Update test.py
Browse files
test.py
CHANGED
@@ -53,19 +53,19 @@ class test :
|
|
53 |
df[options2] = encoded_gender
|
54 |
|
55 |
|
56 |
-
app_time = st.sidebar.number_input('Enter app time : ',min_value=0
|
57 |
df['App_Time(hours/day)'] = app_time
|
58 |
|
59 |
|
60 |
-
screen_time = st.sidebar.number_input('Enter your screen time : ',min_value=0
|
61 |
df['screen_Time(hours/day)'] = screen_time
|
62 |
|
63 |
|
64 |
-
battary = st.sidebar.number_input('Enter battary drain(mAh) : ',min_value=100
|
65 |
df['Battery_Drain(mAh)'] = battary
|
66 |
|
67 |
|
68 |
-
no_app = st.sidebar.number_input('Enter number of apps installed : ',min_value=5
|
69 |
df['Installed_app'] = no_app
|
70 |
|
71 |
|
@@ -73,10 +73,10 @@ class test :
|
|
73 |
df['Data_Usage(GB)'] = data_use
|
74 |
|
75 |
|
76 |
-
age = st.sidebar.number_input('Enter your age : ',min_value=15
|
77 |
df['Age'] = age
|
78 |
|
79 |
-
if st.button("Submit"):
|
80 |
st.write("Processing...")
|
81 |
time.sleep(2)
|
82 |
prediction = S_algo.predict(df)
|
|
|
53 |
df[options2] = encoded_gender
|
54 |
|
55 |
|
56 |
+
app_time = st.sidebar.number_input('Enter total app time (in Hours): ',min_value=0,max_value=24,value=0)
|
57 |
df['App_Time(hours/day)'] = app_time
|
58 |
|
59 |
|
60 |
+
screen_time = st.sidebar.number_input('Enter your screen time(in hours) : ',min_value=0,max_value=24,value=)
|
61 |
df['screen_Time(hours/day)'] = screen_time
|
62 |
|
63 |
|
64 |
+
battary = st.sidebar.number_input('Enter battary drain(mAh) : ',min_value=100,max_value=6000,value=100)
|
65 |
df['Battery_Drain(mAh)'] = battary
|
66 |
|
67 |
|
68 |
+
no_app = st.sidebar.number_input('Enter number of apps installed : ',min_value=5,max_value=100,value=5)
|
69 |
df['Installed_app'] = no_app
|
70 |
|
71 |
|
|
|
73 |
df['Data_Usage(GB)'] = data_use
|
74 |
|
75 |
|
76 |
+
age = st.sidebar.number_input('Enter your age(in years) : ',min_value=15,max_value=100,value=15)
|
77 |
df['Age'] = age
|
78 |
|
79 |
+
if st.sidebar.button("Submit"):
|
80 |
st.write("Processing...")
|
81 |
time.sleep(2)
|
82 |
prediction = S_algo.predict(df)
|