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import streamlit as st
import numpy as np
import pickle
pipe = pickle.load(open('pipe.pkl','rb'))
df = pickle.load(open('df.pkl','rb'))
st.title("Laptop Price Predictor")
company = st.selectbox('Brand',df['Company'].unique())
type = st.selectbox('Type',df['TypeName'].unique())
ram = st.selectbox('RAM(in GB)',[2,4,8,12,16,24,32,64])
weight = st.number_input('Weight of laptop')
touchscreen = st.selectbox('Touchscreen',['No','Yes'])
ips = st.selectbox('IPS',['No','Yes'])
screen_size = st.number_input('Screen Size')
resolution = st.selectbox('Screen Resolution',['1920x1080','1600x900','3840x2160','3200x1800','2560x1600','2560x1440','2304x1440','1366x768','2880x1800'])
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)',[0,8,128,256,512,1024,2048])
gpu = st.selectbox('GPU',df['Gpu Brand'].unique())
os = st.selectbox('Os',df['os'].unique())
if st.button('Predict Price'):
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
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: "+str(int(np.exp(pipe.predict(query)[0])))) |