Update app.py

app.py

@@ -2,14 +2,14 @@ import torch
 import random
 import pandas as pd
 from utils import create_vocab, setup_seed
-from dataset_mlm import  get_paded_token_idx_gen, add_tokens_to_vocab
+from dataset_mlm import get_paded_token_idx_gen, add_tokens_to_vocab
 import gradio as gr
 from gradio_rangeslider import RangeSlider
 import time
 
 is_stopped = False
 
-seed = random.randint(0,100000)
+seed = random.randint(0, 100000)
 setup_seed(seed)
 
 def temperature_sampling(logits, temperature):
@@ -32,158 +32,146 @@ def CTXGen(X0, X1, X2, τ, g_num, model_name):
     vocab_mlm = add_tokens_to_vocab(vocab_mlm)
     save_path = model_name
     train_seqs = pd.read_csv('C0_seq.csv')
-    train_seq = train_seqs['Seq'].tolist()
+    train_seq = set(train_seqs['Seq'].tolist())  # 使用集合加快查找速度
     model = torch.load(save_path, map_location=torch.device('cpu'))
     model = model.to(device)
+    model.eval()
 
     X3 = "X" * len(X0)
-    msa_data = pd.read_csv('conoData_C0.csv') 
+    msa_data = pd.read_csv('conoData_C0.csv')
     msa = msa_data['Sequences'].tolist()
     msa = [x for x in msa if x.startswith(f"{X1}|{X2}")]
     if not msa:
-        X4 = ""
-        X5 = ""
-        X6 = ""
+        X4, X5, X6 = "", "", ""
     else:
         msa = random.choice(msa)
-        X4 = msa.split("|")[3]
-        X5 = msa.split("|")[4]
-        X6 = msa.split("|")[5]
-    model.eval()
-    with torch.no_grad():
-        new_seq = None
-        IDs = []
-        generated_seqs = []
-        generated_seqs_FINAL = []
-        cls_probability_all = []
-        act_probability_all = []
-        count = 0
-        gen_num = g_num
-        NON_AA = ["B","O","U","Z","X",'<K16>', '<α1β1γδ>', '<Ca22>', '<AChBP>', '<K13>', '<α1BAR>', '<α1β1ε>', '<α1AAR>', '<GluN3A>', '<α4β2>',
-                        '<GluN2B>', '<α75HT3>', '<Na14>', '<α7>', '<GluN2C>', '<NET>', '<NavBh>', '<α6β3β4>', '<Na11>', '<Ca13>', 
-                        '<Ca12>', '<Na16>', '<α6α3β2>', '<GluN2A>', '<GluN2D>', '<K17>', '<α1β1δε>', '<GABA>', '<α9>', '<K12>', 
-                        '<Kshaker>', '<α3β4>', '<Na18>', '<α3β2>', '<α6α3β2β3>', '<α1β1δ>', '<α6α3β4β3>', '<α2β2>','<α6β4>', '<α2β4>',
-                        '<Na13>', '<Na12>', '<Na15>', '<α4β4>', '<α7α6β2>', '<α1β1γ>', '<NaTTXR>', '<K11>', '<Ca23>', 
-                        '<α9α10>','<α6α3β4>', '<NaTTXS>', '<Na17>','<high>','<low>','[UNK]','[SEP]','[PAD]','[CLS]','[MASK]']
-        
-        seq_parent = [f"{X1}|{X2}|{X0}|{X4}|{X5}|{X6}"]
-        padded_seqseq_parent, _, idx_msaseq_parent, _ = get_paded_token_idx_gen(vocab_mlm, seq_parent, new_seq)
-        idx_msaseq_parent = torch.tensor(idx_msaseq_parent).unsqueeze(0).to(device)
-        seqseq_parent = ["[MASK]" if i=="X" else i for i in padded_seqseq_parent]
-
-        seqseq_parent[1] = "[MASK]"
-        input_ids_parent = vocab_mlm.__getitem__(seqseq_parent)
-        logits_parent = model(torch.tensor([input_ids_parent]).to(device), idx_msaseq_parent)
-
-        cls_mask_logits_parent = logits_parent[0, 1, :]
-        cls_probability_parent, cls_mask_probs_parent = torch.topk((torch.softmax(cls_mask_logits_parent, dim=-1)), k=85)
-
-        seqseq_parent[2] = "[MASK]"
-        input_ids_parent = vocab_mlm.__getitem__(seqseq_parent)
-        logits_parent = model(torch.tensor([input_ids_parent]).to(device), idx_msaseq_parent)
-        act_mask_logits_parent = logits_parent[0, 2, :]
-        act_probability_parent, act_mask_probs_parent = torch.topk((torch.softmax(act_mask_logits_parent, dim=-1)), k=2)
-
-        cls_pos_parent = vocab_mlm.to_tokens(list(cls_mask_probs_parent))
-        act_pos_parent = vocab_mlm.to_tokens(list(act_mask_probs_parent))
-
-        cls_proba_parent = cls_probability_parent[cls_pos_parent.index(X1)].item()
-        act_proba_parent = act_probability_parent[act_pos_parent.index(X2)].item()
-
-        start_time = time.time()
-        while count < gen_num:
-            gen_len = len(X0)
+        X4, X5, X6 = msa.split("|")[3:6]
+
+    NON_AA = {"B", "O", "U", "Z", "X", '<K16>', '<α1β1γδ>', '<Ca22>', '<AChBP>', '<K13>', '<α1BAR>', '<α1β1ε>', '<α1AAR>', '<GluN3A>', '<α4β2>',
+              '<GluN2B>', '<α75HT3>', '<Na14>', '<α7>', '<GluN2C>', '<NET>', '<NavBh>', '<α6β3β4>', '<Na11>', '<Ca13>', '<Ca12>', '<Na16>', '<α6α3β2>',
+              '<GluN2A>', '<GluN2D>', '<K17>', '<α1β1δε>', '<GABA>', '<α9>', '<K12>', '<Kshaker>', '<α3β4>', '<Na18>', '<α3β2>', '<α6α3β2β3>', '<α1β1δ>',
+              '<α6α3β4β3>', '<α2β2>', '<α6β4>', '<α2β4>', '<Na13>', '<Na12>', '<Na15>', '<α4β4>', '<α7α6β2>', '<α1β1γ>', '<NaTTXR>', '<K11>', '<Ca23>',
+              '<α9α10>', '<α6α3β4>', '<NaTTXS>', '<Na17>', '<high>', '<low>', '[UNK]', '[SEP]', '[PAD]', '[CLS]', '[MASK]'}
+
+    seq_parent = [f"{X1}|{X2}|{X0}|{X4}|{X5}|{X6}"]
+    padded_seqseq_parent, _, idx_msaseq_parent, _ = get_paded_token_idx_gen(vocab_mlm, seq_parent, None)
+    idx_msaseq_parent = torch.tensor(idx_msaseq_parent).unsqueeze(0).to(device)
+    seqseq_parent = ["[MASK]" if i == "X" else i for i in padded_seqseq_parent]
+
+    seqseq_parent[1] = "[MASK]"
+    input_ids_parent = vocab_mlm.__getitem__(seqseq_parent)
+    logits_parent = model(torch.tensor([input_ids_parent]).to(device), idx_msaseq_parent)
+    cls_mask_logits_parent = logits_parent[0, 1, :]
+    cls_probability_parent, cls_mask_probs_parent = torch.topk((torch.softmax(cls_mask_logits_parent, dim=-1)), k=85)
+
+    seqseq_parent[2] = "[MASK]"
+    input_ids_parent = vocab_mlm.__getitem__(seqseq_parent)
+    logits_parent = model(torch.tensor([input_ids_parent]).to(device), idx_msaseq_parent)
+    act_mask_logits_parent = logits_parent[0, 2, :]
+    act_probability_parent, act_mask_probs_parent = torch.topk((torch.softmax(act_mask_logits_parent, dim=-1)), k=2)
+
+    cls_pos_parent = vocab_mlm.to_tokens(list(cls_mask_probs_parent))
+    act_pos_parent = vocab_mlm.to_tokens(list(act_mask_probs_parent))
+
+    cls_proba_parent = cls_probability_parent[cls_pos_parent.index(X1)].item()
+    act_proba_parent = act_probability_parent[act_pos_parent.index(X2)].item()
+
+    start_time = time.time()
+    count = 0
+    generated_seqs_FINAL = []
+    cls_probability_all = []
+    act_probability_all = []
+    IDs = []
+
+    while count < g_num:
+        if is_stopped:
+            return pd.DataFrame(), "output.csv"
+
+        if time.time() - start_time > 1200:
+            break
+
+        seq = [f"{X1}|{X2}|{X3}|{X4}|{X5}|{X6}"]
+        padded_seq, _, _, _ = get_paded_token_idx_gen(vocab_mlm, seq, None)
+        input_text = ["[MASK]" if i == "X" else i for i in padded_seq]
+
+        gen_length = len(input_text)
+        length = gen_length - sum(1 for x in input_text if x != '[MASK]')
+        for i in range(length):
             if is_stopped:
                 return pd.DataFrame(), "output.csv"
 
-            if time.time() - start_time > 1200:
-                break
-
-            seq = [f"{X1}|{X2}|{X3}|{X4}|{X5}|{X6}"]
-            vocab_mlm.token_to_idx["X"] = 4
-
-            padded_seq, _, _, _ = get_paded_token_idx_gen(vocab_mlm, seq, new_seq)
-            input_text = ["[MASK]" if i=="X" else i for i in padded_seq]
-
-            gen_length = len(input_text)
-            length = gen_length - sum(1 for x in input_text if x != '[MASK]')
-            for i in range(length):
-                if is_stopped:
-                    return pd.DataFrame(), "output.csv"
-                
-                _, idx_seq, idx_msa, attn_idx = get_paded_token_idx_gen(vocab_mlm, seq, new_seq)
-                idx_seq = torch.tensor(idx_seq).unsqueeze(0).to(device)
-                idx_msa = torch.tensor(idx_msa).unsqueeze(0).to(device)
-                attn_idx = torch.tensor(attn_idx).to(device)
-
-                mask_positions = [j for j in range(gen_length) if input_text[j] == "[MASK]"]
-                mask_position = torch.tensor([mask_positions[torch.randint(len(mask_positions), (1,))]])
-                
-                logits = model(idx_seq,idx_msa, attn_idx) 
-                mask_logits = logits[0, mask_position.item(), :] 
-
-                predicted_token_id = temperature_sampling(mask_logits, τ)
-
-                predicted_token = vocab_mlm.to_tokens(int(predicted_token_id))
-                input_text[mask_position.item()] = predicted_token
-                padded_seq[mask_position.item()] = predicted_token.strip()
-                new_seq = padded_seq
-            generated_seq = input_text
-        
-            generated_seq[1] = "[MASK]"
-            input_ids = vocab_mlm.__getitem__(generated_seq)
-            logits = model(torch.tensor([input_ids]).to(device), idx_msa)
-            cls_mask_logits = logits[0, 1, :]
-            cls_probability, cls_mask_probs = torch.topk((torch.softmax(cls_mask_logits, dim=-1)), k=10)
-
-            generated_seq[2] = "[MASK]"
-            input_ids = vocab_mlm.__getitem__(generated_seq)
-            logits = model(torch.tensor([input_ids]).to(device), idx_msa)
-            act_mask_logits = logits[0, 2, :]
-            act_probability, act_mask_probs = torch.topk((torch.softmax(act_mask_logits, dim=-1)), k=2)
-
-            cls_pos = vocab_mlm.to_tokens(list(cls_mask_probs))
-            act_pos = vocab_mlm.to_tokens(list(act_mask_probs))
-            
-            if X1 in cls_pos and X2 in act_pos:
-                cls_proba = cls_probability[cls_pos.index(X1)].item()
-                act_proba = act_probability[act_pos.index(X2)].item()
-                generated_seq = generated_seq[generated_seq.index('[MASK]') + 2:generated_seq.index('[SEP]')]
-                if cls_proba>=cls_proba_parent and act_proba>=act_proba_parent and generated_seq.count('C') % 2 == 0 and len("".join(generated_seq)) == gen_len:
-                    generated_seqs.append("".join(generated_seq))
-                    if "".join(generated_seq) not in train_seq and "".join(generated_seq) not in generated_seqs[0:-1] and all(x not in NON_AA for x in generated_seq):
-                        generated_seqs_FINAL.append("".join(generated_seq))
-                        cls_probability_all.append(cls_proba)
-                        act_probability_all.append(act_proba)
-                        IDs.append(count+1)
-                        out = pd.DataFrame({
-                            'ID':IDs,
-                            'Generated_seq': generated_seqs_FINAL,
-                            'Subtype': X1,
-                            'Subtype_probability': cls_probability_all, 
-                            'Potency': X2, 
-                            'Potency_probability': act_probability_all, 
-                            'Random_seed': seed
-                        })
-                        out.to_csv("output.csv", index=False, encoding='utf-8-sig')
-                        count += 1
-                        yield out, "output.csv"
+            _, idx_seq, idx_msa, attn_idx = get_paded_token_idx_gen(vocab_mlm, seq, None)
+            idx_seq = torch.tensor(idx_seq).unsqueeze(0).to(device)
+            idx_msa = torch.tensor(idx_msa).unsqueeze(0).to(device)
+            attn_idx = torch.tensor(attn_idx).to(device)
+
+            mask_positions = [j for j in range(gen_length) if input_text[j] == "[MASK]"]
+            mask_position = torch.tensor([mask_positions[torch.randint(len(mask_positions), (1,))]])
+
+            logits = model(idx_seq, idx_msa, attn_idx)
+            mask_logits = logits[0, mask_position.item(), :]
+
+            predicted_token_id = temperature_sampling(mask_logits, τ)
+            predicted_token = vocab_mlm.to_tokens(int(predicted_token_id))
+            input_text[mask_position.item()] = predicted_token
+            padded_seq[mask_position.item()] = predicted_token.strip()
+
+        generated_seq = input_text
+
+        generated_seq[1] = "[MASK]"
+        input_ids = vocab_mlm.__getitem__(generated_seq)
+        logits = model(torch.tensor([input_ids]).to(device), idx_msa)
+        cls_mask_logits = logits[0, 1, :]
+        cls_probability, cls_mask_probs = torch.topk((torch.softmax(cls_mask_logits, dim=-1)), k=10)
+
+        generated_seq[2] = "[MASK]"
+        input_ids = vocab_mlm.__getitem__(generated_seq)
+        logits = model(torch.tensor([input_ids]).to(device), idx_msa)
+        act_mask_logits = logits[0, 2, :]
+        act_probability, act_mask_probs = torch.topk((torch.softmax(act_mask_logits, dim=-1)), k=2)
+
+        cls_pos = vocab_mlm.to_tokens(list(cls_mask_probs))
+        act_pos = vocab_mlm.to_tokens(list(act_mask_probs))
+
+        if X1 in cls_pos and X2 in act_pos:
+            cls_proba = cls_probability[cls_pos.index(X1)].item()
+            act_proba = act_probability[act_pos.index(X2)].item()
+            generated_seq = generated_seq[generated_seq.index('[MASK]') + 2:generated_seq.index('[SEP]')]
+            if cls_proba >= cls_proba_parent and act_proba >= act_proba_parent and generated_seq.count('C') % 2 == 0 and len("".join(generated_seq)) == gen_len:
+                generated_seq_str = "".join(generated_seq)
+                if generated_seq_str not in train_seq and generated_seq_str not in generated_seqs_FINAL and not any(x in NON_AA for x in generated_seq):
+                    generated_seqs_FINAL.append(generated_seq_str)
+                    cls_probability_all.append(cls_proba)
+                    act_probability_all.append(act_proba)
+                    IDs.append(count + 1)
+                    out = pd.DataFrame({
+                        'ID': IDs,
+                        'Generated_seq': generated_seqs_FINAL,
+                        'Subtype': X1,
+                        'Subtype_probability': cls_probability_all,
+                        'Potency': X2,
+                        'Potency_probability': act_probability_all,
+                        'Random_seed': seed
+                    })
+                    out.to_csv("output.csv", index=False, encoding='utf-8-sig')
+                    count += 1
+                    yield out, "output.csv"
     return out, "output.csv"
 
 with gr.Blocks() as demo:
     gr.Markdown("# Conotoxin Optimization Generation")
     with gr.Row():
         X0 = gr.Textbox(label="conotoxin")
-        X1 = gr.Dropdown(choices=['<α7>','<AChBP>','<α4β2>','<α3β4>','<Ca22>','<α3β2>', '<Na12>','<α9α10>','<K16>', '<α1β1γδ>', 
-                                  '<K13>', '<α1BAR>', '<α1β1ε>', '<α1AAR>', '<GluN3A>', '<GluN2B>', '<α75HT3>', '<Na14>', 
-                                  '<GluN2C>', '<NET>', '<NavBh>', '<α6β3β4>', '<Na11>', '<Ca13>', '<Ca12>', '<Na16>', '<α6α3β2>', 
-                                  '<GluN2A>', '<GluN2D>', '<K17>', '<α1β1δε>', '<GABA>', '<α9>', '<K12>', '<Kshaker>',  '<Na18>', 
-                                  '<α6α3β2β3>', '<α1β1δ>', '<α6α3β4β3>', '<α2β2>','<α6β4>', '<α2β4>','<Na13>', '<Na15>', '<α4β4>', 
+        X1 = gr.Dropdown(choices=['<α7>', '<AChBP>', '<α4β2>', '<α3β4>', '<Ca22>', '<α3β2>', '<Na12>', '<α9α10>', '<K16>', '<α1β1γδ>',
+                                  '<K13>', '<α1BAR>', '<α1β1ε>', '<α1AAR>', '<GluN3A>', '<GluN2B>', '<α75HT3>', '<Na14>',
+                                  '<GluN2C>', '<NET>', '<NavBh>', '<α6β3β4>', '<Na11>', '<Ca13>', '<Ca12>', '<Na16>', '<α6α3β2>',
+                                  '<GluN2A>', '<GluN2D>', '<K17>', '<α1β1δε>', '<GABA>', '<α9>', '<K12>', '<Kshaker>', '<Na18>',
+                                  '<α6α3β2β3>', '<α1β1δ>', '<α6α3β4β3>', '<α2β2>', '<α6β4>', '<α2β4>', '<Na13>', '<Na15>', '<α4β4>',
                                   '<α7α6β2>', '<α1β1γ>', '<NaTTXR>', '<K11>', '<Ca23>', '<α6α3β4>', '<NaTTXS>', '<Na17>'], label="Subtype")
-        X2 = gr.Dropdown(choices=['<high>','<low>'], label="Potency")
+        X2 = gr.Dropdown(choices=['<high>', '<low>'], label="Potency")
         τ = gr.Slider(minimum=1, maximum=2, step=0.1, label="τ")
         g_num = gr.Dropdown(choices=[1, 10, 20, 30, 40, 50], label="Number of generations")
-        model_name = gr.Dropdown(choices=['model_final.pt','model_C1.pt','model_C2.pt','model_C3.pt','model_C4.pt','model_C5.pt','model_mlm.pt'], label="Model")
+        model_name = gr.Dropdown(choices=['model_final.pt', 'model_C1.pt', 'model_C2.pt', 'model_C3.pt', 'model_C4.pt', 'model_C5.pt', 'model_mlm.pt'], label="Model")
     with gr.Row():
         start_button = gr.Button("Start Generation")
         stop_button = gr.Button("Stop Generation")
@@ -191,7 +179,7 @@ with gr.Blocks() as demo:
         output_df = gr.DataFrame(label="Generated Conotoxins")
     with gr.Row():
         output_file = gr.File(label="Download generated conotoxins")
-    
+
     start_button.click(CTXGen, inputs=[X0, X1, X2, τ, g_num, model_name], outputs=[output_df, output_file])
     stop_button.click(stop_generation, outputs=None)