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| import streamlit as st | |
| import pandas as pd | |
| import torch | |
| from transformers import BertTokenizer, BertModel | |
| import faiss | |
| import numpy as np | |
| import re | |
| import nltk | |
| from nltk.corpus import stopwords | |
| # Загрузка стоп-слов для английского языка | |
| nltk.download('stopwords') | |
| stop_words = set(stopwords.words('english')) | |
| def load_data(url): | |
| df = pd.read_csv(url) | |
| return df | |
| def embedding_and_index(): | |
| embeddings_array = np.load('data/embeddings_eng.npy') | |
| index = faiss.read_index('data/desc_faiss_index_eng.index') | |
| return embeddings_array, index | |
| def load_model(): | |
| model = BertModel.from_pretrained('bert-base-uncased') | |
| return model | |
| def clean_text(text): | |
| text = text.lower() | |
| text = re.sub(r'[^\w\s]', '', text) | |
| text = ' '.join(word for word in text.split() if word not in stop_words) | |
| return text | |
| st.header("Selection of films by description✏️🔍") | |
| # Загрузка данных | |
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') | |
| df = load_data('data/eng_data.csv') | |
| embeddings_array, index = embedding_and_index() | |
| model = load_model() | |
| # Пользовательский ввод | |
| user_input = st.text_input("Enter a movie description:", value="", help="The more detailed your description is, the more accurately we can choose a film for you 🤗'") | |
| if st.button("Search🔍🎦"): | |
| if user_input: | |
| def encode_description(description, tokenizer, model): | |
| tokens = tokenizer(description, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**tokens) | |
| embeddings = outputs.last_hidden_state.mean(dim=1) | |
| return embeddings.cpu().numpy().astype('float32') | |
| # Применяем очистку текста к пользовательскому вводу | |
| cleaned_input = clean_text(user_input) | |
| # Векторизация очищенного запроса | |
| input_embedding = encode_description(cleaned_input, tokenizer, model) | |
| # Поиск с использованием Faiss | |
| _, sorted_indices = index.search(input_embedding.reshape(1, -1), 5) | |
| # Используйте индексы для извлечения строк из DataFrame | |
| recs = df.iloc[sorted_indices[0]].reset_index(drop=True) | |
| recs.index = recs.index + 1 | |
| # Вывод рекомендованных фильмов с изображениями | |
| st.subheader("Recommended movies 🎉:") | |
| for i in range(5): | |
| st.markdown(f"<span style='font-size:{20}px; color:purple'>{recs['movie_title'].iloc[i]}</span>", unsafe_allow_html=True) | |
| # Создаем две колонки: одну для текста, другую для изображения | |
| col1, col2 = st.columns([2, 1]) | |
| # В колонке отображаем название фильма, описание, роли и ссылку | |
| col1.info(recs['description'].iloc[i]) | |
| col1.markdown(f"**You can watch the film [here]({recs['page_url'].iloc[i]})**") | |
| # В колонке отображаем изображение | |
| col2.image(recs['image_url'].iloc[i], caption=recs['movie_title'].iloc[i], width=200) | |
| with st.sidebar: | |
| st.info(""" | |
| #### Were we able to help you with the choice? | |
| """) | |
| feedback = st.text_input('Share with us') | |
| feedback_button = st.button("Send feedback", key="feedback_button") | |
| if feedback_button and feedback: | |
| feedback_container.success("Thank you, every day we try to be better for you 💟") | |
| elif feedback_button: | |
| feedback_container.warning("Please enter a review before submitting") |