app.py

import joblib
import pandas as pd
import streamlit as st

model = joblib.load('model.joblib')
unique_values = joblib.load('unique_values.joblib')
    
unique_area_type =  unique_values["Area Type"]
unique_city =  unique_values["City"]
unique_furnishing =  unique_values["Furnishing Status"]
unique_tenant =  unique_values["Tenant Preferred"]
unique_contact =  unique_values["Point of Contact"]


def main():
    st.title("House Rent Prediction")

    with st.form("questionaire"):
        BHK = st.slider("BHK",min_value=1,max_value=6)
        Size = st.slider("Size",min_value=10,max_value=8000)
        Bathroom = st.slider("Bathroom",min_value=1,max_value=10)
        AreaType = st.selectbox("Area Type",options=unique_area_type)
        city = st.selectbox("City",options=unique_city)
        FurnishingStatus = st.selectbox("Furnishing Status",options=unique_furnishing)
        TenantPreferred = st.selectbox("Tenant Preferred",options=unique_tenant)
        PointofContact = st.selectbox("Point of Contact",options=unique_contact)

        # clicked==True only when the button is clicked
        clicked = st.form_submit_button("Predict Rent")
        if clicked:
            result=model.predict(pd.DataFrame({"BHK": [BHK],
                                               "Size": [Size],
                                               "Bathroom": [Bathroom],
                                               "Area Type": [AreaType],
                                               "City": [city],
                                               "Furnishing Status": [FurnishingStatus],
                                               "Tenant Preferred": [TenantPreferred],
                                               "Point of Contact": [PointofContact]}))
            # Show prediction
            st.success(f'Your predicted Rent is {result}')

if __name__ == "__main__":
    main()