Upload app.py

app.py

@@ -0,0 +1,57 @@
+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, taxonomy
+
+from parser import get_text_title
+
+model = load_model()
+tokenizer = get_tokenizer()
+
+arxiv_model = ArxivModel(model, tokenizer)
+softmax = Softmax(dim=1)
+
+st.markdown("### Classification of article topics")
+
+col1, col2 = st.columns(2)
+
+text = ""
+with col1:
+    title_text = st.text_area("Write title of article", key='arxiv_title_input')
+
+with col2:
+    summary_text = st.text_area("Write summary of article (optional)", key='arxiv_sum_input')
+click_button_text = st.button('Submit title and summary', key=1)
+
+if click_button_text and summary_text.strip() != "":
+    text = title_text.strip() + '\t' + summary_text.strip()
+else:
+    text = title_text.strip()
+text = text.strip()
+
+id_url = st.text_input("Write article's url or id", key='arxiv_id_input').strip()
+click_button_url = st.button('Submit id', key=1)
+
+if click_button_url and id_url != "":
+    res = get_text_title(id_url)
+    if res is not None:
+        text = res[0].strip() + '\t' + res[1].strip()
+        text = text.strip()
+    else:
+        st.markdown(f'<p style="color:#FF2D00;font-size:18px">Incorrect url or id</p>', unsafe_allow_html=True)
+        text = ""
+
+print(text)
+
+if text != "":
+    idxs = arxiv_model.get_idx_class(text, thr=0.95)[:10]
+
+    for idx, prob in idxs:
+        if taxonomy.get(num_to_classes[idx], -1) != -1:
+            st.markdown("{} \t {}%".format(taxonomy.get(num_to_classes[idx], -1), round(prob * 100, 1)))
+else:
+    st.markdown("")