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| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import torch | |
| import transformers | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from scipy.special import softmax | |
| def load_model(): | |
| model = AutoModelForSequenceClassification.from_pretrained('model_distilbert_trained') | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| 'distilbert-base-cased', do_lower_case=True) | |
| model.eval() | |
| return model, tokenizer | |
| def get_predictions(logits, indexes): | |
| sum = 0 | |
| ind = [] | |
| probs = [] | |
| for i in indexes: | |
| sum += logits[i] | |
| ind.append(i) | |
| probs.append(indexes[i]) | |
| if sum >= 0.95: | |
| return ind, probs | |
| def return_pred_name(name_dict, ind): | |
| out = [] | |
| for i in ind: | |
| out.append(name_dict[i]) | |
| return out | |
| def predict(title, summary, model, tokenizer): | |
| text = title + '.' + summary | |
| tokens = tokenizer.encode(text) | |
| with torch.no_grad(): | |
| logits = model(torch.as_tensor([tokens]))[0] | |
| probs = torch.softmax(logits[-1, :], dim=-1).data.cpu().numpy() | |
| classes = np.flip(np.argsort(probs)) | |
| sum_probs = 0 | |
| ind = 0 | |
| prediction = [] | |
| prediction_probs = [] | |
| while sum_probs < 0.95: | |
| prediction.append(name_dict[classes[ind]]) | |
| prediction_probs.append(str("{:.2f}".format(100 * probs[classes[ind]])) + "%") | |
| sum_probs += probs[classes[ind]] | |
| ind += 1 | |
| return prediction, prediction_probs | |
| def get_results(prediction, prediction_probs): | |
| frame = pd.DataFrame({'Category': prediction, 'Confidence': prediction_probs}) | |
| frame.index = np.arange(1, len(frame) + 1) | |
| return frame | |
| name_dict = {4: 'cs', | |
| 19: 'stat', | |
| 1: 'astro-ph', | |
| 16: 'q-bio', | |
| 6: 'eess', | |
| 3: 'cond-mat', | |
| 12: 'math', | |
| 15: 'physics', | |
| 18: 'quant-ph', | |
| 17: 'q-fin', | |
| 7: 'gr-qc', | |
| 13: 'nlin', | |
| 2: 'cmp-lg', | |
| 5: 'econ', | |
| 8: 'hep-ex', | |
| 11: 'hep-th', | |
| 14: 'nucl-th', | |
| 10: 'hep-ph', | |
| 9: 'hep-lat', | |
| 0: 'adap-org'} | |
| st.title("Find out the topic of the article without reading!") | |
| st.markdown("<h1 style='text-align: center;'><img width=320px src = 'https://upload.wikimedia.org/wikipedia/ru/8/81/Sheldon_cooper.jpg'>", | |
| unsafe_allow_html=True) | |
| # ^-- можно показывать пользователю текст, картинки, ограниченное подмножество html - всё как в jupyter | |
| title = st.text_area(label='Title', | |
| value='', | |
| height=30, | |
| help='If you know a title type it here') | |
| summary = st.text_area(label='Summary', | |
| value='', | |
| height=200, | |
| help='If you have a summary enter it here') | |
| button = st.button(label='Get the theme!') | |
| if button: | |
| if (title == '' and summary == ''): | |
| st.write('There is nothing to analyze...') | |
| st.write('Fill at list one of the fields') | |
| else: | |
| model, tokenizer = load_model() | |
| prediction, prediction_probs = predict(title, summary, model, tokenizer) | |
| ans = get_results(prediction, prediction_probs) | |
| st.write('Result') | |
| st.write(ans) | |
| # ^-- показать текстовое поле. В поле text лежит строка, которая находится там в данный момент | |
| #from transformers import pipeline | |
| #pipe = pipeline("ner", "Davlan/distilbert-base-multilingual-cased-ner-hrl") | |
| #raw_predictions = pipe(text) | |
| # тут уже знакомый вам код с huggingface.transformers -- его можно заменить на что угодно от fairseq до catboost | |
| #st.markdown(f"{raw_predictions}") | |
| # выводим результаты модели в текстовое поле, на потеху пользователю | |