Upload app.py

app.py

@@ -1,6 +1,9 @@
 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
@@ -14,14 +17,11 @@ 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.to("cpu")
 bert_model=bert_model.eval()
 
 
-
-def greet(name):
-
-
+def func(name):
 
     translation_table = str.maketrans("", "", " \t\n\r\f\v")
     name = name.translate(translation_table)
@@ -36,14 +36,85 @@ def greet(name):
     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 = gr.Interface(
-    fn=greet,
-    # 自定义输入框
-    # 具体设置方法查看官方文档
-    inputs=gr.Textbox(lines=3, placeholder="",label="Paste a protein sequence in plain text (not in FASTA format)"),
-    outputs=gr.Textbox(lines=3, placeholder="",label="Cluster prediction"),
-)
 demo.launch(share=True)