Upload app.py

app.py

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+import time
+
+import streamlit as st
+
+from torch.nn import Softmax
+
+from model import ArxivModel, load_model
+from tokenizer import get_tokenizer
+
+from lables import num_to_classes
+
+
+start_time = time.time()
+model = load_model()
+end_time = time.time()
+
+print("Model:", (end_time - start_time))
+
+start_time = time.time()
+tokenizer = get_tokenizer()
+end_time = time.time()
+
+print("Tokenizer:", (end_time - start_time))
+
+arxiv_model = ArxivModel(model, tokenizer)
+softmax = Softmax(dim=1)
+
+st.markdown("### Classification of article topics")
+# st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
+
+text = st.text_area("Write title and (optional) summary of article")
+text = text.strip()
+
+if text != "":
+    idxs = arxiv_model.get_idx_class(text, thr=0.95)
+
+    for idx in idxs:
+        st.markdown(num_to_classes[idx])
+else:
+    st.markdown("")