app.py

import json
import pickle
import numpy as np
import gradio as gr

# global variables
__locations = None
__data_columns = None
__model = None


def get_estimated_price(sqft, bhk, bath, location):
    # Predicts the price of a property using the features given as arguments

    try:
        # Find the index of the given location in data_columns
        loc_index = __data_columns.index(location.lower())
    
    except:
        # Location could not be found
        loc_index = -1

    # Create an array of input features
    x = np.zeros(len(__data_columns))
    x[0] = sqft
    x[1] = bath
    x[2] = bhk

    # If location is not "other" and is a valid location
    if loc_index >= 0:
        x[loc_index] = 1

    # Make a prediction on the price of the property using the model
    y_pred = round(__model.predict([x])[0],2)  # Round to 2 d.p

    if y_pred <0:
        y_pred = 0

    y_pred = y_pred * 100000
    y_pred = str(y_pred) + "₹"
    return y_pred
    

def get_location_names():
    # Returns a list of all the locations in the dataset
    return __locations


def load_saved_artifacts():
    print("Loading Saved Artifacts...Start")
    global __data_columns, __locations, __model

    # file_path = "Week 20 & 21\RealEstateProject\model\columns.json"
    file_path = "columns.json"
    with open(file_path,"r") as f:
        # Loads the data with data_columns as a key from the json file
        __data_columns = json.load(f)["data_columns"]

        # Extract locations from the data columns
        __locations = __data_columns[3:]

    # file_path = "Week 20 & 21\RealEstateProject\model\home_prices_model.pickle"
    file_path = "home_prices_model.pickle"

    with open(file_path, "rb") as f:
        # Load the pickle model
        __model = pickle.load(f)
    print("Loading Saved Artifacts...Done")


if __name__ == "__main__":
    load_saved_artifacts()

    # Define a list of inputs
    inputs = [
        gr.Number(
            minimum=0, maximum=10000, info="The area of the house in square feet",
            placeholder="e.g: 5000", label="Area"
        ),
        gr.Radio(
            choices=[1,2,3,4,5], 
            label="BHK",
            interactive="True",
            info="The number of bedrooms, halls and kitchens combined in the house",
            value=1
        ),
        gr.Radio(
            choices=[1,2,3,4,5], 
            label="Bathrooms",
            interactive="True",
            info="The number of bathrooms in the house",
            value=1
        ),
        gr.Dropdown(
            choices=get_location_names(),
            label="Location",
            interactive=True,
            info="The location of the house in Bengaluru, India"
        )
    ]

    # Create a Gradio Interface
    demo = gr.Interface(
    fn=get_estimated_price,  # Function to use
    inputs = inputs,
    outputs= ["text"]
    )

    # Launch the interface
    demo.launch(share=True)