Delete app.py

app.py

@@ -1,75 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import torch
-from sentence_transformers import SentenceTransformer
-from resources.functions import recommend, find_rows_with_genres, get_mask_in_range
-
-
-st.markdown(f"<h1 style='text-align: center;'>Семантический поиск фильмов", unsafe_allow_html=True)
-
-df = pd.read_csv('resources/DF_FINAL.csv')
-genre_lists = df['ganres'].apply(lambda x: x.split(', ') if isinstance(x, str) else [])
-all_genres = sorted(list(set([genre for sublist in genre_lists for genre in sublist])))
-
-st.write(f'<p style="text-align: center; font-family: Arial, sans-serif; font-size: 20px; color: white;">Количество фильмов \
-         для поиска {len(df)}</p>', unsafe_allow_html=True)
-
-st.header(':wrench: Панель инструментов')
-col1, col2, col3 = st.columns([1, 2, 1]) 
-with col1:
-    top_k = st.selectbox("Сколько фильмов?", options=[5, 10, 15, 20])
-with col2:
-    model_type = st.selectbox("Какой моделью пользуемся?\n ", options=['rubert-tiny2', 'msmarco-MiniLM-L-12-v3'])
-with col3:
-    genres_list = st.multiselect("Какого жанра?\n ", options=all_genres)
-
-if model_type == 'rubert-tiny2':
-    model = SentenceTransformer('cointegrated/rubert-tiny2')
-    emb = torch.load('resources/corpus_embeddings_rub.pth', map_location=torch.device('cpu'))
-else:
-    model = SentenceTransformer('msmarco-MiniLM-L-12-v3')
-    emb = torch.load('resources/corpus_embeddings_ms.pth', map_location=torch.device('cpu'))
-
-range_years = st.slider("В каком году вышел фильм?", min_value=df['year'].unique().min(), 
-                         max_value=df['year'].unique().max(), 
-                         value=(df['year'].unique().min(), df['year'].unique().max()))
-
-text = st.text_input('Что будем искать?')
-button = st.button('Начать поиск', type="primary")
-    
-if text and button:
-    if len(genres_list) == 0:
-        mask = get_mask_in_range(df=df, range_values=range_years)
-    else:
-        mask1 = find_rows_with_genres(df=df, genres_list=genres_list)
-        mask2 = get_mask_in_range(df=df, range_values=range_years)
-        mask = mask1 & mask2
-
-    emb = emb[mask]
-    df = df[mask]
-    hits = recommend(model, text, emb, top_k)
-    st.write(f'<p style="font-family: Arial, sans-serif; font-size: 24px; color: pink; font-weight: bold;"><strong>\
-        {len(hits[0])} лучших рекомендаций</strong></p>', unsafe_allow_html=True)
-    st.write('\n')
-
-    for i in range(top_k):
-        col3, col4 = st.columns([3, 4]) 
-        with col3:
-            try:
-                st.image(df['poster'][hits[0][i]['corpus_id']], width=300)
-            except:
-                st.image('https://cdnn11.img.sputnik.by/img/104126/36/1041263627_235:441:1472:1802_1920x0_80_0_0_fc2acc893b618b7c650d661fafe178b8.jpg', width=300)
-        with col4:
-            st.write(f"***Название:*** {df['title'][hits[0][i]['corpus_id']]}")
-            st.write(f"***Жанр:*** {(df['ganres'][hits[0][i]['corpus_id']])}")
-            st.write(f"***Описание:*** {df['description'][hits[0][i]['corpus_id']]}")
-            st.write(f"***Год:*** {df['year'][hits[0][i]['corpus_id']]}")
-            st.write(f"***Актерский состав:*** {df['cast'][hits[0][i]['corpus_id']]}")
-            st.write(f"***Косинусное сходство:*** {round(hits[0][i]['score'], 2)}")
-            st.write(f"***Ссылка на фильм : {df['url'][hits[0][i]['corpus_id']]}***")
-
-        st.markdown(
-        "<hr style='border: 2px solid #000; margin-top: 10px; margin-bottom: 10px;'>",
-        unsafe_allow_html=True
-        )