Upload app.py

app.py

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+import streamlit as st
+import torch
+import numpy as np
+
+st.markdown("### A dummy site for classifying article topics by title and abstract.")
+st.markdown("It can predict the following topics: Computer Science, Economics, Electrical Engineering and Systems Science, Mathematics, Quantitative Biology, Quantitative Finance, Statistics, Physics")
+
+
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+@st.cache(suppress_st_warning=True)
+def model_tokenizer():
+    model_name = 'distilbert-base-cased'
+    #tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased", problem_type="multi_label_classification")
+    model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-cased", num_labels=8, problem_type="multi_label_classification")
+    weights = torch.load('model.pt', map_location=torch.device('cpu'))
+    model.load_state_dict(weights)
+    return model#, tokenizer
+
+def make_prediction(model, tokenizer, text):
+    #print(text)
+    tokens = tokenizer.encode(text)
+    with torch.no_grad():
+        logits = model.cpu()(torch.as_tensor([tokens]))[0]
+        #print(logits)
+        probs = np.array(torch.softmax(logits[-1, :], dim=-1))
+        #print(probs)
+    
+    sorted_classes, sorted_probs = np.flip(np.argsort(probs)), sorted(probs, reverse=True)
+    prediction_classes, prediction_probs = [], []
+    probs_sum = 0
+    i=0
+    res = []
+    while probs_sum <= 0.95:
+#         print(i)
+#         print(sorted_classes)
+#         print(sorted_classes[i])
+#         print(to_category)
+#         print(sorted_classes[i], to_category[sorted_classes[i]])
+        prediction_classes.append(to_category[sorted_classes[i]])
+        prediction_probs.append(100*sorted_probs[i])
+        probs_sum += sorted_probs[i]
+        i += 1
+    for pr, cl in zip(prediction_probs, prediction_classes):
+        print(str("{:.2f}".format(pr) + "%"), cl)
+        res.append((str("{:.2f}".format(pr) + "%"), cl))
+    return res
+
+model = model_tokenizer()
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased", problem_type="multi_label_classification")
+
+categories_full = ['Computer Science', 'Economics', 'Electrical Engineering and Systems Science', 'Mathematics', 'Quantitative Biology', 'Quantitative Finance', 'Statistics', 'Physics']
+
+to_category = {}
+
+for i in range(len(categories_full)):
+    to_category[i] = categories_full[i]
+
+
+
+title = st.text_area("Type the title of the article here")
+abstract = st.text_area("Type the abstract of the article here")
+
+
+if st.button('Analyse'):
+    if title or abstract:
+        text = '[TITLE] ' + title + ' [ABSTRACT] ' + abstract
+        res = make_prediction(model, tokenizer, text)
+        for cat in res:
+            st.markdown(f"{cat[0],  cat[1]}")
+    else:
+        st.error(f"Write title or abstract")
+
+#st.markdown(f"{make_prediction(model, tokenizer, text)}")