app.py

import torch
from NeuroPredPLM.predict import predict, batch_predict
import gradio as gr
from io import StringIO
from Bio import SeqIO

def classifier(peptide_seq):
    handle = StringIO(peptide_seq)
    data = []
    for record in SeqIO.parse(handle, 'fasta'):
        data.append((record.id, str(record.seq)))
    device = "cuda" if torch.cuda.is_available() else "cpu"
    neuropeptide_pred = predict(data, './model.pth', device)
    return neuropeptide_pred
# {peptide_id:[Type:int(1->neuropeptide,0->non-neuropeptide), attention score:nd.array]}


def batch_classifier(file, cutoff):
    data = []
    for record in SeqIO.parse(file.name, 'fasta'):
        data.append((record.id, str(record.seq)))
    device = "cuda" if torch.cuda.is_available() else "cpu"
    neuropeptide_pred = batch_predict(data, cutoff, './model.pth', device)
    return neuropeptide_pred

with gr.Blocks() as demo:
    gr.Markdown(" ## NeuroPred-PLM")
    gr.Markdown("In this work, we developed an interpretable and robust neuropeptide prediction model, named NeuroPred-PLM. First, we employed a language model (ESM) of proteins to obtain semantic representations of neuropeptides, which could reduce the complexity of feature engineering. Next, we adopted a multi-scale convolutional neural network to enhance the local feature representation of neuropeptide embeddings. To make the model interpretable, we proposed a global multi-head attention network that could be used to capture the position-wise contribution to neuropeptide prediction via the attention scores. In addition, NeuroPred-PLM was developed based on our newly constructed NeuroPep 2.0 database. Benchmarks based on the independent test set show that NeuroPred-PLM achieves superior predictive performance compared to other state-of-the-art predictors.")
    with gr.Tab("Single Sequence Medel"):
        # cutoff = gr.Slider(0, 1, step=0.1, value=0.5, interactive=True)
        with gr.Row():
            with gr.Column(scale=2):
                text_input = gr.Textbox(
                    label="Input single peptide sequence in the Fasta format",
                    lines=4,
                    value=">peptide-1\nIGLRLPNMLKF",
                )
                gr.Markdown("#### The input peptide sequence length should be between 5-100")
                single_cutoff = gr.Slider(0, 1, step=0.1, value=0.5, interactive=True, label="Threshold")
                text_button = gr.Button("Submit")
            with gr.Column(scale=2):
                gr.Markdown("Note: the output scores indicates the probability scores of the input sequence to be predicted as a neuropeptide or a non-neuropeptide.")
                text_output = gr.outputs.Label(num_top_classes=2, label='Output')
    with gr.Tab("Batch Model"):
        with gr.Row():
            with gr.Column(scale=2):
                input_file_fasta = gr.File()
                # cutoff = gr.Slider(0, 1, step=0.1, value=0.5, interactive=True, label="threshold")
                # image_button = gr.Button("Submit")
            with gr.Column(scale=2):
                batch_cutoff = gr.Slider(0, 1, step=0.1, value=0.5, interactive=True, label="Threshold")
                gr.Markdown("### Note")
                gr.Markdown("- Limit the number of input sequences to less than 30")
                gr.Markdown("- The file should be the Fasta format")
                gr.Markdown("- The input peptide sequence length should be between 5-100")
                image_button = gr.Button("Submit")
        with gr.Column():
            # gr.Markdown(" ### Flip text or image files using this demo.")
            gr.Markdown("Note: the output scores indicates the probability scores of the input sequence to be predicted as a neuropeptide or a non-neuropeptide.")
            frame_output = gr.DataFrame(headers=["Sequence Id", "Sequence", "Probability of neuropeptides", "Neuropeptide"],
            datatype=["str", "str", "str", 'str'],)

        

    with gr.Accordion("Citation"):
        gr.Markdown("- Wang, L., Huang, C., Wang, M., Xue, Z., & Wang, Y. (2022). NeuroPred-PLM: an interpretable and robust model for neuropeptide prediction by protein language model. In preparation.")
        gr.Markdown("- GitHub: https://github.com/ISYSLAB-HUST/NeuroPred-PLM")

    with gr.Accordion("License"):
        gr.Markdown("- Released under the [MIT license](https://github.com/ISYSLAB-HUST/NeuroPred-PLM/blob/main/LICENSE). ")

    with gr.Accordion("Contact"):
        gr.Markdown("- If you have any questions, comments, or would like to report a bug, please file a Github issue or contact me at wanglei94@hust.edu.cn.")

    text_button.click(classifier, inputs=text_input, outputs=text_output)
    image_button.click(batch_classifier, inputs=[input_file_fasta, batch_cutoff], outputs=frame_output)

demo.queue(4)
demo.launch()