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server.py
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server.py
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from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
import pickle
import pandas as pd
import re
app = FastAPI()
with open('model.pickle', 'rb') as f:
model = pickle.load(f)
with open('col.pickle', 'rb') as f:
col = pickle.load(f)
class Item(BaseModel):
name: str
year: int
selling_price: int
km_driven: int
fuel: str
seller_type: str
transmission: str
owner: str
mileage: str
engine: str
max_power: str
torque: str
seats: float
class Items(BaseModel):
objects: List[Item]
def parse_date(item):
arr = [0 for i in range(len(col))]
fuel = 'fuel_' + item.fuel
seller_type = 'seller_type_' + item.seller_type
owner = 'owner_' + item.owner
seats = 'seats_' + str(int(item.seats))
for i in range(len(col)):
if col[i] == 'year' or col[i] == 'km_driven':
arr[i] = int(getattr(item, col[i]))
if col[i] == 'mileage':
match = re.search(r'\d+\.\d+', str(getattr(item, col[i])))
arr[i] = float(match.group()) if match else 0
if col[i] == 'engine':
match = re.search(r'\d+', str(getattr(item, col[i])))
arr[i] = int(match.group()) if match else 0
if col[i] == 'max_power':
match = re.search(r'\d+\.*\d*', str(getattr(item, col[i])))
arr[i] = float(match.group()) if match else 0
if col[i] == seats:
arr[i] = 1
if col[i] == fuel:
arr[i] = 1
if col[i] == seller_type:
arr[i] = 1
if col[i] == owner:
arr[i] = 1
data = pd.DataFrame(columns=col)
data.loc[len(data)] = arr
data_subset = data.drop(['max_power', 'mileage'], axis=1)
for column in data_subset.columns:
data[column] = data[column].astype(int)
prediction = model.predict(data)
return prediction
@app.post("/predict_item")
def predict_item(item: Item) -> float:
return parse_date(item)
@app.post("/predict_items")
def predict_items(items: List[Item]):
predictions = []
data = {item_data: [] for item_data in list(items[0].model_dump().keys())}
сols = data.keys()
for item in items:
prediction_item = parse_date(item)
predictions.append(float(prediction_item))
for key in сols:
data[key].append(item.model_dump()[key])
data['predict'] = predictions
return data