app.py

import random
import torch
import gradio as gr
from gradio_rangeslider import RangeSlider
import pandas as pd
from utils import create_vocab, setup_seed
from dataset_mlm import get_paded_token_idx_gen, add_tokens_to_vocab
import time

is_stopped = False

def temperature_sampling(logits, temperature):
    logits = logits / temperature
    probabilities = torch.softmax(logits, dim=-1)
    sampled_token = torch.multinomial(probabilities, 1)
    return sampled_token

def stop_generation():
    global is_stopped
    is_stopped = True
    return "Generation stopped."

def CTXGen(τ, g_num, length_range, model_name, seed):
    if seed =='random':
        seed = random.randint(0,100000)
        setup_seed(seed)
    else:
        setup_seed(int(seed))
        
    global is_stopped
    is_stopped = False

    device = torch.device("cpu")
    vocab_mlm = create_vocab()
    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()
    model = torch.load(save_path, map_location=torch.device('cpu'))
    model = model.to(device)

    start, end = length_range
    X1 = "X"
    X2 = "X"
    X4 = ""
    X5 = ""
    X6 = ""
    model.eval()
    with torch.no_grad():
        IDs = []
        generated_seqs = []
        generated_seqs_FINAL = []
        cls_pos_all = []
        cls_probability_all = []
        act_pos_all = []
        act_probability_all = []

        count = 0
        gen_num = int(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]']

        start_time = time.time()
        while count < gen_num:
            new_seq = None
            if is_stopped:
                return "output.csv", pd.DataFrame()

            if time.time() - start_time > 1200:
                break

            gen_len = random.randint(int(start), int(end))
            X3 = "X" * gen_len
            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 "output.csv", pd.DataFrame()

                _, 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]"
            generated_seq[2] = "[MASK]"
            input_ids = vocab_mlm.__getitem__(generated_seq)
            logits = model(torch.tensor([input_ids]).to(device), idx_msa)

            cls_mask_logits = logits[0, 1, :]
            act_mask_logits = logits[0, 2, :]

            cls_probability, cls_mask_probs = torch.topk((torch.softmax(cls_mask_logits, dim=-1)), k=1)
            act_probability, act_mask_probs = torch.topk((torch.softmax(act_mask_logits, dim=-1)), k=1)

            cls_pos = vocab_mlm.idx_to_token[cls_mask_probs[0].item()]
            act_pos = vocab_mlm.idx_to_token[act_mask_probs[0].item()]

            cls_probability = cls_probability[0].item()
            act_probability = act_probability[0].item()
            generated_seq = generated_seq[generated_seq.index('[MASK]') + 2:generated_seq.index('[SEP]')]

            if 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_pos_all.append(cls_pos)
                    cls_probability_all.append(cls_probability)
                    act_pos_all.append(act_pos)
                    act_probability_all.append(act_probability)
                    IDs.append(count+1)
                    out = pd.DataFrame({
                        'ID':IDs,
                        'Generated_seq': generated_seqs_FINAL,
                        'Subtype': cls_pos_all,
                        'Subtype_probability': cls_probability_all,
                        'Potency': act_pos_all,
                        'Potency_probability': act_probability_all,
                        'random_seed': seed
                    })
                    out.to_csv("output.csv", index=False, encoding='utf-8-sig')
                    count += 1
                    yield "output.csv", out
    return "output.csv", out

with gr.Blocks() as demo:
    gr.Markdown("🔗 **[Label Prediction](https://huggingface.co/spaces/oucgc1996/CreoPep_Label_Prediction)**"
                "🔗 **[Unconstrained Generation](https://huggingface.co/spaces/oucgc1996/CreoPep_Unconstrained_generation)**"
                "🔗 **[Conditional Generation](https://huggingface.co/spaces/oucgc1996/CreoPep_conditional_generation)**"
                "🔗 **[Optimization Generation](https://huggingface.co/spaces/oucgc1996/CreoPep_optimization_generation)**")
    gr.Markdown("# Conotoxin Unconstrained Generation")
    gr.Markdown("#### Input")
    gr.Markdown("✅**τ**: temperature factor controls the diversity of conotoxins generated. The higher the value, the higher the diversity.")
    gr.Markdown("✅**Number of generations**: if it is not completed within 1200 seconds, it will automatically stop.")
    gr.Markdown("✅**Length range**: expected length range of conotoxins generated.")
    gr.Markdown("✅**Model**: model parameters trained at different stages of data augmentation. Please refer to the paper for details.")
    gr.Markdown("✅**Seed**: enter an integer as the random seed to ensure reproducible results. The default is random.")
    with gr.Row():
        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")
        length_range = RangeSlider(minimum=8, maximum=50, step=1, value=(12, 16), label="Length range")
        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")
        seed = gr.Textbox(label="Seed", value="random")
    with gr.Row():
        start_button = gr.Button("Start Generation")
        stop_button = gr.Button("Stop Generation")
    with gr.Row():
        output_file = gr.File(label="Download generated conotoxins")
    with gr.Row():
        output_df = gr.DataFrame(label="Generated Conotoxins")

    start_button.click(CTXGen, inputs=[τ, g_num, length_range, model_name, seed], outputs=[output_file, output_df])
    stop_button.click(stop_generation, outputs=None)

demo.launch()