Delete app.py

app.py

@@ -1,59 +0,0 @@
-import numpy as np
-import gradio as gr
-from transformers import AutoTokenizer,AutoModelForSequenceClassification
-from transformers import set_seed
-from torch.utils.data import Dataset,DataLoader
-import torch
-import torch.nn as nn
-import numpy as np
-import warnings
-warnings.filterwarnings('ignore')
-set_seed(4)  
-device = "cpu"
-model_checkpoint = "facebook/esm2_t6_8M_UR50D"
-tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-
-def AMP(file):
-    test_sequences = file
-    max_len = 30
-    test_data = tokenizer(test_sequences, max_length=max_len, padding="max_length",truncation=True, return_tensors='pt')
-
-    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)
-            
-    model = MyModel()
-    model.load_state_dict(torch.load("Best_model.pth",map_location=torch.device('cpu')))
-    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]
-
-iface = gr.Interface(fn=AMP,
-                     inputs="text", 
-                    outputs= ["text", "text"])
-iface.launch()