Delete src/test.py
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src/test.py
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''''
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Author : Rupesh Garsondiya
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github : @Rupeshgarsondiya
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Organization : L.J University
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'''
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import time
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import streamlit as st
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from train import *
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class test :
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def __init__(self):
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pass
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def predict_data(self):
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st.sidebar.title("Select Parameter ")
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mt = Model_Train()
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S_algo,Pipeline = mt.train_model()
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df = None
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options = ["Google Pixel 5", "OnePlus 9", "Samsung Galaxy S21", "Xiaomi Mi 11",'iPhone 12']
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selected_option = st.sidebar.selectbox("Select phone model :", options)
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if selected_option in options:
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encoded_model = [1 if i == selected_option else 0 for i in options]
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df = pd.DataFrame([encoded_model], columns=options)
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options1 = ["Android",'IOS']
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if selected_option =='iPhone 12':
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selected_option1 = st.sidebar.selectbox("Select OS :", 'IOS')
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encoded_os = [0,1]
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else :
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encoded_os = [1,0]
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selected_option1 = st.sidebar.selectbox("Select OS :", 'Android')
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df[options1] = encoded_os
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options2 = ['Female','Male']
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selected_option2 = st.sidebar.radio("Select Gender :", options2)
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encoded_gender = [1 if i == selected_option2 else 0 for i in options2]
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df[options2] = encoded_gender
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app_time = st.sidebar.number_input('Enter app time : ',min_value=0.0,max_value=24.0,value=0.0)
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df['App_Time(hours/day)'] = app_time
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screen_time = st.sidebar.number_input('Enter your screen time : ',min_value=0.0,max_value=24.0,value=0.0)
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df['screen_Time(hours/day)'] = screen_time
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battary = st.sidebar.number_input('Enter battary drain(mAh) : ',min_value=100.0,max_value=6000.0,value=100.0)
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df['Battery_Drain(mAh)'] = battary
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no_app = st.sidebar.number_input('Enter number of apps installed : ',min_value=5.0,max_value=50.0,value=5.0)
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df['Installed_app'] = no_app
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data_use = st.sidebar.number_input('Enter data usage (GB) : ',min_value=0.0,max_value=10.0,value=0.0)
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df['Data_Usage(GB)'] = data_use
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age = st.sidebar.number_input('Enter your age : ',min_value=15.0,max_value=100.0,value=15.0)
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df['Age'] = age
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if st.button("Submit"):
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st.write("Processing...")
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time.sleep(2)
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prediction = S_algo.predict(df)
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if prediction==1:
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st.write('Output : Occasional Users')
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elif prediction==2:
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st.write('Output : Casual Users ')
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elif prediction==3:
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st.write('Output : content consumer : ')
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elif prediction==4:
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st.write('Output : Social Media Enthusiasts')
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else :
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st.write('Output : Power Users')
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