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data.py
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data.py
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"""Code pertaining to determining their vehicle based on the form"""
from pydantic import BaseModel
from copy import deepcopy
#hardcoded database of ford models
data = [
{
"name": "F-150",
"type": "truck",
"price": 34445,
"engine": "hybrid",
"seats": 6,
"uses": ["camping", "towing", "worktable", "loading", "offroad", "performance"],
"allterrain": True,
},
{
"name": "F-150",
"type": "truck",
"price": 34445,
"engine": "gas",
"seats": 6,
"uses": ["camping", "towing", "worktable", "loading", "offroad"],
"allterrain": True,
},
{
"name": "Mustang",
"type": "sports",
"price": 27770,
"engine": "gas",
"seats": 4,
"uses": ["performance", "track", "fashion"],
"allterrain": False,
},
{
"name": "Bronco",
"type": "suv",
"price": 34095,
"engine": "gas",
"seats": 4,
"uses": ["camping", "offroad", "fashion"],
"allterrain": True
},
{
"name": "Bronco Sport",
"type": "suv",
"price": 29215,
"engine": "gas",
"seats": 4,
"uses": ["camping", "offroad", "performance"],
"allterrain": True
},
{
"name": "Ford GT",
"type": "sports",
"price": 1700000,
"engine": "gas",
"seats": 2,
"uses": ["track", "performance", "speed", "hypercar"],
"allterrain": False
},
{
"name": "EcoSport",
"type": "suv",
"price": 22040,
"engine": "gas",
"seats": 5,
"uses": ["everyday", "family", "safety", "economic", "performance"],
"allterrain": False
},
{
"name": "Escape",
"type": "suv",
"price": 28000,
"engine": "hybrid",
"seats": 5,
"uses": ["safety", "family", "everyday", "economic", "comfort"],
"allterrain": False
},
{
"name": "Edge",
"type": "suv",
"price": 37945,
"engine": "gas",
"seats": 5,
"uses": ["safety", "family", "everyday", "economic", "comfort"],
"allterrain": False
},
{
"name": "Mustang Mach-e",
"type": "suv",
"price": 45995,
"engine": "electric",
"seats": 5,
"uses": ["safety", "family", "everyday", "economic", "fashion", "performance"],
"allterrain": False
},
{
"name": "Expedition",
"type": "suv",
"price": 55125,
"engine": "electric",
"seats": 8,
"uses": ["camping", "family", "everyday", "economic"],
"allterrain": True
},
{
"name": "Maverick",
"type": "truck",
"price": 22595,
"engine": "hybrid",
"seats": 5,
"uses": ["camping", "towing", "worktable", "loading", "offroad"],
"allterrain": True,
},
{
"name": "Ranger",
"type": "truck",
"price": 27400,
"engine": "gas",
"seats": 5,
"uses": ["camping", "towing", "worktable", "loading", "offroad"],
"allterrain": True,
},
{
"name": "Super-Duty",
"type": "truck",
"price": 43970,
"engine": "gas",
"seats": 6,
"uses": ["camping", "towing", "worktable", "loading", "offroad"],
"allterrain": True,
},
{
"name": "Lightning",
"type": "truck",
"price": 55974,
"engine": "electric",
"seats": 5,
"uses": ["camping", "towing", "worktable", "loading", "offroad"],
"allterrain": True,
},
]
class CarForm(BaseModel):
"""Form data model"""
type: str | None = None
engine: str | None = None
price: int
seats: int | None = None
uses: list = []
allterrain: bool
def calculate_car(form: CarForm):
"""Determine optimal car based on form data"""
filterable = deepcopy(data)
#filter those with incompatible pricing
for car in filterable:
if car["price"] > form.price:
filterable.remove(car)
#return car with most category matches
for car in filterable:
car["score"] = 0
#distinct categories worth 5 points
if car["type"] == form.type:
car["score"] += 5
if car["engine"] == form.engine:
car["score"] += 5
if car["allterrain"] == form.allterrain:
car["score"] += 5
#seat points worth 3 - difference of what is desired
if form.seats is not None:
car["score"] += 3 - abs(car["seats"] - form.seats)
#uses worth 1 each
if form.uses is not None:
for use in car["uses"]:
if use in form.uses:
car["score"] += 1
#return car with highest score
return sorted(filterable, key=lambda x: x["score"], reverse=True)[0]