Upload app.py

app.py

@@ -0,0 +1,81 @@
+import numpy as np
+import pandas as pd
+import transformers
+import torch
+import tokenizers
+import streamlit as st
+
+NUM_LABELS = 15
+
+labels_names = {
+    0: 'Astrophysics',
+    1: 'Condensed Matter',
+    2: 'Computer Science',
+    3: 'Economics',
+    4: 'Electrical Engineering and Systems Science',
+    5: 'General Relativity and Quantum Cosmology',
+    6: 'High Energy Physics',
+    7: 'Mathematics',
+    8: 'Nonlinear Sciences',
+    9: 'Nuclear Theory',
+    10: 'General Physics',
+    11: 'Quantitative Biology',
+    12: 'Quantitative Finance',
+    13: 'Quantum Physics',
+    14: 'Statistics',
+}
+
+@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None}, suppress_st_warning=True)
+def get_model(model_name, model_path):
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+    model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=NUM_LABELS)
+    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
+    model.eval()
+    return model, tokenizer
+    
+@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None}, suppress_st_warning=True)
+def predict(text, tokenizer, model, temperature = 1):
+    tokens = tokenizer.encode(text)
+    with torch.no_grad():
+        logits = model.cpu()(torch.as_tensor([tokens]))[0]
+        probs = torch.softmax(logits[-1, :] / temperature, dim=-1).data.cpu().numpy()
+    
+    indexes_descending = np.argsort(probs)[::-1]
+    percents = 0
+    preds = []
+    pred_probs = []
+    for index in indexes_descending:
+        preds.append(labels_names[index])
+        pred_prob = 100 * probs[index]
+        pred_probs.append(f"{pred_prob:.1f}%")
+
+        percents += pred_prob
+        if percents >= 95:
+          break
+
+    result = pd.DataFrame({'Probability': pred_probs})
+    result.index = preds
+    
+    return result
+
+model, tokenizer = get_model('distilbert-base-cased', 'distilbert-checkpoint-10983.bin')
+
+st.title("Yandex School of Data Analysis. ML course, laboratory work 2: classifier of categories of scientific papers")
+st.markdown("<img width=500px src='https://m.media-amazon.com/images/I/71XOMSKx8NL._AC_SL1500_.jpg'>", unsafe_allow_html=True)
+st.markdown("Enter the title of the article and its abstract (although, if you really don't want to, you can do with just the title)")
+
+title = st.text_area(label='Title of the article', height=200)
+abstract = st.text_area(label='Abstract of the article', height=400)
+button = st.button('Go')
+
+if button:
+    try:
+        text = ' [ABSTRACT] '.join(title, abstract)
+        result = predict(text, tokenizer, model)
+        if len(text) > 10:
+            st.subheader('I think, this paper related to')
+            st.write(result)
+        else:
+            st.error("Enter some more info please")
+    except Exception:
+        st.error("Ooooops, something went wrong. Try again please and report to me, tg: @vladyur")