updated

app.py

@@ -109,71 +109,13 @@
 #                 st.write(f'Автор: {df["author"][i]}')
 #                 st.write(f'Ссылка: {df["page_url"][i]}')
 #                 st.write(f'Аннотация: {df["annotation"][i]}')
-# 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
-# import faiss
-
-# 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(hash_funcs={tokenizers.Tokenizer: my_hash_func})
-# 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()
-
-# @st.cache_data
-# def load_faiss_index():
-#     books_embs = np.loadtxt('vectors.txt')
-#     index = faiss.IndexFlatIP(books_embs.shape[1])
-#     index.add(books_embs)
-#     return index
-
-# st.title('Приложение для рекомендации книг')
-
-# category_filter = st.selectbox('Выберите категорию книги (необязательно)', ['Все'] + list(df['category'].unique()))
-
-# text = st.text_input('Введите запрос:')
-# top_n = st.number_input('Введите количество рекомендаций:', min_value=1, max_value=50, value=1)
-
-# recommend_button = st.button('Найти')
-
-# if text and recommend_button:
-#     query_emb = embed_bert_cls(text, model, tokenizer)
-#     index = load_faiss_index()
-#     D, I = index.search(query_emb.reshape(1, -1), top_n)
-
-#     st.subheader('Топ рекомендуемых книг:')
-
-#     for i, j in zip(I[0], D[0]):
-#         if category_filter == 'Все' or df['category'][i] == category_filter:
-#             col_1, col_2 = st.columns([1, 3])
-
-#             with col_1:
-#                 st.image(df['image_url'][i], use_column_width=True)
-#                 st.write(round(j, 2))
-#             with col_2:
-#                 st.write(f'Название книги: **{df["title"][i]}**')
-#                 st.write(f'Автор: {df["author"][i]}')
-#                 st.write(f'Ссылка: {df["page_url"][i]}')
-#                 st.write(f'Аннотация: {df["annotation"][i]}')
 
 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
 import faiss
 
 tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2")
@@ -182,13 +124,8 @@ model = AutoModel.from_pretrained("cointegrated/rubert-tiny2")
 df = pd.read_csv('data_final.csv')
 
 MAX_LEN = 300
-@st.cache_data
-def load_faiss_index():
-    books_embs = np.loadtxt('vectors.txt')
-    index = faiss.IndexFlatIP(books_embs.shape[1])
-    index.add(books_embs)
-    return index
 
+# @st.cache(hash_funcs={tokenizers.Tokenizer: my_hash_func})
 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():
@@ -197,23 +134,12 @@ def embed_bert_cls(text, model, tokenizer):
     embeddings = torch.nn.functional.normalize(embeddings)
     return embeddings[0].cpu().numpy()
 
-@st.cache_data()
-def get_recommendations(query_emb, top_n):
-    index = load_faiss_index()
-    D, I = index.search(query_emb.reshape(1, -1), top_n)
-    recommendations = []
-    for i, j in zip(I[0], D[0]):
-        recommendation = {
-            'image_url': df['image_url'][i],
-            'title': df['title'][i],
-            'author': df['author'][i],
-            'page_url': df['page_url'][i],
-            'annotation': df['annotation'][i],
-            'category': df['category'][i],
-            'similarity_score': round(j, 2)
-        }
-        recommendations.append(recommendation)
-    return recommendations
+@st.cache_data
+def load_faiss_index():
+    books_embs = np.loadtxt('vectors.txt')
+    index = faiss.IndexFlatIP(books_embs.shape[1])
+    index.add(books_embs)
+    return index
 
 st.title('Приложение для рекомендации книг')
 
@@ -226,22 +152,97 @@ recommend_button = st.button('Найти')
 
 if text and recommend_button:
     query_emb = embed_bert_cls(text, model, tokenizer)
-    recommendations = get_recommendations(query_emb, top_n)
-
-    if not recommendations:  # Если рекомендации не найдены
-        st.write('По вашему запросу ничего не найдено.')
-    else:
-        st.subheader('Топ рекомендуемых книг:')
-        for recommendation in recommendations:
-            if category_filter == 'Все' or recommendation['category'] == category_filter:
-                col_1, col_2 = st.columns([1, 3])
-                with col_1:
-                    st.image(recommendation['image_url'], use_column_width=True)
-                    st.write(recommendation['similarity_score'])
-                with col_2:
-                    st.write(f'Название книги: **{recommendation["title"]}**')
-                    st.write(f'Автор: {recommendation["author"]}')
-                    st.write(f'Ссылка: {recommendation["page_url"]}')
-                    st.write(f'Аннотация: {recommendation["annotation"]}')
-                    st.write(f'Категория: {recommendation["category"]}')
+    index = load_faiss_index()
+    D, I = index.search(query_emb.reshape(1, -1), top_n)
+
+    st.subheader('Топ рекомендуемых книг:')
+
+    for i, j in zip(I[0], D[0]):
+        if category_filter == 'Все' or df['category'][i] == category_filter:
+            col_1, col_2 = st.columns([1, 3])
+
+            with col_1:
+                st.image(df['image_url'][i], use_column_width=True)
+                st.write(round(j, 2))
+            with col_2:
+                st.write(f'Название книги: **{df["title"][i]}**')
+                st.write(f'Автор: {df["author"][i]}')
+                st.write(f'Ссылка: {df["page_url"][i]}')
+                st.write(f'Аннотация: {df["annotation"][i]}')
+
+# import streamlit as st
+# import pandas as pd
+# import numpy as np
+# import torch
+# from transformers import AutoTokenizer, AutoModel
+# import faiss
+
+# 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_data
+# def load_faiss_index():
+#     books_embs = np.loadtxt('vectors.txt')
+#     index = faiss.IndexFlatIP(books_embs.shape[1])
+#     index.add(books_embs)
+#     return index
+
+# 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()
+
+# @st.cache_data()
+# def get_recommendations(query_emb, top_n):
+#     index = load_faiss_index()
+#     D, I = index.search(query_emb.reshape(1, -1), top_n)
+#     recommendations = []
+#     for i, j in zip(I[0], D[0]):
+#         recommendation = {
+#             'image_url': df['image_url'][i],
+#             'title': df['title'][i],
+#             'author': df['author'][i],
+#             'page_url': df['page_url'][i],
+#             'annotation': df['annotation'][i],
+#             'category': df['category'][i],
+#             'similarity_score': round(j, 2)
+#         }
+#         recommendations.append(recommendation)
+#     return recommendations
+
+# st.title('Приложение для рекомендации книг')
+
+# category_filter = st.selectbox('Выберите категорию книги (необязательно)', ['Все'] + list(df['category'].unique()))
+
+# text = st.text_input('Введите запрос:')
+# top_n = st.number_input('Введите количество рекомендаций:', min_value=1, max_value=50, value=1)
+
+# recommend_button = st.button('Найти')
+
+# if text and recommend_button:
+#     query_emb = embed_bert_cls(text, model, tokenizer)
+#     recommendations = get_recommendations(query_emb, top_n)
+
+#     if not recommendations:  # Если рекомендации не найдены
+#         st.write('По вашему запросу ничего не найдено.')
+#     else:
+#         st.subheader('Топ рекомендуемых книг:')
+#         for recommendation in recommendations:
+#             if category_filter == 'Все' or recommendation['category'] == category_filter:
+#                 col_1, col_2 = st.columns([1, 3])
+#                 with col_1:
+#                     st.image(recommendation['image_url'], use_column_width=True)
+#                     st.write(recommendation['similarity_score'])
+#                 with col_2:
+#                     st.write(f'Название книги: **{recommendation["title"]}**')
+#                     st.write(f'Автор: {recommendation["author"]}')
+#                     st.write(f'Ссылка: {recommendation["page_url"]}')
+#                     st.write(f'Аннотация: {recommendation["annotation"]}')
+#                     st.write(f'Категория: {recommendation["category"]}')