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Taxi_Trip_Duration.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f926d184068226ee17d20dc0bc381de5fa2937717b78e88c290c7d4a38f4c447
+size 300120

app.py

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+import folium as fl
+from streamlit_folium import st_folium
+import streamlit as st
+import googlemaps
+import pandas as pd
+from tensorflow.keras.models import load_model
+import numpy as np
+import pickle
+
+st.title('Taxi Trip Duration Prediction 🚖')
+
+# Load trained model and preprocessor
+model = load_model('Taxi_Trip_Duration.h5', compile=False)
+with open('preprocessor.pkl', 'rb') as f:
+    preprocessor = pickle.load(f)
+
+# Google Maps API client
+gmaps_api_key = 'AIzaSyCmyJwPBalqt1djDKK09YY2iYL7_cVA7e8' 
+gmaps = googlemaps.Client(key=gmaps_api_key)
+
+def calculate_distance(pickup_lat, pickup_lng, dropoff_lat, dropoff_lng):
+    directions_result = gmaps.directions((pickup_lat, pickup_lng), (dropoff_lat, dropoff_lng), mode="driving")
+    distance = directions_result[0]['legs'][0]['distance']['value'] / 1000.0  # Distance in kilometers
+    return distance
+
+def predict_duration(features):
+    predicted_duration = model.predict(features)[0][0]
+    return predicted_duration
+
+# Initialize session state variables
+if 'pickup_location' not in st.session_state:
+    st.session_state.pickup_location = None
+if 'dropoff_location' not in st.session_state:
+    st.session_state.dropoff_location = None
+if 'last_clicked' not in st.session_state:
+    st.session_state.last_clicked = None
+if 'step' not in st.session_state:
+    st.session_state.step = 0
+if 'distance' not in st.session_state:
+    st.session_state.distance = None
+if 'predicted_duration' not in st.session_state:
+    st.session_state.predicted_duration = None
+
+# Function to save location based on map click
+def save_location(location_type):
+    if st.session_state[location_type] is None and st.session_state.last_clicked:
+        st.session_state[location_type] = (st.session_state.last_clicked['lat'], st.session_state.last_clicked['lng'])
+        st.session_state.last_clicked = None
+        st.session_state.step += 1
+        st.experimental_rerun()  # Trigger a rerun to update the state and UI
+
+# Step 0: Select pickup location on the map
+if st.session_state.step == 0:
+    st.write('Please select the pickup location on the map 🚖')
+    starting_point = (37.0, 35.3213)  # Adana coordinates
+    m = fl.Map(location=starting_point, zoom_start=12)
+    m.add_child(fl.LatLngPopup())
+    map_data = st_folium(m, height=500)
+    if map_data and 'last_clicked' in map_data and map_data['last_clicked']:
+        st.session_state.last_clicked = map_data['last_clicked']
+        if st.button("Save Pickup Location"):
+            save_location('pickup_location')
+
+# Step 1: Select dropoff location on the map
+if st.session_state.step == 1 and st.session_state.pickup_location is not None:
+    st.write('Pickup location selected. Now select the dropoff location 📍')
+    m = fl.Map(location=st.session_state.pickup_location, zoom_start=12)
+    fl.Marker(st.session_state.pickup_location, popup="Pickup Location").add_to(m)
+    m.add_child(fl.LatLngPopup())
+    map_data = st_folium(m, height=500)
+    if map_data and 'last_clicked' in map_data and map_data['last_clicked']:
+        st.session_state.last_clicked = map_data['last_clicked']
+        if st.button("Save Dropoff Location"):
+            save_location('dropoff_location')
+
+# Step 2: Enter additional trip details and display prediction
+if st.session_state.step == 2 and st.session_state.pickup_location is not None and st.session_state.dropoff_location is not None:
+    st.write('Dropoff location selected. Please enter additional trip details.')
+
+    passenger_count = st.slider('Passenger Count', min_value=1, max_value=6, value=1)
+    hour = st.slider('Hour of Day', min_value=0, max_value=23, value=12)
+    day = st.slider('Day of Month', min_value=1, max_value=31, value=15)
+    weekday = st.selectbox('Weekday', ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])
+
+    weekday_dict = {'Monday': 0, 'Tuesday': 1, 'Wednesday': 2, 'Thursday': 3, 'Friday': 4, 'Saturday': 5, 'Sunday': 6}
+    weekday_numeric = weekday_dict[weekday]
+
+    # Calculate distance between pickup and dropoff locations
+    pickup_lat, pickup_lng = st.session_state.pickup_location
+    dropoff_lat, dropoff_lng = st.session_state.dropoff_location
+    distance = calculate_distance(pickup_lat, pickup_lng, dropoff_lat, dropoff_lng)
+    st.session_state.distance = distance
+
+    # Prepare input features for prediction
+    features = np.array([[pickup_lat, pickup_lng, dropoff_lat, dropoff_lng, passenger_count, distance, hour, day, weekday_numeric]])
+    features_scaled = preprocessor.transform(features)
+
+    # Predict trip duration
+    predicted_duration = predict_duration(features_scaled)
+    st.session_state.predicted_duration = predicted_duration
+
+    st.write(f'Predicted Trip Duration: {predicted_duration:.2f} seconds')
+
+    # Show map with pickup and dropoff locations and route
+    m = fl.Map(location=st.session_state.pickup_location, zoom_start=12)
+    fl.Marker(st.session_state.pickup_location, popup="Pickup Location").add_to(m)
+    fl.Marker(st.session_state.dropoff_location, popup="Dropoff Location").add_to(m)
+    fl.PolyLine([st.session_state.pickup_location, st.session_state.dropoff_location], color="blue").add_to(m)
+    st_folium(m, height=500)

preprocessor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e7dc5f43a8bff9c0b8d878f0867167fe22439937dedad7425e2fb068ce1e398
+size 2820

requirements.txt

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+streamlit
+streamlit-folium
+folium
+pandas
+tensorflow
+requests
+googlemaps
+numpy
+scikit-learn
+matplotlib