commit from

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import numpy as np
-from tokenizers import Tokenizer
+import torch
 from transformers import AutoTokenizer, AutoModel
 
 my_model_name = "istassiy/ysda_2022_ml2_hw3_distilbert_base_uncased"
@@ -80,11 +80,12 @@ paper_summary = st.text_area("Paper abstract")
 if not paper_title and not paper_summary:
   st.markdown(f"Must have non-empty title or summary")
 else:
-  raw_predictions = model(
-    **tokenizer(
-      [paper_title + "." + paper_summary],
-      padding=True, truncation=True, return_tensors="pt"
+  with torch.no_grad():
+    raw_predictions = model(
+      **tokenizer(
+        [paper_title + "." + paper_summary],
+        padding=True, truncation=True, return_tensors="pt"
+      )
     )
-  )
-  results = get_top_predictions(raw_predictions[0].numpy())
-  st.markdown(f"{results}")
+    results = get_top_predictions(raw_predictions[0].numpy())
+    st.markdown(f"{results}")