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import gradio as gr
import pandas as pd
import io
from Bio import SeqIO
from tape import ProteinBertModel, ProteinBertConfig, TAPETokenizer # type: ignore
from tape.models import modeling_bert
import numpy as np
import torch
tokenizer = TAPETokenizer(vocab='iupac')
config=modeling_bert.ProteinBertConfig(num_hidden_layers=5,num_attention_heads=8,hidden_size=400)
bert_model = torch.load('models/transformer1500_95p_500.pt')
class_model=torch.load('models/down_model_500_kfold1.pt')
bert_model=bert_model.module
bert_model=bert_model.to("cpu")
bert_model=bert_model.eval()
def func(name):
translation_table = str.maketrans("", "", " \t\n\r\f\v")
name = name.translate(translation_table)
token_ids = torch.tensor([tokenizer.encode(name)])
token_ids = token_ids
bert_output = bert_model(token_ids)
class_output=class_model(bert_output[1])
class_output = torch.softmax(class_output, dim=1)
cluster = torch.argmax(class_output, dim=1) + 1
cluster=cluster.item()
return "cluster "+str(cluster)
def func_mult(name):
sequence_list = name.split("\n")
sequence_list = [s.strip() for s in sequence_list]
sequence_list = [x for x in sequence_list if x] # 列表推导式
output=[]
for i in range(0, len(sequence_list), 1):
output.append(func(sequence_list[i]))
result = "\n".join(output)
return result
def read_fasta_file(file_path):
sequences = []
for seq_record in SeqIO.parse(file_path, "fasta"):
sequences.append(str(seq_record.seq))
return sequences
def func_file(file_path):
sequence_list = read_fasta_file(file_path)
output=[]
for i in range(0, len(sequence_list), 1):
output.append(func(sequence_list[i]))
result = "\n".join(output)
return result
def upload_file(files):
file_paths = [file.name for file in files]
return file_paths[0]
def save_to_txt(data):
# 写入数据到 TXT 文件
file_name="output.txt"
with open(file_name, mode='w') as file:
file.write(data)
# 返回文件路径
return file_name
css = """
.gradio-container {background-color: #EDEFF7}
.button {background-color: #515D90; color:#FFFFFF}
.feedback {font-size: 36px}
"""
with gr.Blocks(css=css, title="GH29 Prediction", theme=gr.themes.Soft()) as demo:
gr.Markdown("GH29 Prediction", elem_classes="feedback")
# 创建一个包含Markdown说明的示例块
with gr.Tab("Single sequence input"):
with gr.Row():
single_input = gr.Textbox(lines=10, placeholder="Please input sequence data (note: do not input fasta data)", label="Input")
single_output = gr.Textbox(lines=10, label="Output", show_copy_button=True)
single_button = gr.Button("Predict", elem_classes="button")
with gr.Tab("Multiple sequence input"):
multiple_input = gr.Textbox(lines=10, placeholder="Please enter multiple sequence data separated by line breaks (do not enter fasta data)", label="Input")
multiple_button = gr.Button("Predict", elem_classes="button")
multiple_output = gr.Textbox(lines=10, label="Output", show_copy_button=True)
with gr.Tab("FASTA input"):
with gr.Row():
file_upload = gr.File(label="Fasta File", interactive=False, scale=2)
file_output_textbox = gr.Textbox(lines=15, label="Output", scale=3, container=True, autoscroll=True, show_copy_button=True)
file_output_file = gr.File(label="Output File", scale=2)
with gr.Row():
upload_button = gr.UploadButton("Click to Upload a File", file_types=["fasta"], scale=2, size="sm", file_count="multiple")
upload_button.upload(upload_file, upload_button, file_upload)
file_button = gr.Button("Predict", scale=3, size="lg", elem_classes="button")
file_button_GenerateFile = gr.Button("Save to file", scale=2, size="sm")
single_button.click(func, inputs=single_input, outputs=single_output)
multiple_button.click(func_mult, inputs=multiple_input, outputs=multiple_output)
file_button.click(func_file, inputs=file_upload, outputs=file_output_textbox)
file_button_GenerateFile.click(save_to_txt, inputs=file_output_textbox, outputs=file_output_file)
demo.launch(share=True) |