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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 sys |
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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 total app time (in Hours): ',min_value=0,max_value=24,value=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(in hours) : ',min_value=0,max_value=24,value=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,max_value=6000,value=100) |
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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,max_value=100,value=5) |
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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(in years) : ',min_value=15,max_value=100,value=15) |
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df['Age'] = age |
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if st.sidebar.button("Submit"): |
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for _ in range(3): |
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sys.stdout.write("Processing") |
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sys.stdout.flush() |
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time.sleep(1) |
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sys.stdout.write("\rProcessing.") |
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sys.stdout.flush() |
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time.sleep(1) |
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sys.stdout.write("\rProcessing..") |
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sys.stdout.flush() |
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time.sleep(1) |
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sys.stdout.write("\rProcessing...") |
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sys.stdout.flush() |
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time.sleep(1) |
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sys.stdout.write("\r ") |
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sys.stdout.flush() |
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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') |
