Delete app.py

app.py

@@ -1,141 +0,0 @@
-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 = "cpu"
-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=512)#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(512, 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'],
-                                     attention_mask=x['attention_mask'])
-        # 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)
-        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"))
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-
-    # Welcome to Antimicrobial Peptide Recognition Model
-    This is an antimicrobial peptide recognition model derived from Diff-AMP, which is a branch of a comprehensive system integrating generation, recognition, and optimization. In this recognition model, you can simply input a sequence, and it will predict whether it is an antimicrobial peptide. Due to limited website capacity, we can only perform simple predictions. 
-    If you require large-scale computations, please contact my email at [email protected]. Feel free to reach out if you have any questions or inquiries.
-
-        """)
-
-    # 定义美化样式
-    custom_css = """
-       body {
-           font-family: Arial, sans-serif;
-           background-color: #f0f0f0;
-           color: #333;
-       }
-
-       .gr-input-container {
-           border: 2px solid #007BFF;
-           border-radius: 5px;
-           padding: 10px;
-       }
-
-       .gr-button {
-           background-color: #007BFF;
-           color: white;
-           border: none;
-           border-radius: 5px;
-       }
-
-       .gr-button:hover {
-           background-color: #0056b3;
-       }
-       """
-
-    # 添加示例输入和输出
-    examples = [
-        ["QGLFFLGAKLFYLLTLFL"],
-        ["FLGLLFHGVHHVGKWIHGLIHGHH"],
-        ["GLMSTLKGAATNAAVTLLNKLQCKLTGTC"]
-    ]
-
-    # 创建 Gradio 接口并应用美化样式和示例
-    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__":
-    demo.launch()
-
-
-
-