| 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])))) |