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app.py
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from shiny import *
from pathlib import Path
import pandas as pd
import numpy as np
import pickle
from sklearn.neighbors import NearestNeighbors
# https://docs.google.com/document/d/1HxcNTPyc8kSnJPwXxZL_vK80l60Mjam72omXK9yfxOk/edit
app_ui = ui.page_fluid(
ui.h2("Vehicle Recommendation System"),
ui.row(),
ui.row(
{"class": "p-4"},
ui.column(6,ui.input_slider("salary",
"Which of the following best describes your personal income last year in Ringgit Malaysia?",
1500,15000,3000)),
ui.column(6,ui.input_slider("data_installment",
"What is your preferred monthly installment?",
300,6000,500,),),
ui.column(6,ui.input_slider("seat_capacity", "How many seats do you prefer?", 2, 9, 5),),
ui.column(6, ui.input_slider("engine_cc", "Preferred engine cc", 300, 6000, 1000)),
ui.column(6,ui.input_select("fuel_type",
"Preferred Fuel Type",
{"0": "Diesel","1": "Electric","2": "Hybrid","3": "Petrol - Unleaded (ULP)",},),),
ui.column(6,ui.input_select("data_transmission","Preferred Transmission",
{"0": "Automatic","1": "Manual",},),),
ui.column(6,ui.input_select("car_model","Preferred car model",
{"0": "Sedans","1": "Hatchbacks","2": "Sports-Utility Vehicle (SUV)",
"3": "Station Wagon","4": "Multi-Purpose Van (MPV)","5": "Coupe",
"6": "Convertible",},),),
ui.column(6,ui.input_select("repayment_period","Preferred repayment period (Years)",
{5: "5 Years",7: "7 Years",9: "9 Years"},),),
),
# ui.row(ui.output_text_verbatim("get_user_inputs")),
ui.row({"class": "p-4"},ui.input_action_button("run", "Surprise Me!", class_="btn btn-primary w-100")),
ui.row({"class": "pt-4"},ui.h5("Recommendation for You!")),
# ui.output_text_verbatim("get_user_inputs"),
ui.output_ui("get_user_inputs")
)
def server(input, output, session):
@output
# @render.text()
@render.ui()
def get_user_inputs():
CLEANED_FILE_PATH = Path(__file__).parent / "df_carlist_cleaned.csv" #for live editor
#CLEANED_FILE_PATH = 'https://gist.githubusercontent.com/Jiayue1030/335451e504b842c0821842eb9ab45fe6/raw/5c95f1432fb122340d03e31935ce3a69c8a8e352/df_carlist_cleaned.csv'
full_carlist = pd.read_csv(CLEANED_FILE_PATH)
user_inputs_dict = {
"salary" : input.salary(),
"data_installment": input.data_installment(),
"seat_capacity": input.seat_capacity(),
"engine_cc": input.engine_cc(),
"data_transmission": input.data_transmission(),
"fuel_type": input.fuel_type(),
"repayment_period": input.repayment_period(),
}
result = get_recommendation(user_inputs_dict)
if result["code"] == 200:
vehicles = result["similar_vehicles"]
vehicle_ids = vehicles.loc[:, 'vehicle_id']
vehicles_data = full_carlist[full_carlist['vehicle_id'].isin(vehicle_ids)]
vehicle_cards = build_recommendation_ui(vehicles_data)
else:
vehicle_cards = result
# return result
return vehicle_cards
def build_recommendation_ui(vehicles_data):
cards = [ui.column(4,class_="card", children=[
#ui.img(src="https://img1.icarcdn.com/4031499/gallery_new-car-carlist-proton-saga-standard-sedan-malaysia_000004031499_67a33b5c_65c9_4b22_a02d_dfd947d034ab.png"),
ui.column(12,class_="card-body", children=[
ui.h5(row["data_display_title"]),
ui.p("Monthly Installment : RM ","{:,}".format(row["data_installment"])),
ui.p("Engine CC:",row["engine_cc"]),
ui.p("Seat Capacity:",row["seat_capacity"]),
ui.p("Price: RM ","{:,}".format(row["listing_price"])),
ui.input_action_button("run", "Check Detail", class_="btn-primary m-10")
])
]) for i,row in vehicles_data.iterrows()]
cards_ui = ui.div(class_="row p-4", children=cards)
return cards_ui
#return the list of recommended vehicles from the full dataset
def get_recommendation(user_input):
features = [
"data_installment",
"seat_capacity",
"data_transmission",
"engine_cc",
"fuel_type",
]
path = Path(__file__).parent / "df_modelling.csv" #for live editor
#path = 'https://gist.githubusercontent.com/Jiayue1030/335451e504b842c0821842eb9ab45fe6/raw/a07b9d4a69fe0a638551083ee1d5937482331896/df_modelling.csv'
carlist = pd.read_csv(path)
result = dict()
model_path = Path(__file__).parent / "model.pkl"
model = NearestNeighbors(n_neighbors=5, algorithm="ball_tree")
model.fit(carlist[features])
user_input = {
"salary" : user_input["salary"],
"data_installment": user_input["data_installment"],
"seat_capacity": user_input["seat_capacity"],
"engine_cc": user_input["engine_cc"],
"data_transmission": user_input["data_transmission"],
"fuel_type": user_input["fuel_type"],
"repayment_period" : user_input["repayment_period"]
}
affordable_price = get_max_affordable_price(user_input["salary"],user_input["repayment_period"])
carlist = carlist.loc[carlist["seat_capacity"] == user_input["seat_capacity"]]
carlist = carlist[carlist['listing_price']<=affordable_price]
user_input = pd.DataFrame(user_input, index=features)
user_input = user_input.transpose()
if len(carlist) > 1:
model.fit(carlist[features])
distances, indices = model.kneighbors(user_input)
similar_vehicles = carlist.iloc[indices[0], :]
result = {"code": 200, "similar_vehicles": similar_vehicles,"affordable_price":affordable_price}
else:
result = {"code": 400,"message":"Not Found. Please reset your criteria.",
"affordable_price":affordable_price}
return result
##get_max_affordable_price
def get_max_affordable_price(salary,repayment_period):
salary = float(salary)
repayment_period = float(repayment_period)
affordable_price = 0.15*salary*12*repayment_period
return affordable_price
##--------------------------------------------------------------------------------------
app = App(app_ui, server)
# display成card，link去url
# 按了button后才显示