| import torch |
| from DeepPD.predictor import predict |
| from DeepPD.data_helper import Data2EqlTensor |
| import gradio as gr |
| from Bio import SeqIO |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
|
|
| def mus_classifier(file,threshold): |
| data = [] |
| for record in SeqIO.parse(file.name, 'fasta'): |
| data.append((record.id, str(record.seq))) |
| seqs,_ = Data2EqlTensor(data,40) |
| mus_peptide_pred = predict(seqs,data, './weight-Mus/4.pth', threshold, device) |
| return mus_peptide_pred |
|
|
| def homo_classifier(file,threshold): |
| data = [] |
| for record in SeqIO.parse(file.name, 'fasta'): |
| data.append((record.id, str(record.seq))) |
| seqs,_ = Data2EqlTensor(data,40) |
| homo_peptide_pred = predict(seqs, data, './weight-Homo/4.pth', threshold, device) |
| return homo_peptide_pred |
| |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown(" ## DeepPD") |
| gr.Markdown("In this study, we developed a peptide detectability prediction model. The model was used to predict the probability that an amino acid sequence is a peptide.") |
| |
| with gr.Tab("Prediction Model(Homo sapiens)"): |
| with gr.Row(): |
| with gr.Column(scale=2): |
| input_fasta_homo = gr.File() |
| with gr.Column(scale=2): |
| homo_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 128.") |
| gr.Markdown("- The file should be the Fasta format.") |
| gr.Markdown("- We used only the first 20 amino acids of each N-terminal and C-terminal of the sequence for prediction.") |
| image_button_homo = gr.Button("Submit") |
| with gr.Column(): |
| |
| gr.Markdown("Note: the output scores indicates the probability of the input sequence to be predicted as a Peptide or a Non-Peptide.") |
| frame_homo_output = gr.DataFrame( |
| headers=["Sequence Id", "Sequence", "Probability of peptides", "Peptide"], |
| datatype=["str", "str", "str", 'str'],) |
| |
| image_button_homo.click(homo_classifier, inputs=[input_fasta_homo, homo_cutoff], outputs=frame_homo_output) |
| |
| with gr.Tab("Prediction Model(Mus musculus)"): |
| |
| with gr.Row(): |
| with gr.Column(scale=2): |
| input_fasta_mus = gr.File() |
| |
| |
| with gr.Column(scale=2): |
| mus_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 128.") |
| gr.Markdown("- The file should be the Fasta format.") |
| gr.Markdown("- We used only the first 20 amino acids of each N-terminal and C-terminal of the sequence for prediction.") |
| image_button_mus = gr.Button("Submit") |
| with gr.Column(): |
| |
| gr.Markdown("Note: the output scores indicates the probability of the input sequence to be predicted as a Peptide or a Non-Peptide.") |
| frame_mus_output = gr.DataFrame( |
| headers=["Sequence Id", "Sequence", "Probability of peptides", "Peptide"], |
| datatype=["str", "str", "str", 'str'],) |
| |
| image_button_mus.click(mus_classifier, inputs=[input_fasta_mus, mus_cutoff], outputs=frame_mus_output) |
|
|
| with gr.Accordion("Citation"): |
| gr.Markdown("- GitHub: https://github.com/leonern/DeepPD") |
|
|
| with gr.Accordion("License"): |
| gr.Markdown("- Released under the [MIT license](https://github.com/leonern/DeepPD/blob/main/LICENSE). ") |
|
|
| with gr.Accordion("Contact"): |
| gr.Markdown("- If you have any questions, please file a Github issue or contact me at 107552103310@stu.xju.edu.cn") |
|
|
|
|
| demo.queue(4) |
| demo.launch() |
