Add streamlit app

app.py

@@ -0,0 +1,42 @@
+import sys
+import tempfile
+from io import StringIO
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent
+sys.path.insert(0, str(ROOT))
+
+import pandas as pd
+import torch
+import streamlit as st
+from torch.utils.data import DataLoader
+
+from data import Vocab
+from model import *
+from utils import Peptides, fasta2df, predict, dict2df
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = torch.load(Path(ROOT / "results" / "model.pt"), map_location=device)
+
+st.header("BeeTLe: A Framework for Linear B-Cell Epitope Prediction and Classification")
+uploaded_file = st.file_uploader("Upload a FASTA file")
+if uploaded_file is not None:
+    temp_dir = tempfile.TemporaryDirectory()
+    temp_file = Path(temp_dir.name) / uploaded_file.name
+    with open(temp_file, "wb") as f:
+        f.write(uploaded_file.getvalue())
+    df_in = fasta2df(temp_file)
+    vocab = Vocab(max_len=25)
+    dataset = Peptides(df_in, vocab)
+    dataloader = DataLoader(dataset, batch_size=32, collate_fn=dataset.collate_fn)
+    output = Path(temp_file).with_suffix(".csv")
+    lst = predict(dataloader, model)
+    df_out = dict2df(lst)
+    df = pd.concat([df_in, df_out], axis=1)
+    csv = df.to_csv(output).encode("utf-8")
+    st.download_button(
+        label="Download result as a CSV file",
+        data=csv,
+        file_name=output,
+        mime="text/csv",
+    )