Update stri.py

stri.py

@@ -2,6 +2,7 @@ import streamlit as st
 import torch
 import numpy as np
 import pandas as pd
+from PIL import Image
 from transformers import AutoTokenizer, AutoModel
 import re
 import pickle
@@ -17,7 +18,6 @@ model = AutoModel.from_pretrained(model_name, output_hidden_states=True)
 books = pd.read_csv('books_6000.csv')
 books.dropna(inplace=True)
 
-
 books = books[books['annotation'].apply(lambda x: len(x.split()) >= 10)]
 books.drop_duplicates(subset='title', keep='first', inplace=True)
 books = books.reset_index(drop=True)
@@ -42,24 +42,24 @@ max_len = 128
 # Определение запроса пользователя
 query = st.text_input("Введите запрос")
 
-if st.button('**Generating recommendations**'):
+if st.button('Сгенерировать'):
     with open("book_embeddings.pkl", "rb") as f:
         book_embeddings = pickle.load(f)
-        
+
     query_tokens = tokenizer.encode(query, add_special_tokens=True,
                                     truncation=True, max_length=max_len)
-    
+
     query_padded = np.array(query_tokens + [0] * (max_len - len(query_tokens)))
     query_mask = np.where(query_padded != 0, 1, 0)
-    
+
     # Переведем numpy массивы в тензоры PyTorch
     query_padded = torch.tensor(query_padded, dtype=torch.long)
     query_mask = torch.tensor(query_mask, dtype=torch.long)
-    
+
     with torch.no_grad():
         query_embedding = model(query_padded.unsqueeze(0), query_mask.unsqueeze(0))
-        query_embedding = query_embedding[0][:, 0, :] 
-    
+        query_embedding = query_embedding[0][:, 0, :]
+
     # Вычисление косинусного расстояния между эмбеддингом запроса и каждой аннотацией
     cosine_similarities = torch.nn.functional.cosine_similarity(
         query_embedding.squeeze(0),
@@ -67,8 +67,14 @@ if st.button('**Generating recommendations**'):
     )
 
     cosine_similarities = cosine_similarities.numpy()
-    
+
     indices = np.argsort(cosine_similarities)[::-1]  # Сортировка по убыванию
-    
-    for i in indices[:10]:
-        st.write(books['title'][i])
+
+    num_books_per_page = st.selectbox("Количество книг на странице:", [3, 5, 10], index=0)
+
+    for i in indices[:num_books_per_page]:
+        st.write("## " + books['title'][i])
+        st.write("**Автор:**", books['author'][i])
+        st.write("**Аннотация:**", books['annotation'][i])
+        st.image(Image.open(books['image_url'][i]))
+        st.write("---")