import streamlit as st
import pandas as pd
import numpy as np
import pickle
import sklearn

@st.cache_resource()
def load_rfr():
    model = pickle.load(open('rfr.sav', 'rb'))
    return model

def load_xgb():
    model = pickle.load(open('xbg.sav', 'rb'))
    return model

def load_lr():
    model = pickle.load(open('lr.sav', 'rb'))
    return model
    
st.title("Burnt Calories Prediction")

st.write("""
         # Select a machine learning model
         """
         )

#define the duration for the model
age = st.number_input('Input your age!', max_value=80, min_value=18, step=1)
duration = st.number_input('Input the amount of exercise time in minutes!', max_value=30.0, min_value=1.0, step=1.0)
height = st.number_input('Input your height!', max_value=230.0, min_value=120.0, step=1.0)
weight = st.number_input('Input your weight!', max_value=140.0, min_value=35.0, step=1.0)
heart_rate = st.number_input('Input your heart rate!', max_value=130.0, min_value=60.0, step=20.0)
body_temp = st.number_input('Input your body temperature!', max_value=45.0, min_value=35.0, step=1.0)

def model_prediction(age, height, weight, duration, heart_rate, body_temp, gender, model_input):
    with st.spinner('Model is being loaded..'):
        if model_input == "Random Forest Regressor":
            model = load_rfr()
        elif model_input == "Linear Regressor":
            model = load_lr()
        else:
            model = load_xgb()
        inputs = pd.DataFrame([{'Gender': gender, 'Age': age, 'Height': height, 'Weight': weight, 'Duration': duration, 'Heart_Rate': heart_rate, 'Body_Temp': body_temp}])
        predictions = model.predict(inputs)
        st.write("""# Prediction : """ , predictions)

with st.sidebar:
    st.markdown("<h2 style='text-align: center; color: red;'>Settings Tab</h2>", unsafe_allow_html=True)

    st.write("Input Settings:")

    #define the gender for the model
    gender_input = st.radio("Gender", ["Male", "Female"], index=None)
    if gender_input == "Male":
        gender = 0
    else:
        gender = 1

    model_input = st.radio("Model", ["Random Forest Regressor", "Linear Regressor", "XG Boost"], index=None)


with st.container():
    if st.button("Calculate"):
        model_prediction(age, height, weight, duration, heart_rate, body_temp, gender, model_input)