Delete screen.py

screen.py

@@ -1,120 +0,0 @@
-import os
-import pandas as pd
-
-import torch
-from torch.nn import functional as F
-from transformers import AutoTokenizer
-
-from util.utils import *
-
-from tqdm import tqdm
-from train import markerModel
-
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-os.environ["CUDA_VISIBLE_DEVICES"] = '0 '
-
-device_count = torch.cuda.device_count()
-device_biomarker = torch.device('cuda' if torch.cuda.is_available() else "cpu")
-
-device = torch.device('cpu')
-a_model_name = 'DeepChem/ChemBERTa-10M-MLM'
-d_model_name = 'DeepChem/ChemBERTa-10M-MTR'
-
-tokenizer = AutoTokenizer.from_pretrained(a_model_name)
-d_tokenizer = AutoTokenizer.from_pretrained(d_model_name)
-
-#--biomarker Model
-##-- hyper param config file Load --##
-config = load_hparams('config/predict.json')
-config = DictX(config)
-model = markerModel(config.d_model_name, config.p_model_name,
-                              config.lr, config.dropout, config.layer_features, config.loss_fn, config.layer_limit, config.pretrained['chem'], config.pretrained['prot'])
- 
-model = markerModel.load_from_checkpoint(config.load_checkpoint,strict=False)
-model.eval()
-model.freeze()
-    
-if device_biomarker.type == 'cuda':
-    model = torch.nn.DataParallel(model)
-    
-def get_marker(drug_inputs, prot_inputs):
-    output_preds = model(drug_inputs, prot_inputs)
-   
-    predict = torch.squeeze( (output_preds)).tolist()
-
-    # output_preds = torch.relu(output_preds)
-    # predict = torch.tanh(output_preds)
-    # predict = predict.squeeze(dim=1).tolist()
-
-    return predict
-
-
-def marker_prediction(smiles, aas):
-    try:
-        aas_input = []
-        for ass_data in aas:
-            aas_input.append(' '.join(list(ass_data)))
-    
-        a_inputs = tokenizer(smiles, padding='max_length', max_length=510, truncation=True, return_tensors="pt")
-        # d_inputs = tokenizer(smiles, truncation=True, return_tensors="pt")
-        a_input_ids = a_inputs['input_ids'].to(device)
-        a_attention_mask = a_inputs['attention_mask'].to(device)
-        a_inputs = {'input_ids': a_input_ids, 'attention_mask': a_attention_mask}
-
-        d_inputs = d_tokenizer(aas_input, padding='max_length', max_length=510, truncation=True, return_tensors="pt")
-        # p_inputs = prot_tokenizer(aas_input, truncation=True, return_tensors="pt")
-        d_input_ids = d_inputs['input_ids'].to(device)
-        d_attention_mask = d_inputs['attention_mask'].to(device)
-        d_inputs = {'input_ids': d_input_ids, 'attention_mask': d_attention_mask}
- 
-        output_predict = get_marker(a_inputs, d_inputs)
-        
-        output_list = [{'acceptor': smiles[i], 'donor': aas[i], 'predict': output_predict[i]} for i in range(0,len(aas))]
-
-        return output_list
-
-    except Exception as e:
-        print(e)
-        return {'Error_message': e}
-
-
-def smiles_aas_test(file):
-     
-    batch_size = 80
-    try:
-        datas = []
-        marker_list = []
-        marker_datas = []
-
-        smiles_aas = pd.read_csv(file)
-        
-        ## -- 1 to 1 pair predict check -- ##
-        for data in smiles_aas.values:
-            mola =  Chem.MolFromSmiles(data[2]) 
-            data[2] = Chem.MolToSmiles(mola,   canonical=True)
-            mola =  Chem.MolFromSmiles(data[1]) 
-            data[1] = Chem.MolToSmiles(mola,   canonical=True)                        
-            marker_datas.append([data[2], data[1]])
-            if len(marker_datas) == batch_size:
-                marker_list.append(list(marker_datas))
-                marker_datas.clear()
-
-        if len(marker_datas) != 0:
-            marker_list.append(list(marker_datas))
-            marker_datas.clear()
-            
-        for marker_datas in tqdm(marker_list, total=len(marker_list)):
-            smiles_d , smiles_a  = zip(*marker_datas)
-            output_pred = marker_prediction(list(smiles_d), list(smiles_a) )
-            if len(datas) == 0:
-                datas = output_pred
-            else:
-                datas = datas + output_pred
-        datas = pd.DataFrame(datas)
- 
-        return datas
-        
-    except Exception as e:
-        print(e)
-        return {'Error_message': e}
-