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.gitignore

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+venv

Procfile

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+web: sh setup.sh && streamlit run app.py

df.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cc60b3b8399854aef6b97ad97ef2e4928da652f860f3a4b60173b5a4647051f7
+size 124865

main.py

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+import learn as learn
+import streamlit as st
+import pickle
+import numpy as np
+import Scikit-learn
+import sklearn
+import sklearn
+# import the model
+pipe = pickle.load(open('pipe.pkl','rb'))
+df = pickle.load(open('df.pkl','rb'))
+
+st.title("Get An Estimated Value For Your Laptop By Filling Out The Details Below:")
+
+# 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])
+
+# weightp
+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 (in inches)')
+
+# 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 (Operating System)',df['Os'].unique())
+
+if st.button('Predict Price'):
+    # query
+    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
+    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]))))

pipe.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8cdc048a9aca4fd13e3a8bdc7fffd357ae2766e076b015d7d79612044b98ff14
+size 583802

requirements.txt

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+streamlit
+scikit-learn
+pickle

setup.sh

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+mkdir -p ~/.streamlit/
+
+echo "\
+[server]\n\
+port = $PORT\n\
+enableCORS = false\n\
+headless = true\n\
+\n\
+" > ~/.streamlit/config.toml