test

app.py

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+import streamlit as st
+import pandas as pd
+import torch
+from geopy.geocoders import ArcGIS
+import folium
+from streamlit_folium import folium_static
+from transformers import AutoTokenizer, AutoModel
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+session_state = st.session_state
+if not hasattr(session_state, 'recommended_countries'):
+    session_state.recommended_countries = []
+
+st.set_page_config(layout="wide")
+
+@st.cache_resource()
+def load_model():
+    model = AutoModel.from_pretrained("cointegrated/rubert-tiny2")
+    tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2")
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+@st.cache_data()
+def load_data():
+    df = pd.read_csv('data/countries.csv')
+    return df
+
+df = load_data()
+
+def embed_bert_cls(text, model, tokenizer):
+    t = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
+    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()
+
+def get_coordinates(country_name):
+    geolocator = ArcGIS()
+    location = geolocator.geocode(country_name)
+    if location:
+        return location.latitude, location.longitude
+    else:
+        return None
+    
+st.markdown("<h1 style='text-align: center;'>блаблабла</h1>", unsafe_allow_html=True)
+    
+
+st.markdown("<style> input {font-size: 25px !important;}</style>", unsafe_allow_html=True)
+    
+first_input = st.text_input('Введите предпочтения по климату и типу местности')
+second_input = st.text_input('Введите предпочтения по еде')
+third_input = st.text_input('Введите предпочтения по активностям')
+option = st.selectbox(
+    'Виза',
+    ('Да', 'Нет')
+)
+sec_option = st.selectbox(
+    'Местоположение',
+    ('Африка','Азия','Европа','Океания','Северная Америка','Южная Америка')
+)
+third_option = st.slider('Выберите значение, характеризующее оценку безопасности страны', 1.0, 3.6, 1.7, 0.1)
+
+col3,col4 = st.columns([1,1])
+col5,col6, col7 = st.columns([5,5,5])
+
+with col3:
+    button_test = st.button('Получить рекомендацию')
+
+if button_test and first_input and second_input and third_input :
+    filtered_df = df[df['visa'] == option]
+    filtered_df = filtered_df[filtered_df['location'] == sec_option]
+    filtered_df = filtered_df[filtered_df['peace_index'] <= third_option]
+
+    decode_first = embed_bert_cls(first_input, model, tokenizer)
+    decode_second = embed_bert_cls(second_input, model, tokenizer)
+    decode_third = embed_bert_cls(third_input, model, tokenizer)
+    try:
+        review_embeddings = np.vstack(filtered_df['embeddings_review'].apply(lambda x: np.fromstring(x[1:-1], sep=' ')))
+        kitchen_embeddings = np.vstack(filtered_df['embeddings_kitchen'].apply(lambda x: np.fromstring(x[1:-1], sep=' ')))
+        activity_embeddings = np.vstack(filtered_df['embeddings_activity'].apply(lambda x: np.fromstring(x[1:-1], sep=' ')))
+        similarity_col1 = cosine_similarity(decode_first.reshape(1, -1), review_embeddings)
+        similarity_col2 = cosine_similarity(decode_second.reshape(1, -1), kitchen_embeddings)
+        similarity_col3 = cosine_similarity(decode_third.reshape(1, -1), activity_embeddings)
+        mean_similarity = np.mean([similarity_col1, similarity_col2, similarity_col3], axis=0)
+        max_similarity_row = np.argmax(mean_similarity)
+        max_similarity_value = np.max(mean_similarity)
+
+        recommended_country = filtered_df.iloc[max_similarity_row]['country']
+        recommended_review = filtered_df.iloc[max_similarity_row]['short_review']
+        recommended_flag = filtered_df.iloc[max_similarity_row]['flag']
+        recommended_photo = filtered_df.iloc[max_similarity_row]['country_photo']
+        similarity_values = [similarity_col1[:, max_similarity_row],
+                            similarity_col2[:, max_similarity_row],
+                            similarity_col3[:, max_similarity_row]]
+
+        session_state.recommended_countries.append(recommended_country)
+        with col5:
+            st.image(recommended_photo, width=795, use_column_width=False)
+        with col6:
+            st.image(recommended_flag, width=200, use_column_width=False)
+            st.markdown(f"<p style='font-size: 25px;'>Рекомендуемая страна: {recommended_country}</p>", unsafe_allow_html=True)
+            st.markdown(f"<p style='font-size: 25px;'> {recommended_review}</p>", unsafe_allow_html=True)
+            scale_html = f'<div style="width: 300px; height: 30px;">'
+            scale_html += f'<progress value="{max_similarity_value}" max="1" style="width: 100%; height: 100%;"></progress>'
+            scale_html += f'<div style="position: relative; top: -22px; text-align: center;">'
+            scale_html += f'<span style="position: absolute; left: 0;">0</span>'
+            scale_html += f'<span style="position: absolute; right: 0;">1</span>'
+            scale_html += f'</div></div>'
+            st.markdown(f"<p style='font-size: 25px;'>Оценка близости вашего запроса и страны</p>", unsafe_allow_html=True)
+            st.markdown(scale_html, unsafe_allow_html=True)
+        with col7:
+            # st.write('Местоположение на карте мира')
+            coordinates = get_coordinates(recommended_country)
+            if coordinates:
+                my_map = folium.Map(location=coordinates, zoom_start=5, tiles="Cartodb Positron",max_bounds=True, min_lon=-180, max_lon=180, min_lat=-90, max_lat=90,min_zoom=2,max_zoom=15)
+
+                folium.Marker(location=coordinates, popup=recommended_country).add_to(my_map)
+                folium_static(my_map)
+            else:
+                st.write(f"Координаты для страны {recommended_country} не найдены.")
+    except ValueError as e:
+        st.write('Нет такой страны')
+if session_state.recommended_countries:
+    with col4:
+       
+        next_button = st.button("Следующая рекомендация")
+    if next_button:
+        filtered_df = df[df['visa'] == option]
+        filtered_df = filtered_df[filtered_df['location'] == sec_option]
+        filtered_df = filtered_df[~filtered_df['country'].isin(session_state.recommended_countries)]
+        decode_first = embed_bert_cls(first_input, model, tokenizer)
+        decode_second = embed_bert_cls(second_input, model, tokenizer)
+        decode_third = embed_bert_cls(third_input, model, tokenizer)
+
+        review_embeddings = np.vstack(filtered_df['embeddings_review'].apply(lambda x: np.fromstring(x[1:-1], sep=' '))) if not filtered_df.empty else None
+        kitchen_embeddings = np.vstack(filtered_df['embeddings_kitchen'].apply(lambda x: np.fromstring(x[1:-1], sep=' '))) if not filtered_df.empty else None
+        activity_embeddings = np.vstack(filtered_df['embeddings_activity'].apply(lambda x: np.fromstring(x[1:-1], sep=' '))) if not filtered_df.empty else None
+
+        if review_embeddings is not None and kitchen_embeddings is not None and activity_embeddings is not None:
+            similarity_col1 = cosine_similarity(decode_first.reshape(1, -1), review_embeddings)
+            similarity_col2 = cosine_similarity(decode_second.reshape(1, -1), kitchen_embeddings)
+            similarity_col3 = cosine_similarity(decode_third.reshape(1, -1), activity_embeddings)
+            mean_similarity = np.mean([similarity_col1, similarity_col2, similarity_col3], axis=0)
+            max_similarity_row = np.argmax(mean_similarity)
+            max_similarity_value = np.max(mean_similarity)
+
+            if max_similarity_value > 0:
+            
+                recommended_country = filtered_df.iloc[max_similarity_row]['country']
+                recommended_review = filtered_df.iloc[max_similarity_row]['short_review']
+                recommended_flag = filtered_df.iloc[max_similarity_row]['flag']
+                recommended_photo = filtered_df.iloc[max_similarity_row]['country_photo']
+                similarity_values = [similarity_col1[:, max_similarity_row],
+                                      similarity_col2[:, max_similarity_row],
+                                      similarity_col3[:, max_similarity_row]]
+
+            
+                session_state.recommended_countries.append(recommended_country)
+                with col5:
+                    st.image(recommended_photo, width=795, use_column_width=False)
+                with col6:
+                    st.image(recommended_flag, width=200, use_column_width=False)
+                    st.markdown(f"<p style='font-size: 25px;'>Рекомендуемая страна: {recommended_country}</p>", unsafe_allow_html=True)
+                    st.markdown(f"<p style='font-size: 25px;'> {recommended_review}</p>", unsafe_allow_html=True)
+                    scale_html = f'<div style="width: 300px; height: 30px;">'
+                    scale_html += f'<progress value="{max_similarity_value}" max="1" style="width: 100%; height: 100%;"></progress>'
+                    scale_html += f'<div style="position: relative; top: -22px; text-align: center;">'
+                    scale_html += f'<span style="position: absolute; left: 0;">0</span>'
+                    scale_html += f'<span style="position: absolute; right: 0;">1</span>'
+                    scale_html += f'</div></div>'
+                    st.markdown(f"<p style='font-size: 25px;'>Оценка близости вашего запроса и страны</p>", unsafe_allow_html=True)
+                    st.markdown(scale_html, unsafe_allow_html=True)
+                with col7:
+                    # st.write('Местоположение на карте мира')
+                    coordinates = get_coordinates(recommended_country)
+                    if coordinates:
+                        my_map = folium.Map(location=coordinates, zoom_start=5, tiles="Cartodb Positron",
+                                            max_bounds=True,
+                                            min_lon=-180, max_lon=180, min_lat=-90, max_lat=90, min_zoom=2,
+                                            max_zoom=15)
+
+                        folium.Marker(location=coordinates, popup=recommended_country).add_to(my_map)
+                        folium_static(my_map)
+                    else:
+                        st.write(f"Координаты для страны {recommended_country} не найдены.")
+        else:
+            st.write("Больше рекомендаций нет.")