 @@ args['loop_bias'] = 0.0
 args['helix_bias'] = 0.0
 
 
+def _get_file_path(file_obj):
+    """Extract file path from a Gradio file upload (handles both old and new Gradio formats)."""
+    if isinstance(file_obj, str):
+        return file_obj
+    return file_obj.name
 
-def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bias, 
-                    secondary_structure, aa_bias, aa_bias_potential, 
-                    #target_charge, target_ph, charge_potential, 
+
+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
@@ -74,8 +75,6 @@ def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bia
     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)
@@ -92,10 +91,9 @@ def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bia
     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
+        S.args['pdb'] = _get_file_path(rewrite_pdb)
 
     if seq_mask not in ['',None]:
         S.args['inpaint_seq'] = [seq_mask]
@@ -113,9 +111,7 @@ def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bia
         else:
             dseq = L - len(secondary_structure)
             secondary_structure += secondary_structure[-1]*dseq
-    
 
-    # potentials
     potential_list = []
     potential_bias_list = []
 
@@ -125,49 +121,33 @@ def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bia
         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
-        
+        S.args['PSSM'] = _get_file_path(pssm)
 
     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]
@@ -181,8 +161,6 @@ def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bia
         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
@@ -191,24 +169,21 @@ def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bia
         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()
     yield output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
 
@@ -224,47 +199,27 @@ def get_plddt_plot(plddt_data, max_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.setStyle({'model': -1}, {"cartoon": {'colorscheme':{'prop':'b','gradient':'roygb','min':0,'max':1}}})
     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
-    
+    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""
+
     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()
@@ -276,11 +231,7 @@ def get_motif_preview(pdb_id, contigs):
     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'}
@@ -288,9 +239,8 @@ def get_motif_preview(pdb_id, contigs):
     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
-    
+    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""
+
     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
@@ -306,25 +256,21 @@ def fetch_pdb(pdb_id=None):
 
 # MSA AND PSSM GUIDANCE
 def save_pssm(file_upload):
-    filename = file_upload.name
-    orig_name = file_upload.orig_name
+    filename = _get_file_path(file_upload)
+    orig_name = file_upload.orig_name if hasattr(file_upload, 'orig_name') else filename
     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"))
+    records = list(SeqIO.parse(_get_file_path(msa_file), "fasta"))
 
-    assert len(records) >= 1, "MSA must contain more than one protein sequecne."
+    assert len(records) >= 1, "MSA must contain more than one protein sequence."
 
     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
@@ -343,13 +289,9 @@ def msa_to_pssm(msa_file):
                 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
@@ -359,19 +301,18 @@ def msa_to_pssm(msa_file):
             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"
+    msa_path = _get_file_path(msa_file)
+    outdir = ".".join(msa_path.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
+        outdir = _get_file_path(input_pssm)
     else:
         outdir = save_pssm(fasta_msa)
 
@@ -382,110 +323,96 @@ def get_pssm(fasta_msa, input_pssm):
     return fig, outdir
 
 
-#toggle options
 def toggle_seq_input(choice):
     if choice == "protein length":
-        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
+        return gr.Slider(visible=True, value=None), gr.Textbox(visible=False, value=None)
     elif choice == "custom sequence":
-        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
+        return gr.Slider(visible=False, value=None), gr.Textbox(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)
+        return gr.Slider(visible=True, value=None),gr.Slider(visible=True, value=None),gr.Slider(visible=True, value=None),gr.Textbox(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)
+        return gr.Slider(visible=False, value=None),gr.Slider(visible=False, value=None),gr.Slider(visible=False, value=None),gr.Textbox(visible=True, value=None)
 
 
-# Define the Gradio interface
-with gr.Blocks(theme='ParityError/Interstellar') as demo:
-    
-    gr.Markdown(f"""# Protein Generation via Diffusion in Sequence Space""")
+with gr.Blocks() as demo:
+
+    gr.Markdown("# Protein Generation via Diffusion in Sequence Space")
 
     with gr.Row():
         with gr.Column(min_width=500):
-            gr.Markdown(f"""
+            gr.Markdown("""
                     ## How does it work?\n
-                    !!! [PAPER](https://www.nature.com/articles/s41587-024-02395-w) !!!
+                    --- [PREPRINT](https://biorxiv.org/content/10.1101/2023.05.08.539766v1) ---
                     Protein sequence and structure co-generation is a long outstanding problem in the field of protein design. By implementing [ddpm](https://arxiv.org/abs/2006.11239) style diffusion over protein seqeuence space we generate protein sequence and structure pairs. Starting with [RoseTTAFold](https://www.science.org/doi/10.1126/science.abj8754), a protein structure prediction network, we finetuned it to predict sequence and structure given a partially noised sequence. By applying losses to both the predicted sequence and structure the model is forced to generate meaningful pairs. Diffusing in sequence space makes it easy to implement potentials to guide the diffusive process toward particular amino acid composition, net charge, and more! Furthermore, you can sample proteins from a family of sequences or even train a small sequence to function classifier to guide generation toward desired sequences.
                     ![fig1](http://files.ipd.uw.edu/pub/sequence_diffusion/figs/diffusion_landscape.png)
-                    
+
                     ## How to use it?\n
                     A user can either design a custom input sequence to diffuse from or specify a length below. To scaffold a sequence use the following format where X represent residues to diffuse: XXXXXXXXSCIENCESCIENCEXXXXXXXXXXXXXXXXXXX. You can even design a protein with your name XXXXXXXXXXXXNAMEHEREXXXXXXXXXXXXX!
-                    
+
                     ### Acknowledgements\n
                     Thank you to Simon Dürr and the Hugging Face team for setting us up with a community GPU grant!
                     """)
-            
+
         gr.Markdown("""
         ## Model in Action
         ![gif1](http://files.ipd.uw.edu/pub/sequence_diffusion/figs/seqdiff_anim_720p.gif)
         """)
 
-    #with gr.Row().style(equal_height=False):
-    with gr.Row():
+    with gr.Row(equal_height=False):
         with gr.Column():
             with gr.Tabs():
-                with gr.TabItem("Inputs"):
-                    gr.Markdown("""## INPUTS""")
+                with gr.Tab("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)
+                                            outputs=[seq_len, sequence])
 
-                    gr.Markdown("""### Optional Parameters""")
+                    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""")
+                        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)
-                        
+                                                outputs=[helix_bias,strand_bias,loop_bias,secondary_structure])
+
                     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""")
+                        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='Charge Bias',open=False):
-                        gr.Markdown("""Bias for a specified net charge at a particular pH using the boxes below""")
-                        with gr.Row():
-                            target_charge = gr.Textbox(label="net charge", lines=1, placeholder='net charge to target')
-                            target_ph = gr.Textbox(label="pH", lines=1, placeholder='pH at which net charge is desired')
-                            charge_potential = gr.Textbox(label="charge potential scale", lines=1, placeholder='charge 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)""")
+                        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""")
+                        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"):
+                            noise = gr.Dropdown(['normal','gmm2 [-1,1]','gmm3 [-1,0,1]'], label='noise type', value='normal')
+
+                with gr.Tab("Motif Selection"):
 
-                    gr.Markdown("""### Motif Selection Preview""")
+                    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)
@@ -497,8 +424,8 @@ with gr.Blocks(theme='ParityError/Interstellar') as demo:
                     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""")
+                with gr.Tab("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')
@@ -510,92 +437,45 @@ with gr.Blocks(theme='ParityError/Interstellar') as demo:
 
             btn = gr.Button("GENERATE")
 
-        #with gr.Row():
         with gr.Column():
-            gr.Markdown("""## OUTPUTS""")
-            gr.Markdown("""#### Confidence score for generated structure at each timestep""")
+            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""")
+            gr.Markdown("#### Output protein sequence")
             output_seq = gr.Textbox(label="sequence")
-            gr.Markdown("""#### Download PDB file""")
+            gr.Markdown("#### Download PDB file")
             output_pdb = gr.File(label="PDB file")
-            gr.Markdown("""#### Structure viewer""")
+            gr.Markdown("#### Structure viewer")
             output_viewer = gr.HTML()
-    '''
-    gr.Markdown("""### Don't know where to get started? Click on an example below to try it out!""")
-    gr.Examples(
-        [["","125",0.0,0.0,0.2,"","","","20","normal",'','','',None,'','',None],
-         ["","100",0.0,0.0,0.0,"","W0.2","2","20","normal",'','','',None,'','',None],
-#         ["","100",0.0,0.0,0.0,
-#        "XXHHHHHHHHHXXXXXXXHHHHHHHHHXXXXXXXHHHHHHHHXXXXSSSSSSSSSSSXXXXXXXXSSSSSSSSSSSSXXXXXXXSSSSSSSSSXXXXXXX",
-#                "","","25","normal",'','','',None,'','',None],
-#         ["XXXXXXXXXXXXXXXXXXXXXXXXXIPDXXXXXXXXXXXXXXXXXXXXXXPEPSEQXXXXXXXXXXXXXXXXXXXXXXXXXXIPDXXXXXXXXXXXXXXXXXXX",
-#          "",0.0,0.0,0.0,"","","","25","normal",'','','',None,'','',None],
-#         ["","",0.0,0.0,0.0,"","","","25","normal",'','',
-#          '9,D10-11,8,D20-20,4,D25-35,65,D101-101,2,D104-105,8,D114-116,15,D132-138,6,D145-145,2,D148-148,12,D161-161,3',
-#          './tmp/PSSM_lysozyme.csv',
-#          'D25-25,D27-31,D33-35,D132-137',
-#          'D26-26','./tmp/150l.pdb']
-         ],
-        inputs=[sequence, 
-                seq_len, 
-                helix_bias, 
-                strand_bias, 
-                loop_bias, 
-                secondary_structure, 
-                aa_bias, 
-                aa_bias_potential,
-                #target_charge, 
-                #target_ph, 
-                #charge_potential, 
-                num_steps, 
-                noise, 
-                hydrophobic_target_score, 
-                hydrophobic_potential,
-                contigs,
-                pssm,
-                seq_mask,
-                str_mask,
-                rewrite_pdb],
-        outputs=[output_seq,
-                 output_pdb,
-                 output_viewer,
-                 plddt_plot],
-        fn=protein_diffusion_model,
-        ) 
-    '''
+
     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])
 
-    btn.click(protein_diffusion_model, 
-                [sequence, 
-                 seq_len, 
-                 helix_bias, 
-                 strand_bias, 
-                 loop_bias, 
-                 secondary_structure, 
-                 aa_bias, 
-                 aa_bias_potential, 
-                 #target_charge, 
-                 #target_ph, 
-                 #charge_potential, 
-                 num_steps, 
-                 noise, 
-                 hydrophobic_target_score, 
+    btn.click(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, 
+                 seq_mask,
                  str_mask,
-                 rewrite_pdb], 
+                 rewrite_pdb],
                 [output_seq,
                  output_pdb,
                  output_viewer,
                  plddt_plot])
 
-demo.queue()
-demo.launch(debug=True)
-
-
-
+demo.launch(
+    debug=True,
+    theme=gr.themes.Soft(),
+)

requirements.txt

@@ -1,10 +1,10 @@
-torch==2.0.0
+torch==2.5.0
 e3nn==0.3.3
-dgl==1.0.2
+dgl==2.2.1
 pynvml==11.0.0
 decorator==5.1.0
 icecream
 biopython
 py3Dmol
-pydantic<2
-numpy==1.24.1
+pydantic>=2.0
+numpy<2