app.py

import streamlit as st
import pickle
import numpy as np
import sklearn
# import the model
pipe = pickle.load(open('pipe.pkl','rb'))
df = pickle.load(open('data.pkl','rb'))

st.title("Laptop Predictor")

# brand
company = st.selectbox('Brand',df['Manufacturer'].unique())

# type of laptop
type_of_laptop = st.selectbox('Type',df['Category'].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')

# 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['Operating System'].unique())

if st.button('Predict Price'):
    # query
    ppi = None

    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_of_laptop,ram,os,weight,ips,ppi,cpu,hdd,ssd,gpu])

    query = query.reshape(1,11)
    # st.title("The predicted price of this configuration is " + str(int((pipe.predict(query)[0]))))
    st.title(pipe.predict(query))