first time

app.py

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+import gradio as gr
+from transformers import AutoModel, AutoTokenizer, AutoModelForTokenClassification
+import torch
+import numpy as np
+import torch.nn.functional as F
+
+tokenizer = AutoTokenizer.from_pretrained("./checkpoint-final/")
+model = AutoModelForTokenClassification.from_pretrained("./checkpoint-final/")
+model = model.eval()
+
+def get_out(sent):
+    encoded = tokenizer.encode_plus(sent, return_tensors="pt")
+    with torch.no_grad():
+        output = model(**encoded)
+    output = F.softmax(torch.squeeze(output['logits']))[1:-1,1].detach().numpy()
+    output = np.array2string(output, precision=4, separator=',',
+                      suppress_small=True)
+    return output
+
+gr.Interface(
+    get_out,
+    [
+        gr.components.Textbox(label="Input Amino Acid Sequence", placeholder = " Amino acid sequence here ...")
+    ],
+    ["text"],
+).launch()

checkpoint-final/config.json

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+  "transformers_version": "4.16.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 35
+}

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checkpoint-final/special_tokens_map.json

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checkpoint-final/training_args.bin

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checkpoint-final/vocab.json

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checkpoint-final/vocab.txt

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requirements.txt

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