Duplicate from MARI-posa/FindMyBook

Co-authored-by: Maria <MARI-posa@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+all+.csv filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: FindMyBook
+emoji: 📈
+colorFrom: purple
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.21.0
+app_file: stri.py
+pinned: false
+duplicated_from: MARI-posa/FindMyBook
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

all+.csv

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+size 57131790

book_embeddings.pkl

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book_embeddings256.pkl

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book_embeddings32.pkl

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book_embeddings512.pkl

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book_embeddingsN.pkl

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requirements.txt

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+streamlit==1.23.1
+torch==2.0.1
+numpy==1.23.5
+pandas==1.5.3
+transformers==4.30.0
+regex==2022.10.31

stri.py

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+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
+import requests
+from io import BytesIO
+
+st.title("Книжные рекомендации")
+
+# Загрузка модели и токенизатора
+model_name = "cointegrated/rubert-tiny2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name, output_hidden_states=True)
+
+# Загрузка датасета и аннотаций к книгам
+books = pd.read_csv('all+.csv')
+books.dropna(inplace=True)
+
+books = books[books['annotation'].apply(lambda x: len(x.split()) >= 40)]
+books.drop_duplicates(subset='title', keep='first', inplace=True)
+books = books.reset_index(drop=True)
+
+
+def data_preprocessing(text: str) -> str:
+    text = re.sub(r'http\S+', " ", text)  # удаляем ссылки
+    text = re.sub(r'@\w+', ' ', text)  # удаляем упоминания пользователей
+    text = re.sub(r'#\w+', ' ', text)  # удаляем хэштеги
+    text = re.sub(r'<.*?>', ' ', text)  # html tags
+    return text
+
+
+for i in ['author', 'title', 'annotation']:
+    books[i] = books[i].apply(data_preprocessing)
+
+annot = books['annotation']
+
+# Получение эмбеддингов аннотаций каждой книги в датасете
+length = 512
+
+# Определение запроса пользователя
+query = st.text_input("Введите запрос")
+
+if st.button('Сгенерировать'):
+    with open("book_embeddingsN.pkl", "rb") as f:
+        book_embeddings = pickle.load(f)
+
+    query_tokens = tokenizer.encode_plus(
+            query,
+            add_special_tokens=True,
+            max_length=length, # Ограничение на максимальную длину входной последовательности
+            pad_to_max_length=True, # Дополним последовательность нулями до максимальной длины
+            return_tensors='pt' # Вернём тензоры PyTorch
+        )
+
+    with torch.no_grad():
+            query_outputs = model(**query_tokens)
+            query_hidden_states = query_outputs.hidden_states[-1][:,0,:]
+            query_hidden_states = torch.nn.functional.normalize(query_hidden_states)
+
+        
+    # Вычисление косинусного расстояния между эмбеддингом запроса и каждой аннотацией
+    cosine_similarities = torch.nn.functional.cosine_similarity(
+        query_embedding.squeeze(0),
+        torch.stack(book_embeddings.cpu())
+    )
+
+    cosine_similarities = cosine_similarities.numpy()
+
+    indices = np.argsort(cosine_similarities)[::-1]  # Сортировка по убыванию
+
+    num_books_per_page = st.selectbox("Количество книг на странице:", [3, 5, 10], index=0)
+
+    for i in indices[:num_books_per_page]:
+        cols = st.columns(2)  # Создание двух столбцов для размещения информации и изображения
+        cols[1].write("## " + books['title'][i])
+        cols[1].markdown("**Автор:** " + books['author'][i])
+        cols[1].markdown("**Аннотация:** " + books['annotation'][i])
+        image_url = books['image_url'][i]
+        response = requests.get(image_url)
+        image = Image.open(BytesIO(response.content))
+        cols[0].image(image)
+        cols[0].write(cosine_similarities[i])
+        cols[1].write("---")