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import streamlit as st
import pickle
import numpy as np
# import the model
pipe = pickle.load(open('pipe.pkl','rb'))
df = pickle.load(open('df.pkl','rb'))
st.title("Laptop Price Predictor")
# 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)',[2,4,6,8,12,16,24,32,64])
# weight
weight = st.number_input('Weight of the Laptop')
# Touchscreen
touchscreen = st.selectbox('Touchscreen',['No','Yes'])
# IPS
ips = st.selectbox('IPS',['No','Yes'])
# screen size
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 = st.selectbox('HDD(in GB)',[0,128,256,512,1024,2048])
ssd = st.selectbox('SSD(in GB)',[0,8,128,256,512,1024])
gpu = st.selectbox('GPU',df['Gpu brand'].unique())
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
#st.title(ppi)
query = np.array([company, type, ram, weight, touchscreen, ips, ppi, cpu, hdd, ssd, gpu, os])
st.title(query)
query = query.reshape(1, 12)
st.title(len(query))
st.title(query)
st.title(np.exp(pipe.predict(query))) |