app.py

import pickle
import pandas as pd
import gradio as gr

eb = "ebas.pkl"
with open(eb, mode="rb") as f:
    model = pickle.load(f)

features = ['rooms', 'area', 'pop', 'pop_dens', 'emp', 'tax_income', 'pop_dens_per_room']

def predict(rooms, area, pop, pop_dens, emp, tax_income, pop_dens_per_room):
    input_data = pd.DataFrame([[rooms, area, pop, pop_dens, emp, tax_income, pop_dens_per_room]],
                              columns=features)
    prediction = model.predict(input_data)[0]
    return prediction

i = gr.Interface(
    fn=predict,
    inputs=["number", "number", "number", "number", "number", "number", "number"],
    outputs="number",
    title="Apartment Rent Price Predictor with Pop_dens_per_room feature",
    description="Enter apartment features to predict the rent price.",
    examples=[[4.5,130,3664,282.9343629344,783,79838,62.874302874311105],
              [3.5,102,8796,605.3682037164,2469,94471,172.9623439189714]]
)

i.launch()