import streamlit as st

import pickle

import numpy as np

import math

# import model

pipe = pickle.load(open('pipe.pkl','rb'))

df = pickle.load(open('df.pkl','rb'))

st.title("Laptop Price Prdictor")

# brand

company = st.selectbox('Brand',df['Company'].unique())

# type of laptop

type = st.selectbox('Type',df['TypeName'].unique())

# ram

ram = st.selectbox('Ram(in GB)',[2,4,6,8,16,12,24,32,64,])

# weight

weight = st.number_input('Weight of the laptop')

# touchscreen

touchscreen = st.selectbox('Touchscreen',['No','Yes'])

# IPS display

ips = st.selectbox('IPS',['No','Yes'])

# screensize]

screen_size = st.number_input('Screen Size')

#resolution

resolution = st.selectbox('Screen Resolution',['1920x1080','1366x768','1600x900','3840x2160','3200x1800','2880x1800','2560x1600','2560x1440','2304x1440'])

#cpu

cpu = st.selectbox('CPU',df['Cpu Brand'].unique())

# hdd

hdd = st.selectbox('HDD(in GB)',[0,128,256,512,1024,2048])

# ssd

ssd = st.selectbox('SSD(in GB)',[0,8,128,256,512,1024])

#gpu

gpu = st.selectbox('GPU',df['Gpu Brand'].unique())

#os

os = st.selectbox('OS',df['OS'].unique())

if st.button('Predict Price'):

ppi = None

if touchscreen == 'Yes':

touchscreen=1

else:

touchscreen=0

if ips=='Yes':

ips =1

else:

ips=0

X_res = int(resolution.split('x')[0])

Y_res = int(resolution.split('x')[1])

ppi = ((X_res**2)+ (Y_res**2))**0.5/screen_size

# create input point query

query = np.array([company,type,ram,weight,touchscreen,ips,ppi,cpu,hdd,ssd,gpu,os])

query = query.reshape(1,12)

st.title("The predicted price of this configuration is " + str(int(np.exp(pipe.predict(query)[0]))))