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Update app.py
Browse files
app.py
CHANGED
@@ -1,203 +1,203 @@
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import streamlit as st
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import requests
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import plotly.graph_objects as go
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from geopy.geocoders import Nominatim
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import pandas as pd
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from datetime import datetime
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import holidays
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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import pickle
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import xgboost as xgb
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# Setting up the page configuration for Streamlit App
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st.set_page_config(
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page_title="Taxi",
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# layout="wide",
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initial_sidebar_state="expanded"
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)
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# Load the XGBoost model
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#@st.cache_data()
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def get_model():
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model = pickle.load(open("models/model_xgb.pkl", "rb"))
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return model
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# Function to make prediction using the model and input data
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def make_prediction(data):
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model = get_model()
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best_features = ['vendor_id', 'passenger_count', 'pickup_longitude', 'pickup_latitude',
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'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',
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'pickup_hour', 'pickup_holiday', 'total_distance', 'total_travel_time',
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'number_of_steps', 'haversine_distance', 'temperature',
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'pickup_day_of_week_1', 'pickup_day_of_week_2', 'pickup_day_of_week_3',
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'pickup_day_of_week_4', 'pickup_day_of_week_5', 'pickup_day_of_week_6',
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'geo_cluster_1', 'geo_cluster_3', 'geo_cluster_5', 'geo_cluster_7',
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'geo_cluster_9']
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data_matrix = xgb.DMatrix(data, feature_names=best_features)
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return model.predict(data_matrix)
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def get_coordinates(address):
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# Создание экземпляра геокодера
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geolocator = Nominatim(user_agent="my_app")
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# Получение координат по адресу
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location = geolocator.geocode(address)
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# Вывод широты и долготы
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return (location.longitude, location.latitude)
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def show_map(lon_from, lat_from, lon_to, lat_to):
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#
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fig = go.Figure(go.Scattermapbox(
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mode = "markers",
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marker = {'size': 15, 'color': 'red'}
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))
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#
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fig.add_trace(go.Scattermapbox(
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mode = "markers",
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lon = [lon_from, lon_to],
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lat = [lat_from, lat_to],
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marker = go.scattermapbox.Marker(
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size=25,
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color='red'
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)
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))
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# Добавление линии между точками
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fig.add_trace(go.Scattermapbox(
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mode = "lines",
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lon = [lon_from, lon_to],
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lat = [lat_from, lat_to],
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line = dict(width=2, color='green')
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))
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# Настройка отображения карты
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fig.update_layout(
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mapbox = {
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'style': "open-street-map", # Стиль карты
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'center': {'lon': (lon_from + lon_to) / 2, 'lat': (lat_from + lat_to) / 2}, # Центр карты
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'zoom': 9, # Уровень масштабирования карты
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},
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showlegend = False,
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height = 600, # Изменение высоты карты
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width = 1200 # Изменение ширины карты
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)
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# Отображение карты
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return fig
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# Get total distance
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def get_total_distance(start_longitude, start_latitude, end_longitude, end_latitude):
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# Construct the URL for sending a request to the public OSRM server
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url = f"http://router.project-osrm.org/route/v1/driving/{start_longitude},{start_latitude};{end_longitude},{end_latitude}?overview=false"
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# Send a GET request to the OSRM server
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response = requests.get(url)
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# Process the response from the server
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if response.status_code == 200:
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data = response.json()
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total_distance = data["routes"][0]["distance"] # Total distance in meters
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total_travel_time = data["routes"][0]["duration"] # Total travel time in seconds
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number_of_steps = len(data["routes"][0]["legs"][0]["steps"]) # Number of steps in the
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return total_distance, total_travel_time, number_of_steps
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# Get Harversine distance
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def get_haversine_distance(lat1, lng1, lat2, lng2):
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# Convert angles to radians
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lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))
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# Earth's radius in kilometers
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EARTH_RADIUS = 6371
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# Calculate the shortest distance h using the Haversine formula
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lat_delta = lat2 - lat1
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lng_delta = lng2 - lng1
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d = np.sin(lat_delta * 0.5) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(lng_delta * 0.5) ** 2
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h = 2 * EARTH_RADIUS * np.arcsin(np.sqrt(d))
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return h
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# User input features
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def user_input_features(lon_from, lat_from, lon_to, lat_to, passenger_count, temperature):
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current_time = datetime.now()
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pickup_hour= current_time.hour
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today = datetime.today()
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pickup_holiday = 1 if today in holidays.USA() else 0
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total_distance, total_travel_time, number_of_steps = get_total_distance(lon_from, lat_from, lon_to, lat_to)
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haversine_distance = get_haversine_distance(lat_from, lon_from, lat_to, lon_to)
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weekday_number = current_time.weekday()
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data = {'vendor_id': 1,
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'passenger_count': passenger_count,
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'pickup_longitude': lon_from,
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'pickup_latitude': lat_from,
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'dropoff_longitude': lon_to,
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'dropoff_latitude': lat_to,
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'store_and_fwd_flag': 0.0,
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'pickup_hour': pickup_hour,
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'pickup_holiday': pickup_holiday,
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'total_distance': total_distance,
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'total_travel_time': total_travel_time,
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'number_of_steps': number_of_steps,
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'haversine_distance': haversine_distance,
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'temperature': temperature,
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'pickup_day_of_week_1': 1 if weekday_number == 1 else 0,
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'pickup_day_of_week_2': 1 if weekday_number == 2 else 0,
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'pickup_day_of_week_3': 1 if weekday_number == 3 else 0,
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'pickup_day_of_week_4': 1 if weekday_number == 4 else 0,
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'pickup_day_of_week_5': 1 if weekday_number == 5 else 0,
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'pickup_day_of_week_6': 1 if weekday_number == 6 else 0,
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'geo_cluster_1':1,
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'geo_cluster_3':0,
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'geo_cluster_5':0,
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'geo_cluster_7':0,
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'geo_cluster_9':0
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}
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features = pd.DataFrame(data, index=[0])
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return features
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# Scale the input data using a pre-trained MinMaxScaler
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def min_max_scaler(data):
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scaler = pickle.load(open("models/min_max_scaler.pkl", "rb"))
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data_scaled = scaler.transform(data)
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return data_scaled
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# Main function
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def main():
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if 'btn_predict' not in st.session_state:
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st.session_state['btn_predict'] = False
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# Sidebar
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st.sidebar.markdown(''' # New York City Taxi Trip Duration''')
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st.sidebar.image("img/taxi_img.png")
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address_from = st.sidebar.text_input("Откуда:", value="New York, Liberty Island")
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address_to = st.sidebar.text_input("Куда:", value="New York, 20 W 34th St")
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passenger_count = st.sidebar.slider("Количество пассажиров", 1, 4, 1)
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temperature = st.sidebar.slider("Temperature (C)", -20, 40, 15)
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st.session_state['btn_predict'] = st.sidebar.button('Start')
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if st.session_state['btn_predict']:
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lon_from, lat_from = get_coordinates(address_from)
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lon_to, lat_to = get_coordinates(address_to)
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st.plotly_chart(show_map(lon_from, lat_from, lon_to, lat_to))
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user_data = user_input_features(lon_from, lat_from, lon_to, lat_to, passenger_count, temperature)
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# st.write(user_data)
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data_scaled = min_max_scaler(user_data)
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trip_duration = np.exp(make_prediction(data_scaled)) - 1
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trip_duration = round(float(trip_duration) / 60)
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st.markdown(f"""
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<div style='background-color: lightgreen; padding: 10px;'>
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<h2 style='color: black; text-align: center;'>Длительность поездки составит: {trip_duration} мин.</h2>
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</div>
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""", unsafe_allow_html=True)
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# Running the main function
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if __name__ == "__main__":
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main()
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import streamlit as st
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import requests
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import plotly.graph_objects as go
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from geopy.geocoders import Nominatim
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import pandas as pd
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from datetime import datetime
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import holidays
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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import pickle
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import xgboost as xgb
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# Setting up the page configuration for Streamlit App
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st.set_page_config(
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page_title="Taxi",
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# layout="wide",
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initial_sidebar_state="expanded"
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)
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# Load the XGBoost model
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#@st.cache_data()
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def get_model():
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model = pickle.load(open("models/model_xgb.pkl", "rb"))
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return model
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# Function to make prediction using the model and input data
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def make_prediction(data):
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model = get_model()
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best_features = ['vendor_id', 'passenger_count', 'pickup_longitude', 'pickup_latitude',
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'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',
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'pickup_hour', 'pickup_holiday', 'total_distance', 'total_travel_time',
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'number_of_steps', 'haversine_distance', 'temperature',
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'pickup_day_of_week_1', 'pickup_day_of_week_2', 'pickup_day_of_week_3',
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'pickup_day_of_week_4', 'pickup_day_of_week_5', 'pickup_day_of_week_6',
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'geo_cluster_1', 'geo_cluster_3', 'geo_cluster_5', 'geo_cluster_7',
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'geo_cluster_9']
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data_matrix = xgb.DMatrix(data, feature_names=best_features)
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return model.predict(data_matrix)
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def get_coordinates(address):
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# Создание экземпляра геокодера
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geolocator = Nominatim(user_agent="my_app")
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# Получение координат по адресу
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location = geolocator.geocode(address)
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# Вывод широты и долготы
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return (location.longitude, location.latitude)
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def show_map(lon_from, lat_from, lon_to, lat_to):
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# Creating a map
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fig = go.Figure(go.Scattermapbox(
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mode = "markers",
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marker = {'size': 15, 'color': 'red'}
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))
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# Adding markers
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fig.add_trace(go.Scattermapbox(
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mode = "markers",
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lon = [lon_from, lon_to],
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lat = [lat_from, lat_to],
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marker = go.scattermapbox.Marker(
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size=25,
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color='red'
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)
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))
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# Добавление линии между точками
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fig.add_trace(go.Scattermapbox(
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mode = "lines",
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lon = [lon_from, lon_to],
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lat = [lat_from, lat_to],
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line = dict(width=2, color='green')
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))
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# Настройка отображения карты
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fig.update_layout(
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mapbox = {
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'style': "open-street-map", # Стиль карты
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'center': {'lon': (lon_from + lon_to) / 2, 'lat': (lat_from + lat_to) / 2}, # Центр карты
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'zoom': 9, # Уровень масштабирования карты
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},
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showlegend = False,
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height = 600, # Изменение высоты карты
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width = 1200 # Изменение ширины карты
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)
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# Отображение карты
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return fig
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# Get total distance
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def get_total_distance(start_longitude, start_latitude, end_longitude, end_latitude):
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# Construct the URL for sending a request to the public OSRM server
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url = f"http://router.project-osrm.org/route/v1/driving/{start_longitude},{start_latitude};{end_longitude},{end_latitude}?overview=false"
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# Send a GET request to the OSRM server
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response = requests.get(url)
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# Process the response from the server
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if response.status_code == 200:
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data = response.json()
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total_distance = data["routes"][0]["distance"] # Total distance in meters
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total_travel_time = data["routes"][0]["duration"] # Total travel time in seconds
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number_of_steps = len(data["routes"][0]["legs"][0]["steps"]) # Number of steps in the
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return total_distance, total_travel_time, number_of_steps
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# Get Harversine distance
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def get_haversine_distance(lat1, lng1, lat2, lng2):
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# Convert angles to radians
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lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))
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# Earth's radius in kilometers
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EARTH_RADIUS = 6371
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# Calculate the shortest distance h using the Haversine formula
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lat_delta = lat2 - lat1
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lng_delta = lng2 - lng1
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d = np.sin(lat_delta * 0.5) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(lng_delta * 0.5) ** 2
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h = 2 * EARTH_RADIUS * np.arcsin(np.sqrt(d))
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return h
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# User input features
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def user_input_features(lon_from, lat_from, lon_to, lat_to, passenger_count, temperature):
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current_time = datetime.now()
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pickup_hour= current_time.hour
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today = datetime.today()
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pickup_holiday = 1 if today in holidays.USA() else 0
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total_distance, total_travel_time, number_of_steps = get_total_distance(lon_from, lat_from, lon_to, lat_to)
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haversine_distance = get_haversine_distance(lat_from, lon_from, lat_to, lon_to)
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weekday_number = current_time.weekday()
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data = {'vendor_id': 1,
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'passenger_count': passenger_count,
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'pickup_longitude': lon_from,
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'pickup_latitude': lat_from,
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'dropoff_longitude': lon_to,
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'dropoff_latitude': lat_to,
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'store_and_fwd_flag': 0.0,
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'pickup_hour': pickup_hour,
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'pickup_holiday': pickup_holiday,
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'total_distance': total_distance,
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'total_travel_time': total_travel_time,
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'number_of_steps': number_of_steps,
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'haversine_distance': haversine_distance,
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'temperature': temperature,
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'pickup_day_of_week_1': 1 if weekday_number == 1 else 0,
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'pickup_day_of_week_2': 1 if weekday_number == 2 else 0,
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'pickup_day_of_week_3': 1 if weekday_number == 3 else 0,
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'pickup_day_of_week_4': 1 if weekday_number == 4 else 0,
|
153 |
+
'pickup_day_of_week_5': 1 if weekday_number == 5 else 0,
|
154 |
+
'pickup_day_of_week_6': 1 if weekday_number == 6 else 0,
|
155 |
+
'geo_cluster_1':1,
|
156 |
+
'geo_cluster_3':0,
|
157 |
+
'geo_cluster_5':0,
|
158 |
+
'geo_cluster_7':0,
|
159 |
+
'geo_cluster_9':0
|
160 |
+
}
|
161 |
+
features = pd.DataFrame(data, index=[0])
|
162 |
+
return features
|
163 |
+
|
164 |
+
|
165 |
+
# Scale the input data using a pre-trained MinMaxScaler
|
166 |
+
def min_max_scaler(data):
|
167 |
+
scaler = pickle.load(open("models/min_max_scaler.pkl", "rb"))
|
168 |
+
data_scaled = scaler.transform(data)
|
169 |
+
return data_scaled
|
170 |
+
|
171 |
+
# Main function
|
172 |
+
def main():
|
173 |
+
|
174 |
+
if 'btn_predict' not in st.session_state:
|
175 |
+
st.session_state['btn_predict'] = False
|
176 |
+
|
177 |
+
# Sidebar
|
178 |
+
st.sidebar.markdown(''' # New York City Taxi Trip Duration''')
|
179 |
+
st.sidebar.image("img/taxi_img.png")
|
180 |
+
address_from = st.sidebar.text_input("Откуда:", value="New York, Liberty Island")
|
181 |
+
address_to = st.sidebar.text_input("Куда:", value="New York, 20 W 34th St")
|
182 |
+
passenger_count = st.sidebar.slider("Количество пассажиров", 1, 4, 1)
|
183 |
+
temperature = st.sidebar.slider("Temperature (C)", -20, 40, 15)
|
184 |
+
st.session_state['btn_predict'] = st.sidebar.button('Start')
|
185 |
+
|
186 |
+
if st.session_state['btn_predict']:
|
187 |
+
lon_from, lat_from = get_coordinates(address_from)
|
188 |
+
lon_to, lat_to = get_coordinates(address_to)
|
189 |
+
st.plotly_chart(show_map(lon_from, lat_from, lon_to, lat_to))
|
190 |
+
user_data = user_input_features(lon_from, lat_from, lon_to, lat_to, passenger_count, temperature)
|
191 |
+
# st.write(user_data)
|
192 |
+
data_scaled = min_max_scaler(user_data)
|
193 |
+
trip_duration = np.exp(make_prediction(data_scaled)) - 1
|
194 |
+
trip_duration = round(float(trip_duration) / 60)
|
195 |
+
st.markdown(f"""
|
196 |
+
<div style='background-color: lightgreen; padding: 10px;'>
|
197 |
+
<h2 style='color: black; text-align: center;'>Длительность поездки составит: {trip_duration} мин.</h2>
|
198 |
+
</div>
|
199 |
+
""", unsafe_allow_html=True)
|
200 |
+
|
201 |
+
# Running the main function
|
202 |
+
if __name__ == "__main__":
|
203 |
+
main()
|