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import streamlit as st | |
import pandas as pd | |
import numpy as np | |
import pickle | |
# load the model from disk | |
loaded_model = pickle.load(open("random_forest_reg.sav", 'rb')) | |
def generate_prediction(input_array): | |
ans = loaded_model.predict(input_array) | |
return ans | |
def main(): | |
# Face Analysis Application # | |
st.title("Online Food Order Prediction") | |
activiteis = ["Home", "Prediction","About"] | |
choice = st.sidebar.selectbox("Select Activity", activiteis) | |
if choice == "Home": | |
html_temp_home1 = """<div style="background-color:#6D7B8D;padding:10px"> | |
<h3 style="color:yellow;text-align:center;"> Welcome to world of AI with Prince </h3> | |
<h4 style="color:white;text-align:center;"> | |
Online Food Order Prediction using Python.</h4> | |
</div> | |
</br>""" | |
st.markdown(html_temp_home1, unsafe_allow_html=True) | |
st.write(""" | |
Predicting the premium of health insurance | |
""") | |
if choice == "Prediction": | |
val1 = 0 | |
val2 = 0 | |
val3 = 0 | |
val4 = 0 | |
st.header("Predicting the premium of health insurance") | |
# Define the input fields | |
age = st.number_input("Age", min_value=0, max_value=120, value=30, step=1) | |
Bmi = st.number_input("bmi", min_value=0, max_value=1000000, value=50000, step=1000) | |
children = st.number_input("children", min_value=1, max_value=10, value=4, step=1) | |
gender = { "Male" :1,"Female" : 0} | |
Gender_index = st.selectbox("Gender", options=list(gender.keys())) | |
Gender = gender[Gender_index] | |
smoke = { "Yes" :1,"No" : 0} | |
Smoke = st.selectbox("Smoke", options=list(smoke.keys())) | |
Snoke = smoke[Smoke] | |
Region = {"northeast" : 1, "northwest": 2,"southeast" : 3, "southwest":4} | |
Region = st.selectbox("Region", options=list(smoke.keys())) | |
Region = smoke[Smoke] | |
if Region == 1: | |
val1 = 1 | |
if Region == 2: | |
val2 = 1 | |
if Region == 3: | |
val3 = 1 | |
if Region == 4: | |
val4 = 1 | |
# Create a button to trigger the model | |
if st.button("Predict"): | |
# TODO: Replace with your model code | |
prediction = generate_prediction(np.array([[age, Bmi, children, Gender, val1, val2, val3, val4 ]])) | |
# Show the prediction | |
st.write("Prediction:", prediction[0]) | |
elif choice == "About": | |
st.subheader("About this app") | |
html_temp_about1= """<div style="background-color:#6D7B8D;padding:10px"> | |
<h4 style="color:white;text-align:center;"> | |
Predicting the premium of health insurance .</h4> | |
</div> | |
</br>""" | |
st.markdown(html_temp_about1, unsafe_allow_html=True) | |
html_temp4 = """ | |
<div style="background-color:#98AFC7;padding:10px"> | |
<h4 style="color:white;text-align:center;">Thanks for Visiting</h4> | |
</div> | |
<br></br> | |
<br></br>""" | |
st.markdown(html_temp4, unsafe_allow_html=True) | |
else: | |
pass | |
if __name__ == "__main__": | |
main() | |
# import streamlit as st | |