Create test.py

test.py

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