commit from

app.py

@@ -53,7 +53,7 @@ def sigmoid(x):
   return 1/(1 + np.exp(-x))
 
 def get_top_predictions(predictions):
-  probs = (sigmoid(predictions) > 0).astype(int)
+  probs = (sigmoid(predictions) > 0.5).astype(float)
   probs = probs / np.sum(probs)
 
   res = {}
@@ -81,16 +81,96 @@ if not paper_title and not paper_summary:
   st.markdown(f"Must have non-empty title or summary")
 else:
   with torch.no_grad():
-    st.markdown(f"{model}", unsafe_allow_html=True)    
+import streamlit as st
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModel, DistilBertForSequenceClassification
+
+my_model_name = "istassiy/ysda_2022_ml2_hw3_distilbert_base_uncased"
+
+arxiv_code_to_topic = {
+  'cs' : 'computer science',
+
+  'q-bio' : 'biology',
 
+  'q-fin' : 'finance',
+
+  'astro-ph' : 'physics',
+  'cond-mat' : 'physics',
+  'gr-qc' : 'physics',
+  'hep-ex' : 'physics',
+  'hep-lat' : 'physics',
+  'hep-ph' : 'physics',
+  'hep-th' : 'physics',
+  'math-ph' : 'physics',
+  'nlin' : 'physics',
+  'nucl-ex' : 'physics',
+  'nucl-th' : 'physics',
+  'quant-ph' : 'physics',
+  'physics' : 'physics',
+
+  'eess' : 'electrical engineering',
+
+  'econ' : 'economics',
+
+  'math' : 'mathematics',
+
+  'stat' : 'statistics',
+}
+
+sorted_arxiv_topics = sorted(set(arxiv_code_to_topic.values()))
+
+NUM_LABELS = len(sorted_arxiv_topics)
+
+@st.cache(allow_output_mutation=True)
+def load_tokenizer():
+  tokenizer = AutoTokenizer.from_pretrained(my_model_name)
+  return tokenizer
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+  model = DistilBertForSequenceClassification.from_pretrained(my_model_name)
+  return model
+
+def sigmoid(x):
+  return 1/(1 + np.exp(-x))
+
+def get_top_predictions(predictions):
+  probs = (sigmoid(predictions) > 0.5).astype(float)
+  probs = probs / np.sum(probs)
+
+  res = {}
+  total_prob = 0
+  for topic, prob in zip(sorted_arxiv_topics, probs):
+    total_prob += prob
+    res[topic] = prob
+    if total_prob > 0.95:
+      break
+  return res
+
+tokenizer = load_tokenizer()
+model = load_model()
+
+st.markdown("# Scientific paper classificator")
+st.markdown(
+    "Fill in paper summary and / or title below:",
+    unsafe_allow_html=False
+)
+
+paper_title = st.text_area("Paper title")
+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:
+  with torch.no_grad():
     raw_predictions = model(
       **tokenizer(
         [paper_title + "." + paper_summary],
         padding=True, truncation=True, return_tensors="pt"
       )
-    )
-    dir_pred = dir(raw_predictions)
-    st.markdown(f"{dir_pred}")
-    st.markdown(f"{raw_predictions}")    
+    )  
     results = get_top_predictions(raw_predictions[0][0].numpy())
-    st.markdown(f"{results}")
+    st.markdown("The following are probabilities:")
+    for topic, prob in sorted(results.items(), lambda item: item[1], reverse=True):
+      st.markdown(f"{topic}: {prob}")