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README.md

@@ -1,8 +1,8 @@
 ---
-title: ConotoxinFinder Regression
-emoji: 😻
-colorFrom: green
-colorTo: indigo
+title: ConotoxinFinder nAChRs
+emoji: 💻
+colorFrom: purple
+colorTo: red
 sdk: gradio
 sdk_version: 4.39.0
 app_file: app.py

app.py

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+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer,AutoModelForSequenceClassification,AutoConfig
+from torchcrf import CRF
+import numpy as np
+import pandas as pd
+import re
+from Bio.Seq import Seq
+import matplotlib.pyplot as plt 
+from collections import OrderedDict	
+from transformers import set_seed
+import random
+import gradio as gr
+
+def setup_seed(seed):
+    set_seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+setup_seed(4)
+
+device = "cpu"
+model_checkpoint = "facebook/esm2_t6_8M_UR50D"
+
+config = AutoConfig.from_pretrained(model_checkpoint)
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+
+
+def conotoxinfinder(files):
+    fr=open(files, 'r')
+    seqs = []
+    for line in fr:
+        if not line.startswith('>'):
+            line = line.replace('\n','')
+            line = line.replace(' ','')
+            if line.islower():
+                seqs.append(str((Seq(line).translate())))
+            else:
+                seqs.append(line)
+
+    model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=1)
+    model.load_state_dict(torch.load("best_model.pth"))
+    model = model.to(device)
+
+    value_all = []
+    for i in seqs:
+        tokenizer_test = tokenizer(i, return_tensors='pt').to(device)
+        with torch.no_grad():
+            value = model(**tokenizer_test)
+            value_all.append(np.exp(value["logits"][0].item()))
+
+    summary = OrderedDict()
+    summary['Seq'] = seqs
+    summary['Value'] = value_all
+    summary_df = pd.DataFrame(summary)
+    summary_df.to_csv('output.csv', index=False)
+    return 'output.csv'
+
+with open("conotoxinfinder.md", "r") as f:
+    description = f.read()
+iface = gr.Interface(fn=conotoxinfinder,
+                     title="ConotoxinFinder α7 regression",
+                     inputs=["file"
+                             ],
+                     outputs= "file",
+                     description=description
+                     )
+iface.launch()

best_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ede487d153ed22a196a8f45fe31bedb334bba2bc8579ee5554a655422a8b4983
+size 31413855

requirements.txt

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+numpy
+torch
+pandas