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Procfile.txt

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+web: sh setup.sh && streamlit run app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import joblib
+import datetime as dt
+
+## loading in trained model
+model = joblib.load('GradientBoost_Flight_Fair_Model')
+
+@st.cache()
+def make_predictions(journey_date, journey_time, arrival_date, arrival_time, source, destination, stops, airline):
+    # preprocessing data before predictions
+    pred_input = []
+
+    stops = int(stops)
+    pred_input.append(stops)
+
+    # departure Date
+    journey_day = int(pd.to_datetime(journey_date, format="%Y-%m-%dT%H:%M").day)
+    pred_input.append(journey_day)
+
+    journey_month = int(pd.to_datetime(journey_date, format ="%Y-%m-%dT%H:%M").month)
+    pred_input.append(journey_month)
+
+    dep_min = int(journey_time.minute)
+    pred_input.append(dep_min)
+
+    dep_hour = int(journey_time.hour)
+    pred_input.append(dep_hour)
+
+    arrival_min = int(arrival_time.minute)
+    pred_input.append(arrival_min)
+
+    arrival_hour = int(arrival_time.hour)
+    pred_input.append(arrival_hour)
+
+    arrival_day = int(pd.to_datetime(arrival_date, format="%Y-%m-%dT%H:%M").day)
+    pred_input.append(arrival_day)
+
+    duration_min = abs(arrival_min - dep_min)
+    pred_input.append(duration_min)
+
+    duration_hour = abs(arrival_hour - dep_hour)
+    pred_input.append(duration_hour)
+
+    air_list = ['IndiGo', 'Air India', 'Jet Airways', 'SpiceJet', 'Multiple carriers', 'GoAir', 'Vistara', 'Air Asia', 'Vistara Premium economy', 'Jet Airways Business', 'Multiple carriers Premium economy', 'Trujet']
+    for a in air_list:
+        if a == airline:
+            pred_input.append(1)
+        else:
+            pred_input.append(0)
+
+    src_list = ['Banglore', 'Kolkata', 'Delhi', 'Chennai', 'Mumbai']
+    for i in src_list:
+        if i == source:
+            pred_input.append(1)
+        else:
+            pred_input.append(0)
+
+    dst_list = ['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad']
+    for d in dst_list:
+        if d == destination:
+            pred_input.append(1)
+        else:
+            pred_input.append(0)
+
+    prediction = model.predict(np.array([pred_input]))
+
+    return int(prediction)
+
+
+def main():
+    
+    st.title('Flight Fair Price Predictor')
+    st.subheader('Fill the following details to get the estimated flight fair price')
+
+    col1, col2 = st.columns([2, 1])
+    journey_date = col1.date_input('Journey Date')
+    journey_time = col2.time_input('Departure time')
+
+    col3, col4 = st.columns([2, 1])
+    arrival_date = col3.date_input('Arroval Date')
+    arrival_time = col4.time_input('Arrival time')
+
+    col5, col6 = st.columns(2)
+    source = col5.selectbox('Departure city',['Banglore', 'Kolkata', 'Delhi', 'Chennai', 'Mumbai'])
+    destination = col6.selectbox('Destination city', ['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'])
+
+    stops = st.selectbox('Total Stops', ['non-stop', 1, 2, 3, 4])
+
+    airline = st.selectbox('Choose Airline', ['IndiGo', 'Air India', 'Jet Airways', 'SpiceJet', 'Multiple carriers', 'GoAir', 'Vistara', 'Air Asia', 'Vistara Premium economy', 'Jet Airways Business', 'Multiple carriers Premium economy', 'Trujet'])
+
+    predict = st.button('Make Prediction',)
+
+    if stops == 'non-stop':
+        stops = 0
+    
+    # make prediction button logic
+    if predict:
+        with st.spinner('Wait for prediction....'):
+            t = make_predictions(journey_date, journey_time, arrival_date, arrival_time, source, destination, stops, airline)
+        st.success(f'Fair Price will be around Rs.{t - 1000}')
+
+if __name__=='__main__': 
+    main()

requirements.txt

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+streamlit>=1.0.0
+numpy>=1.9.2
+pandas>=0.19
+matplotlib>=1.4.3
+scikit-learn>=0.18
+joblib
+xgboost>=1.4.0

setup.sh

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+mkdir -p ~/.streamlit/
+echo "\
+[server]\n\
+headless = true\n\
+port = $PORT\n\
+enableCORS = false\n\
+\n\
+" > ~/.streamlit/config.toml