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app.py

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+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import set_seed
+import torch
+import torch.nn as nn
+import warnings
+from tqdm import tqdm
+import gradio as gr
+
+warnings.filterwarnings('ignore')
+device = "cuda:0"
+model_checkpoint1 = "facebook/esm2_t12_35M_UR50D"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint1)
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.bert1 = AutoModelForSequenceClassification.from_pretrained(model_checkpoint1, num_labels=3000).cuda()#3000
+        # for param in self.bert1.parameters():
+        #     param.requires_grad = False
+        self.bn1 = nn.BatchNorm1d(256)
+        self.bn2 = nn.BatchNorm1d(128)
+        self.bn3 = nn.BatchNorm1d(64)
+        self.relu = nn.LeakyReLU()
+        self.fc1 = nn.Linear(3000, 256)
+        self.fc2 = nn.Linear(256, 128)
+        self.fc3 = nn.Linear(128, 64)
+        self.output_layer = nn.Linear(64, 2)
+        self.dropout = nn.Dropout(0.3)  # 0.3
+
+    def forward(self, x):
+        with torch.no_grad():
+            bert_output = self.bert1(input_ids=x['input_ids'].to(device),
+                                     attention_mask=x['attention_mask'].to(device))
+        # output_feature = bert_output["logits"]
+        # print(output_feature.size())
+        # output_feature = self.bn1(self.fc1(output_feature))
+        # output_feature = self.bn2(self.fc1(output_feature))
+        # output_feature = self.relu(self.bn3(self.fc3(output_feature)))
+        # output_feature = self.dropout(self.output_layer(output_feature))
+        output_feature = self.dropout(bert_output["logits"])
+        output_feature = self.dropout(self.relu(self.bn1(self.fc1(output_feature))))
+        output_feature = self.dropout(self.relu(self.bn2(self.fc2(output_feature))))
+        output_feature = self.dropout(self.relu(self.bn3(self.fc3(output_feature))))
+        output_feature = self.dropout(self.output_layer(output_feature))
+        # return torch.sigmoid(output_feature),output_feature
+        return torch.softmax(output_feature, dim=1)
+
+
+def AMP(test_sequences, model):
+    # 保持 AMP 函数不变，只处理传入的 test_sequences 数据
+    max_len = 18
+    test_data = tokenizer(test_sequences, max_length=max_len, padding="max_length", truncation=True,
+                          return_tensors='pt')
+    model = model.to(device)
+    model.eval()
+    out_probability = []
+    with torch.no_grad():
+        predict = model(test_data).cuda()
+        out_probability.extend(np.max(np.array(predict.cpu()), axis=1).tolist())
+        test_argmax = np.argmax(predict.cpu(), axis=1).tolist()
+    id2str = {0: "non-AMP", 1: "AMP"}
+    return id2str[test_argmax[0]], out_probability[0]
+
+
+def classify_sequence(sequence):
+    # Check if the sequence is a valid amino acid sequence and has a length of at least 3
+    valid_amino_acids = set("ACDEFGHIKLMNPQRSTVWY")
+    sequence = sequence.upper()
+
+    if all(aa in valid_amino_acids for aa in sequence) and len(sequence) >= 3:
+        result, probability = AMP(sequence, model)
+        return "yes" if result == "AMP" else "no"
+    else:
+        return "Invalid Sequence"
+
+# 加载模型
+model = MyModel()
+model.load_state_dict(torch.load("best_model.pth"))
+
+iface = gr.Interface(
+    fn=classify_sequence,
+    inputs=gr.inputs.Textbox(label="Enter Sequence"),
+    outputs=gr.outputs.Textbox(label="AMP Classification (yes/no)"),
+    live=True,
+    title="AMP Sequence Detector",
+    description="Enter a sequence to detect if it is an AMP (Antimicrobial Peptide) or not (yes/no)."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+
+
+

best_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dfea2eeaab91540cc8e05009c85b886bd0904c0e8a5621fb7bc4c3d310ca8578
+size 145091125