Upload app.py

app.py

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+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import pandas as pd
+from transformers import set_seed
+import torch
+import torch.nn as nn
+from collections import OrderedDict
+import warnings
+import gradio as gr
+
+warnings.filterwarnings('ignore')
+set_seed(4)  
+device = "cuda:0"
+model_checkpoint = "facebook/esm2_t30_150M_UR50D"
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.bert = AutoModelForSequenceClassification.from_pretrained(model_checkpoint,num_labels=320)
+        self.bn1 = nn.BatchNorm1d(256)
+        self.bn2 = nn.BatchNorm1d(128)
+        self.bn3 = nn.BatchNorm1d(64)
+        self.relu = nn.ReLU()
+        self.fc1 = nn.Linear(320,256)
+        self.fc2 = nn.Linear(256,128)
+        self.fc3 = nn.Linear(128,64)
+        self.output_layer = nn.Linear(64,2)
+        self.dropout = nn.Dropout(0)
+
+    def forward(self,x):
+        with torch.no_grad():
+            bert_output = self.bert(input_ids=x['input_ids'].to(device),attention_mask=x['attention_mask'].to(device)) 
+        output_feature = self.dropout(bert_output["logits"])
+        output_feature = self.relu(self.bn1(self.fc1(output_feature)))
+        output_feature = self.relu(self.bn2(self.fc2(output_feature)))
+        output_feature = self.relu(self.bn3(self.fc3(output_feature)))
+        output_feature = self.output_layer(output_feature)
+        return torch.softmax(output_feature,dim=1)
+
+def Kmers_funct(seq,num):
+    for i in range(len(seq)):
+        a = seq[i]
+        l = []
+        for index in range(len(a)):
+            t = a[index:index + num]
+            if (len(t)) == num:
+                l.append(t)
+    return l
+
+def ACE(file):
+    model = MyModel()
+    model.load_state_dict(torch.load("best_model.pth"))
+    model = model.to(device)
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+    test_sequences = file
+    max_len = 30
+    test_data = tokenizer(test_sequences, max_length=max_len, padding="max_length",truncation=True, return_tensors='pt')
+    out_probability = []
+    with torch.no_grad():
+        predict = model(test_data)
+        out_probability.extend(np.max(np.array(predict.cpu()),axis=1).tolist())
+        test_argmax = np.argmax(predict.cpu(), axis=1).tolist()
+    id2str = {0:"non-ACE", 1:"ACE"}
+    return id2str[test_argmax[0]], out_probability[0]
+
+def main(file):
+    test_seq = file
+    all = []
+    seq_all = []
+    output_all = []
+    probability_all = []
+    for j in range(2, 11):
+        X = Kmers_funct([test_seq], j)
+        all.extend(X)
+    for seq in all:
+        output, probability = ACE(str(seq))
+        seq_all.append(seq)
+        output_all.append(output)
+        probability_all.append(probability)
+
+    summary = OrderedDict()
+    summary['Seq'] = seq_all
+    summary['Class'] = output_all
+    summary['Probability'] = probability_all
+    summary_df = pd.DataFrame(summary)
+    summary_df.to_csv('output.csv', index=False)
+    return 'outputs.csv'
+
+
+iface = gr.Interface(fn=main,
+                     inputs="text", 
+                     outputs= "file")
+iface.launch()