Add application file

app.py

@@ -0,0 +1,98 @@
+from pyuul import VolumeMaker 
+from pyuul import utils 
+import os
+from sklearn.cluster import KMeans
+from collections import OrderedDict
+import numpy as np
+import pandas as pd
+import random
+import torch
+import os
+import shutil
+import gradio as gr
+
+# 设置随机数种子
+def setup_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+setup_seed(100)
+device = "cuda"
+
+def add_file_to_folder(new_file_path, folder):
+    ls = os.listdir(new_file_path)
+    for line in ls:
+        filePath = os.path.join(new_file_path, line)
+        if os.path.isfile(filePath):
+            shutil.copy(filePath, folder)
+
+def copy(files,folder):
+    file = os.listdir(folder)
+    for i in file:
+        if i not in files:
+            file_path = os.path.join(folder, i)
+            os.remove(file_path)
+
+def pyuul(folder,n_clusters):
+    coords, atname, pdbname, pdb_num = utils.parsePDB(folder) 
+    atoms_channel = utils.atomlistToChannels(atname) 
+    radius = utils.atomlistToRadius(atname) 
+
+    PointCloudSurfaceObject = VolumeMaker.PointCloudVolume(device=device)
+
+    coords = coords.to(device)
+    radius = radius.to(device) 
+    atoms_channel = atoms_channel.to(device) 
+
+    SurfacePoitCloud = PointCloudSurfaceObject(coords, radius) 
+    feature = SurfacePoitCloud.view(pdb_num,-1).cpu()
+    
+    kmean = KMeans(n_clusters=n_clusters,n_init=10,init="k-means++",random_state=100)
+    y = kmean.fit_predict(feature)
+
+    pairs = zip(pdbname, y)
+    result_dict = {key: value for key, value in pairs}
+    ligend_class = result_dict['ligend.pdb']
+
+    sheet = []
+    for key, value in result_dict.items():
+        if value == ligend_class:
+            sheet.append(key)
+    return sheet
+
+def kmeans(ligend,n_clusters,n_num):
+    peptide_folder_path = "/peptide/" 
+    pdb_folder = "/temp/"
+    shutil.copyfile(ligend,pdb_folder)
+    for i in range(1,n_num+1):
+        if i == 1:
+            add_file_to_folder(peptide_folder_path,pdb_folder)
+            output = pyuul(pdb_folder, n_clusters)
+            copy(output,pdb_folder)
+        else:      
+            if pdb_folder != None:
+                output = pyuul(pdb_folder, n_clusters)
+                copy(output,pdb_folder)
+
+    data = OrderedDict()
+    data['Name'] = output
+    data = pd.DataFrame(data)
+    data.to_csv('outputs.csv', index=False)
+    shutil.rmtree(pdb_folder)
+    os.mkdir(pdb_folder) 
+    return 'outputs.csv'
+    
+iface = gr.Interface(fn=kmeans,
+                     inputs=["text",
+                             gr.Textbox(label="n_clusters", placeholder="2", lines=1),
+                             gr.Textbox(label="Times", placeholder="2", lines=1)
+                             ],
+                     outputs= "text"
+                     )
+iface.launch() 
+
+
+
+