Update app.py

app.py

@@ -1,203 +1,203 @@
-import streamlit as st
-import requests
-import plotly.graph_objects as go
-from geopy.geocoders import Nominatim
-import pandas as pd
-from datetime import datetime
-import holidays
-import numpy as np
-from sklearn.preprocessing import MinMaxScaler
-import pickle
-import xgboost as xgb
-
-# Setting up the page configuration for Streamlit App
-st.set_page_config(
-    page_title="Taxi",    
-    # layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Load the XGBoost model
-#@st.cache_data()
-def get_model():
-    model = pickle.load(open("models/model_xgb.pkl", "rb"))
-    return model
-
-# Function to make prediction using the model and input data
-def make_prediction(data):
-    model = get_model()
-    best_features = ['vendor_id', 'passenger_count', 'pickup_longitude', 'pickup_latitude',
-       'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',
-       'pickup_hour', 'pickup_holiday', 'total_distance', 'total_travel_time',
-       'number_of_steps', 'haversine_distance', 'temperature',
-       'pickup_day_of_week_1', 'pickup_day_of_week_2', 'pickup_day_of_week_3',
-       'pickup_day_of_week_4', 'pickup_day_of_week_5', 'pickup_day_of_week_6',
-       'geo_cluster_1', 'geo_cluster_3', 'geo_cluster_5', 'geo_cluster_7',
-       'geo_cluster_9']
-    data_matrix = xgb.DMatrix(data, feature_names=best_features)
-    return model.predict(data_matrix)
-
-
-def get_coordinates(address):
-    # Создание экземпляра геокодера
-    geolocator = Nominatim(user_agent="my_app")
-
-    # Получение координат по адресу
-    location = geolocator.geocode(address)
-
-    # Вывод широты и долготы
-    return (location.longitude, location.latitude)
-
-
-def show_map(lon_from, lat_from, lon_to, lat_to):
-    # Создание карты
-    fig = go.Figure(go.Scattermapbox(
-        mode = "markers",        
-        marker = {'size': 15, 'color': 'red'}
-    ))
-
-    # Добавление флажков для точек
-    fig.add_trace(go.Scattermapbox(
-        mode = "markers",
-        lon = [lon_from, lon_to],
-        lat = [lat_from, lat_to],
-        marker = go.scattermapbox.Marker(
-            size=25,
-            color='red'              
-        )
-    ))
-
-    # Добавление линии между точками
-    fig.add_trace(go.Scattermapbox(
-        mode = "lines",
-        lon = [lon_from, lon_to],
-        lat = [lat_from, lat_to],
-        line = dict(width=2, color='green')
-    ))
-
-    # Настройка отображения карты
-    fig.update_layout(
-        mapbox = {
-            'style': "open-street-map",  # Стиль карты
-            'center': {'lon': (lon_from + lon_to) / 2, 'lat': (lat_from + lat_to) / 2},  # Центр карты
-            'zoom': 9,  # Уровень масштабирования карты            
-        },
-        showlegend = False,
-        height = 600,  # Изменение высоты карты
-        width = 1200  # Изменение ширины карты
-    )
-    
-    # Отображение карты
-    return fig
-
-
-# Get total distance
-def get_total_distance(start_longitude, start_latitude, end_longitude, end_latitude):
-    # Construct the URL for sending a request to the public OSRM server
-    url = f"http://router.project-osrm.org/route/v1/driving/{start_longitude},{start_latitude};{end_longitude},{end_latitude}?overview=false"
-
-    # Send a GET request to the OSRM server
-    response = requests.get(url)
-
-    # Process the response from the server
-    if response.status_code == 200:
-        data = response.json()
-        total_distance = data["routes"][0]["distance"]  # Total distance in meters
-        total_travel_time = data["routes"][0]["duration"]  # Total travel time in seconds
-        number_of_steps = len(data["routes"][0]["legs"][0]["steps"])  # Number of steps in the
-        return total_distance,  total_travel_time, number_of_steps
-
-
-# Get Harversine distance
-def get_haversine_distance(lat1, lng1, lat2, lng2):
-    # Convert angles to radians
-    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))
-    # Earth's radius in kilometers
-    EARTH_RADIUS = 6371
-    # Calculate the shortest distance h using the Haversine formula
-    lat_delta = lat2 - lat1
-    lng_delta = lng2 - lng1
-    d = np.sin(lat_delta * 0.5) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(lng_delta * 0.5) ** 2
-    h = 2 * EARTH_RADIUS * np.arcsin(np.sqrt(d))
-    return h
-
-
-# User input features
-def user_input_features(lon_from, lat_from, lon_to, lat_to, passenger_count, temperature):    
-    current_time = datetime.now()    
-    pickup_hour= current_time.hour
-    today = datetime.today()
-    pickup_holiday = 1 if today in holidays.USA() else 0
-    total_distance,  total_travel_time, number_of_steps = get_total_distance(lon_from, lat_from, lon_to, lat_to)
-    haversine_distance = get_haversine_distance(lat_from, lon_from, lat_to, lon_to)    
-    weekday_number = current_time.weekday()
-
-    data = {'vendor_id': 1,
-            'passenger_count': passenger_count,
-            'pickup_longitude': lon_from,            
-            'pickup_latitude': lat_from,
-            'dropoff_longitude': lon_to,
-            'dropoff_latitude': lat_to,
-            'store_and_fwd_flag': 0.0,
-            'pickup_hour': pickup_hour,
-            'pickup_holiday': pickup_holiday,
-            'total_distance': total_distance,
-            'total_travel_time': total_travel_time,
-            'number_of_steps': number_of_steps,
-            'haversine_distance': haversine_distance,
-            'temperature': temperature,
-            'pickup_day_of_week_1': 1 if weekday_number == 1 else 0,
-            'pickup_day_of_week_2': 1 if weekday_number == 2 else 0,
-            'pickup_day_of_week_3': 1 if weekday_number == 3 else 0,
-            'pickup_day_of_week_4': 1 if weekday_number == 4 else 0,
-            'pickup_day_of_week_5': 1 if weekday_number == 5 else 0,
-            'pickup_day_of_week_6': 1 if weekday_number == 6 else 0,
-            'geo_cluster_1':1,
-            'geo_cluster_3':0,
-            'geo_cluster_5':0,
-            'geo_cluster_7':0,
-            'geo_cluster_9':0
-            }
-    features = pd.DataFrame(data, index=[0])
-    return features
-
-
-# Scale the input data using a pre-trained MinMaxScaler
-def min_max_scaler(data):
-    scaler = pickle.load(open("models/min_max_scaler.pkl", "rb"))
-    data_scaled = scaler.transform(data)
-    return data_scaled
-
-# Main function
-def main(): 
-
-    if 'btn_predict' not in st.session_state:
-        st.session_state['btn_predict'] = False 
-    
-    # Sidebar
-    st.sidebar.markdown(''' # New York City Taxi Trip Duration''')
-    st.sidebar.image("img/taxi_img.png")
-    address_from = st.sidebar.text_input("Откуда:", value="New York, Liberty Island")
-    address_to = st.sidebar.text_input("Куда:", value="New York, 20 W 34th St")    
-    passenger_count = st.sidebar.slider("Количество пассажиров", 1, 4, 1)
-    temperature = st.sidebar.slider("Temperature (C)", -20, 40, 15)
-    st.session_state['btn_predict'] = st.sidebar.button('Start')
-
-    if st.session_state['btn_predict']:
-        lon_from, lat_from = get_coordinates(address_from)
-        lon_to, lat_to = get_coordinates(address_to)
-        st.plotly_chart(show_map(lon_from, lat_from, lon_to, lat_to))
-        user_data = user_input_features(lon_from, lat_from, lon_to, lat_to, passenger_count, temperature)
-        # st.write(user_data)
-        data_scaled = min_max_scaler(user_data)
-        trip_duration = np.exp(make_prediction(data_scaled)) - 1     
-        trip_duration = round(float(trip_duration) / 60)
-        st.markdown(f"""
-                        <div style='background-color: lightgreen; padding: 10px;'>
-                            <h2 style='color: black; text-align: center;'>Длительность поездки составит: {trip_duration} мин.</h2>
-                        </div>
-                    """, unsafe_allow_html=True)
-
-# Running the main function
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import requests
+import plotly.graph_objects as go
+from geopy.geocoders import Nominatim
+import pandas as pd
+from datetime import datetime
+import holidays
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+import pickle
+import xgboost as xgb
+
+# Setting up the page configuration for Streamlit App
+st.set_page_config(
+    page_title="Taxi",    
+    # layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Load the XGBoost model
+#@st.cache_data()
+def get_model():
+    model = pickle.load(open("models/model_xgb.pkl", "rb"))
+    return model
+
+# Function to make prediction using the model and input data
+def make_prediction(data):
+    model = get_model()
+    best_features = ['vendor_id', 'passenger_count', 'pickup_longitude', 'pickup_latitude',
+       'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',
+       'pickup_hour', 'pickup_holiday', 'total_distance', 'total_travel_time',
+       'number_of_steps', 'haversine_distance', 'temperature',
+       'pickup_day_of_week_1', 'pickup_day_of_week_2', 'pickup_day_of_week_3',
+       'pickup_day_of_week_4', 'pickup_day_of_week_5', 'pickup_day_of_week_6',
+       'geo_cluster_1', 'geo_cluster_3', 'geo_cluster_5', 'geo_cluster_7',
+       'geo_cluster_9']
+    data_matrix = xgb.DMatrix(data, feature_names=best_features)
+    return model.predict(data_matrix)
+
+
+def get_coordinates(address):
+    # Создание экземпляра геокодера
+    geolocator = Nominatim(user_agent="my_app")
+
+    # Получение координат по адресу
+    location = geolocator.geocode(address)
+
+    # Вывод широты и долготы
+    return (location.longitude, location.latitude)
+
+
+def show_map(lon_from, lat_from, lon_to, lat_to):
+    # Creating a map
+    fig = go.Figure(go.Scattermapbox(
+        mode = "markers",        
+        marker = {'size': 15, 'color': 'red'}
+    ))
+
+    # Adding markers
+    fig.add_trace(go.Scattermapbox(
+        mode = "markers",
+        lon = [lon_from, lon_to],
+        lat = [lat_from, lat_to],
+        marker = go.scattermapbox.Marker(
+            size=25,
+            color='red'              
+        )
+    ))
+
+    # Добавление линии между точками
+    fig.add_trace(go.Scattermapbox(
+        mode = "lines",
+        lon = [lon_from, lon_to],
+        lat = [lat_from, lat_to],
+        line = dict(width=2, color='green')
+    ))
+
+    # Настройка отображения карты
+    fig.update_layout(
+        mapbox = {
+            'style': "open-street-map",  # Стиль карты
+            'center': {'lon': (lon_from + lon_to) / 2, 'lat': (lat_from + lat_to) / 2},  # Центр карты
+            'zoom': 9,  # Уровень масштабирования карты            
+        },
+        showlegend = False,
+        height = 600,  # Изменение высоты карты
+        width = 1200  # Изменение ширины карты
+    )
+    
+    # Отображение карты
+    return fig
+
+
+# Get total distance
+def get_total_distance(start_longitude, start_latitude, end_longitude, end_latitude):
+    # Construct the URL for sending a request to the public OSRM server
+    url = f"http://router.project-osrm.org/route/v1/driving/{start_longitude},{start_latitude};{end_longitude},{end_latitude}?overview=false"
+
+    # Send a GET request to the OSRM server
+    response = requests.get(url)
+
+    # Process the response from the server
+    if response.status_code == 200:
+        data = response.json()
+        total_distance = data["routes"][0]["distance"]  # Total distance in meters
+        total_travel_time = data["routes"][0]["duration"]  # Total travel time in seconds
+        number_of_steps = len(data["routes"][0]["legs"][0]["steps"])  # Number of steps in the
+        return total_distance,  total_travel_time, number_of_steps
+
+
+# Get Harversine distance
+def get_haversine_distance(lat1, lng1, lat2, lng2):
+    # Convert angles to radians
+    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))
+    # Earth's radius in kilometers
+    EARTH_RADIUS = 6371
+    # Calculate the shortest distance h using the Haversine formula
+    lat_delta = lat2 - lat1
+    lng_delta = lng2 - lng1
+    d = np.sin(lat_delta * 0.5) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(lng_delta * 0.5) ** 2
+    h = 2 * EARTH_RADIUS * np.arcsin(np.sqrt(d))
+    return h
+
+
+# User input features
+def user_input_features(lon_from, lat_from, lon_to, lat_to, passenger_count, temperature):    
+    current_time = datetime.now()    
+    pickup_hour= current_time.hour
+    today = datetime.today()
+    pickup_holiday = 1 if today in holidays.USA() else 0
+    total_distance,  total_travel_time, number_of_steps = get_total_distance(lon_from, lat_from, lon_to, lat_to)
+    haversine_distance = get_haversine_distance(lat_from, lon_from, lat_to, lon_to)    
+    weekday_number = current_time.weekday()
+
+    data = {'vendor_id': 1,
+            'passenger_count': passenger_count,
+            'pickup_longitude': lon_from,            
+            'pickup_latitude': lat_from,
+            'dropoff_longitude': lon_to,
+            'dropoff_latitude': lat_to,
+            'store_and_fwd_flag': 0.0,
+            'pickup_hour': pickup_hour,
+            'pickup_holiday': pickup_holiday,
+            'total_distance': total_distance,
+            'total_travel_time': total_travel_time,
+            'number_of_steps': number_of_steps,
+            'haversine_distance': haversine_distance,
+            'temperature': temperature,
+            'pickup_day_of_week_1': 1 if weekday_number == 1 else 0,
+            'pickup_day_of_week_2': 1 if weekday_number == 2 else 0,
+            'pickup_day_of_week_3': 1 if weekday_number == 3 else 0,
+            'pickup_day_of_week_4': 1 if weekday_number == 4 else 0,
+            'pickup_day_of_week_5': 1 if weekday_number == 5 else 0,
+            'pickup_day_of_week_6': 1 if weekday_number == 6 else 0,
+            'geo_cluster_1':1,
+            'geo_cluster_3':0,
+            'geo_cluster_5':0,
+            'geo_cluster_7':0,
+            'geo_cluster_9':0
+            }
+    features = pd.DataFrame(data, index=[0])
+    return features
+
+
+# Scale the input data using a pre-trained MinMaxScaler
+def min_max_scaler(data):
+    scaler = pickle.load(open("models/min_max_scaler.pkl", "rb"))
+    data_scaled = scaler.transform(data)
+    return data_scaled
+
+# Main function
+def main(): 
+
+    if 'btn_predict' not in st.session_state:
+        st.session_state['btn_predict'] = False 
+    
+    # Sidebar
+    st.sidebar.markdown(''' # New York City Taxi Trip Duration''')
+    st.sidebar.image("img/taxi_img.png")
+    address_from = st.sidebar.text_input("Откуда:", value="New York, Liberty Island")
+    address_to = st.sidebar.text_input("Куда:", value="New York, 20 W 34th St")    
+    passenger_count = st.sidebar.slider("Количество пассажиров", 1, 4, 1)
+    temperature = st.sidebar.slider("Temperature (C)", -20, 40, 15)
+    st.session_state['btn_predict'] = st.sidebar.button('Start')
+
+    if st.session_state['btn_predict']:
+        lon_from, lat_from = get_coordinates(address_from)
+        lon_to, lat_to = get_coordinates(address_to)
+        st.plotly_chart(show_map(lon_from, lat_from, lon_to, lat_to))
+        user_data = user_input_features(lon_from, lat_from, lon_to, lat_to, passenger_count, temperature)
+        # st.write(user_data)
+        data_scaled = min_max_scaler(user_data)
+        trip_duration = np.exp(make_prediction(data_scaled)) - 1     
+        trip_duration = round(float(trip_duration) / 60)
+        st.markdown(f"""
+                        <div style='background-color: lightgreen; padding: 10px;'>
+                            <h2 style='color: black; text-align: center;'>Длительность поездки составит: {trip_duration} мин.</h2>
+                        </div>
+                    """, unsafe_allow_html=True)
+
+# Running the main function
+if __name__ == "__main__":
+    main()