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| import streamlit as st | |
| import pickle | |
| import torch | |
| import numpy as np | |
| from transformers import TrainingArguments, Trainer, AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification | |
| from PIL import Image | |
| with open('labels.pickle', 'rb') as handle: | |
| labels = pickle.load(handle) | |
| # @st.cache | |
| def predict_topic_by_title_and_abstract(text): | |
| tokens = tokenizer(text, return_tensors='pt', truncation=True) | |
| with torch.no_grad(): | |
| logits = model(**tokens).logits | |
| probs = torch.nn.functional.softmax(logits[0], dim=0).numpy() * 100 | |
| ans = list(zip(probs,labels.values())) | |
| ans.sort(reverse=True) | |
| sum = 0 | |
| i = 0 | |
| while sum <= 95: | |
| prob, label = ans[i] | |
| st.write("it's topic \"" + label + "\" with probability "+ str(np.round(prob,1)) + "%") | |
| sum += prob | |
| i += 1 | |
| tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") | |
| #model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=8) | |
| #model.load_state_dict( | |
| # torch.load( | |
| # "./trained_model" | |
| # ) | |
| #) | |
| with open('trained_model.pickle', 'rb') as handle: | |
| model = pickle.load(handle) | |
| image = Image.open('logo.png') | |
| st.image(image) | |
| st.markdown("##### This app predicts the probabilities of the article belonging to the following topics: \'biology\', \'computer science\', \'economics\', \'electrics\', \'finance\', \'math\', \'physics\', \'statistics\'.") | |
| st.markdown("##### To get an article topic prediction, please write down it's title, abstract, or both.") | |
| st.markdown('<style>textarea { background: #E8E8E8 !important;}</style>', unsafe_allow_html=True) | |
| title = st.text_area("Write article title:", height=30) | |
| abstract = st.text_area("Write article abstract:", height=60) | |
| input_text = title + " " + abstract | |
| input_text = ''.join(filter(str.isalnum, input_text)) | |
| if len(input_text.split()) > 0: | |
| predict_topic_by_title_and_abstract(input_text) |