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Upload Summary.py
Browse files- pages/Summary.py +131 -0
pages/Summary.py
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import os
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import time
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import re
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import streamlit as st
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import requests
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from wordcloud import WordCloud
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import matplotlib.pyplot as plt
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# Установка API URL и заголовков
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API_URL_TRA = "https://api-inference.huggingface.co" \
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"/models/Helsinki-NLP/opus-mt-en-ru"
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API_URL_KEY = "https://api-inference.huggingface.co" \
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"/models/ml6team/keyphrase-extraction-kbir-inspec"
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API_URL_SUM = "https://api-inference.huggingface.co" \
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"/models/facebook/bart-large-cnn"
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TOKEN = os.getenv('API_TOKEN')
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HEADERS = {"Authorization": TOKEN}
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def hugging_api_request(url, payload):
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response = requests.post(url, headers=HEADERS, json=payload, timeout=120)
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body = response.json()
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if 'error' in body:
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print(response.status_code, body)
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if 'estimated_time' in body:
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time.sleep(body['estimated_time'])
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else:
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return
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hugging_api_request(url, payload)
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return body
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# Функция для получения ключевых слов
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def get_key_words(payload):
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return hugging_api_request(API_URL_KEY, payload)
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# Функция для перевода слова
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def translate_key_words(payload):
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return hugging_api_request(API_URL_TRA, payload)
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# Функция для составления конспекта
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def make_summary(payload):
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return hugging_api_request(API_URL_SUM, payload)
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# Очищаем список слов
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def clean_list(words_list):
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cleaned_words_list = []
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for word in words_list:
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word = word.lower()
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word = re.sub(r"[^а-яА-Яa-zA-Z\s]", "", word)
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word = word.lstrip()
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word = word.rstrip()
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cleaned_words_list.append(word)
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return cleaned_words_list
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# Настраиваем заголовок и название страницы
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st.set_page_config(layout="wide", page_title="Students' Personal Assistant")
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st.markdown(' # :female-student: Персональный помощник для студентов')
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st.divider()
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st.markdown('# :blue_book: Конспект на английском языке')
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col1, col2 = st.columns(2)
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text_from_tarea = col1.text_area('Введите тект статьи на английском языке',
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key='t_area', height=500)
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button_start = st.button('Обработать текст')
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key_words_list = []
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if button_start:
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with st.spinner('Составляем конспект...'):
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# Составляем конспект
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summary_text = make_summary({"inputs": text_from_tarea})
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col2.text_area('Конспект статьи', height=500,
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key='sum_area', value=summary_text[0]['summary_text'])
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with st.spinner('Получаем ключевые слова...'):
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# Извлекаем ключевые слова
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kew_words = get_key_words({"inputs": text_from_tarea})
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for key_word in kew_words:
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key_words_list.append(key_word['word'].lower())
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sorted_keywords = set(sorted(key_words_list))
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sorted_keywords = clean_list(sorted_keywords)
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with st.spinner('Переводим ключевые слова...'):
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# Переводим ключевые слова
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translated_words_dict = translate_key_words(
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{"inputs": sorted_keywords})
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translated_words_list = [
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word['translation_text'] for word in translated_words_dict]
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# Создаем карточки
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cleaned_words_list_ru = clean_list(translated_words_list)
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cards_list = []
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for item1, item2 in zip(sorted_keywords, cleaned_words_list_ru):
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cards_list.append([item1, item2])
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st.success('Готово')
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with st.spinner('Создаем WordCloud...'):
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# Выводим Word Cloud
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st.set_option('deprecation.showPyplotGlobalUse', False)
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words_str = ', '.join(sorted_keywords)
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w = WordCloud(background_color="white",
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width=1600, height=800).generate(words_str)
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plt.imshow(w, interpolation='bilinear')
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plt.imshow(w)
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plt.axis("off")
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st.pyplot()
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# Выводим карточки
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st.markdown('# :bookmark_tabs: Карточки из ключевых слов')
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col1, col2, col3 = st.columns(3)
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columns = [col1, col2, col3]
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for index, el in enumerate(cards_list):
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with columns[(index + 1) % 3]:
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with st.container(border=True):
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col4, col5 = st.columns([0.1, 0.9])
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with col4:
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st.write("# :flower_playing_cards:")
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with col5:
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st.write(f'## :green[{el[0]}]')
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st.write(f'### :blue[{el[1]}]')
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