Upload Project.py

pages/Project.py

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+import streamlit as st
+import pandas as pd
+import requests
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+import os 
+import re
+
+# Установка API URL и заголовков
+API_URL_gen = "https://api-inference.huggingface.co/models/facebook/blenderbot-400M-distill"
+API_URL_tra = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-ru"
+API_URL_key = "https://api-inference.huggingface.co/models/ml6team/keyphrase-extraction-kbir-inspec"
+API_URL_sum = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
+
+headers = {"Authorization": os.getenv("api_token")}
+
+# Функция для генерирования предложения
+def generate_example(payload):
+	response = requests.post(API_URL_gen, headers=headers, json=payload)
+	return response.json()
+
+# Функция для получения ключевых слов
+def get_key_words(payload):
+	response = requests.post(API_URL_key, headers=headers, json=payload)
+	return response.json()
+
+# Функция для перевода слова
+def translate_key_words(payload):
+	response = requests.post(API_URL_tra, headers=headers, json=payload)
+	return response.json()
+
+# Функция для составления конспекта
+def make_summary(payload):
+	response = requests.post(API_URL_sum, headers=headers, json=payload)
+	return response.json()
+
+
+# Очищаем список слов
+def clean_list(words_list):
+    cleaned_words_list = []
+    for word in words_list:
+        word = word.lower()
+        word =re.sub(r"[^а-яА-Яa-zA-Z\s]", "", word)
+        word = word.lstrip()
+        word = word.rstrip()
+        cleaned_words_list.append(word)
+    return cleaned_words_list
+
+
+# Настраеваем заголовок и название страницы
+st.set_page_config(layout="wide", page_title="Students' Personal Assistant")
+st.markdown(' # :female-student: Персональный помощник для студентов')
+
+st.divider()
+st.markdown('## :flower_playing_cards: Как назвать?')
+
+
+st.markdown('# :bookmark_tabs: :bookmark_tabs:  :bookmark_tabs:  :bookmark_tabs: ')
+col1, col2 = st.columns(2)
+text_from_tarea = col1.text_area('Введите тект статьи на английском языке', height=500)
+
+
+button_start = st.button('Обработать текст')
+
+key_words_list = []
+if button_start:
+    
+    with st.spinner('...'):
+        summary_text = make_summary({"inputs": text_from_tarea})
+        col2.text_area('Конспект статьи', height=500, value=summary_text[0]['summary_text']) 
+        kew_words = get_key_words({	"inputs": text_from_tarea,})
+        for key_word in kew_words :
+            key_words_list.append(key_word['word'].lower())
+        
+        sorted_keywords = set(sorted(key_words_list))
+        sorted_keywords = clean_list(sorted_keywords)
+
+        translated_words_list = []
+        for key_word in sorted_keywords:   
+            res = translate_key_words({"inputs": key_word,})
+            translated_words_list.append(res[0]['translation_text'])
+
+        cleaned_words_list_ru = clean_list(translated_words_list)
+
+        cards_list = []
+        for item1, item2 in zip(sorted_keywords, cleaned_words_list_ru):            
+            cards_list.append([item1, item2])
+        
+        # Преобразуем полученные данные в DataFrame
+        #cards_df = pd.DataFrame(cards_list, columns=['word', 'translated', 'example'])
+    st.success('Готово')
+
+    # Выводим Word Cloud
+    st.set_option('deprecation.showPyplotGlobalUse', False)
+    words_str = ', '.join(sorted_keywords)
+    w = WordCloud(background_color="white").generate(words_str)
+    plt.imshow(w, interpolation='bilinear')
+    plt.imshow(w)
+    plt.axis("off")
+    st.pyplot()
+
+    # Выводим карточки 
+    for el in cards_list:
+        with st.chat_message("assistant"):
+            #st.divider()            
+            st.markdown('# :flower_playing_cards:')
+            st.markdown(f'#  :green[{el[0]}]')
+            st.markdown(f'## :blue[{el[1]}]')            
+            st.divider()