app file

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModel
+from sklearn.metrics.pairwise import pairwise_distances, cosine_similarity
+
+tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2")
+model = AutoModel.from_pretrained("cointegrated/rubert-tiny2")
+
+df = pd.read_csv('data_final.csv')
+
+MAX_LEN = 300
+
+# @st.cache_resource
+def embed_bert_cls(text, model, tokenizer):
+    t = tokenizer(text, padding=True, truncation=True, return_tensors='pt', max_length=MAX_LEN)
+    with torch.no_grad():
+        model_output = model(**{k: v.to(model.device) for k, v in t.items()})
+    embeddings = model_output.last_hidden_state[:, 0, :]
+    embeddings = torch.nn.functional.normalize(embeddings)
+    return embeddings[0].cpu().numpy()
+
+books_vector = np.loadtxt('vectors.txt')
+
+st.title('Приложение для рекомендации книг')
+
+text = st.text_input('Введите запрос:')
+num_results = st.number_input('Введите количество рекомендаций:', min_value=1, max_value=50, value=1)
+
+recommend_button = st.button('Найти')
+
+if text and recommend_button:
+    user_text_pred = embed_bert_cls(text, model, tokenizer)
+    list_ = pairwise_distances(user_text_pred.reshape(1, -1), books_vector).argsort()[0][:num_results]
+
+    st.subheader('Топ рекомендуемых книг:')
+
+    for i in list_:
+        col_1, col_2 = st.columns([1, 3])
+
+        with col_1:
+            st.image(df['image_url'][i], use_column_width=True)
+        with col_2:
+            st.write(f'Название книги: {df["title"][i]}')
+            st.write(f'Название книги: {df["author"][i]}')
+            st.write(f'Название книги: {df["annotation"][i]}')
+            st.write(f'{df["page_url"][i]}')