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| import streamlit as st | |
| import pandas as pd | |
| import random as rd | |
| import webbrowser as wb | |
| import numpy as np | |
| from find import find_similar,df,lems_eng,lems_rus,clean,find_unsimilar | |
| st.set_page_config( | |
| page_title="Умный поиск книг", | |
| page_icon="📖", | |
| layout="wide", | |
| #initial_sidebar_state="expanded" | |
| ) | |
| with st.expander('Исходный датафрейм'): | |
| if st.checkbox('С лемматизацией'): | |
| df | |
| else: | |
| df.iloc[:,:-1] | |
| st.title('Умный поиск книг') | |
| #negability= st.checkbox('Негативный промт (beta)') | |
| with st.form(key='search_form'): | |
| input=st.text_input('Введите поисковый запрос','Пример запроса') | |
| # if negability: | |
| # neg=st.text_input('Введите отрицательный запрос') | |
| search_but=st.form_submit_button('Искать') | |
| items_per_page=st.number_input('Количество книг на странице',min_value=1,max_value=10,value=5) | |
| # if search_but: | |
| # st.session_state.clicked = True | |
| #st.toast('Уфф') | |
| #@st.cache_data(experimental_allow_widgets=True) | |
| def books_show(books_idx,sim,n=items_per_page): | |
| col=[] | |
| books=df.copy().iloc[books_idx][:n] | |
| for author in books['author']: | |
| if author.find('Донцова')!=-1: | |
| #st.toast('Уфф') | |
| pass | |
| books['sims']=sims[:n] | |
| with st.expander('Датафрейм с результатами'): | |
| books | |
| #print(books.index) | |
| for i,book_id in enumerate(books_idx[:n]): | |
| pic_col,text_col=st.columns([0.2,0.8]) | |
| '---' | |
| url=books.loc[book_id][0] | |
| #url | |
| pic_col.image(books.loc[book_id,'image_url'],use_column_width=True) | |
| pic_col.markdown(f'<a href={url} target="_blank">Ссылка на книгу</a>', unsafe_allow_html=True) | |
| pic_col.markdown(f'**Степень похожести:** {books.loc[book_id,"sims"]:.4f}') | |
| #col[i][0].button('Купить',key=books['page_url'][i],on_click=lambda: wb.open_new_tab(books['page_url'][i])) | |
| text_col.markdown('## ' + books.loc[book_id, 'title']) | |
| text_col.markdown('**Автор:** ' + books.loc[book_id, 'author']) | |
| text_col.markdown('**Жанр:** ' + books.loc[book_id, 'genre']) | |
| text_col.markdown('**Аннотация:** ' + books.loc[book_id, 'annotation']) | |
| if search_but: | |
| neg_mark=input.find(' -') | |
| cleaned_input=clean(lems_eng(lems_rus(input[:neg_mark]))) | |
| cleaned_neg=clean(lems_eng(lems_rus(input[neg_mark+2:]))) | |
| #print(cleaned_neg.split(),df.loc[15390,'lemmatized'].split()) | |
| with st.spinner('Wait for it...'): | |
| if neg_mark!=-1: | |
| st.markdown(f'**Лемматизированный запрос:** {cleaned_input} \n\n **Лемматизированый негативный запрос:** {cleaned_neg}') | |
| sims,books_idx=find_similar(cleaned_input,50) | |
| for book in books_idx: | |
| if any(word in cleaned_neg.split() for word in df.loc[book,'lemmatized'].split()): | |
| books_idx=np.delete(books_idx,np.where(books_idx==book)) | |
| else: | |
| st.markdown(f'**Лемматизированный запрос:** {cleaned_input}') | |
| sims,books_idx=find_similar(cleaned_input) | |
| print(f'Похожести:\n{sims}\nИндексы:\n{books_idx}') | |
| books_show(books_idx,sims) | |