import streamlit as st
import joblib
import numpy as np


# load model
loaded_model = joblib.load('model.pkl')


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("""
                  Online Food Order Prediction

                 """)
    if choice == "Prediction":
        st.header("Online Food Order Prediction")
        # Define the input fields
        age = st.number_input("Age", min_value=0, max_value=120, value=30, step=1)
        income = st.number_input("Income", min_value=0, max_value=1000000, value=50000, step=1000)
        family_size = st.number_input("Family Size", min_value=1, max_value=10, value=4, step=1)
        pin = st.number_input("Pin", min_value=100000, max_value=999999, value=500000, step=1)
        
        gender = { "Male" :1,"Female" : 2}
        Gender_index = st.selectbox("Gender", options=list(gender.keys()))
        Gender = gender[Gender_index]
        
        Mirrage = {"Single" : 1, "Married": 2,"Not Revealed" : 3}
        Marital_index =  st.selectbox("Marital Status", options=list(Mirrage.keys()))
        Marital_status = Mirrage[Marital_index]
        
        
        occupation_dict = {"Student" :1, "Employee" : 2, "Self Employeed" : 3, "House wife" : 4}
        occupation_index = st.selectbox("Marital Status", options=list(occupation_dict.keys()))
        occupation = occupation_dict[occupation_index]
        
        educational_level = {"Graduate": 1, "Post Graduate":2, "Ph.D":3, "School" :4, "Uneducated" :5}
        educational_index = st.selectbox("educational_level", options=list(educational_level.keys()))
        education = educational_level[educational_index]
        
        Review_dict = {"Positive": 1, "Negative": 0}
        Review_index = st.selectbox("Review", options=list(Review_dict.keys()))
        Review = Review_dict[Review_index]

        # Create a button to trigger the model
        if st.button("Predict"):
        # TODO: Replace with your model code
            prediction = generate_prediction(np.array([[age, income, family_size, pin, Gender, Review, Marital_status, occupation, education]]))
        
            # 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;">
                                    Online Food Order Prediction with Machine Learning .</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