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import gradio as gr
import pickle
import json
import numpy as np
# Load model and columns
with open("kigali_model.pickle", "rb") as f:
model = pickle.load(f)
with open("columns.json", "r") as f:
data_columns = json.load(f)["data_columns"]
# Define the location and property type mappings
location_mapping = {
'gacuriro': 1,
'kacyiru': 2,
'kanombe': 3,
'kibagabaga': 4,
'kicukiro': 5,
'kimironko': 6,
'nyamirambo': 7,
'nyarutarama': 8
}
property_type_mapping = {
'apartment': 1,
'bungalow': 2,
'house': 3,
'villa': 4
}
def transform_data(size_sqm, number_of_bedrooms, number_of_bathrooms, number_of_floors, parking_space, location, property_type):
# Prepare the input array
x = np.zeros(len(data_columns))
x[0] = size_sqm
x[1] = number_of_bedrooms
x[2] = number_of_bathrooms
x[3] = number_of_floors
x[4] = parking_space
if location in location_mapping:
loc_index = data_columns.index(location)
x[loc_index] = 1
if property_type in property_type_mapping:
prop_index = data_columns.index(property_type)
x[prop_index] = 1
return np.array([x])
def predict(size_sqm, number_of_bedrooms, number_of_bathrooms, number_of_floors, parking_space, location, property_type):
# Transform input data
input_data_transformed = transform_data(size_sqm, number_of_bedrooms, number_of_bathrooms, number_of_floors, parking_space, location, property_type)
# Predict using the model
prediction = model.predict(input_data_transformed)
return prediction[0]
# Define Gradio interface components
inputs = [
gr.Number(label="Size (sqm)", value=0, placeholder="Enter size in sqm", minimum=0, interactive=True),
gr.Number(label="Number of Bedrooms", value=0, placeholder="Enter number of bedrooms", minimum=0, interactive=True),
gr.Number(label="Number of Bathrooms", value=0, placeholder="Enter number of bathrooms", minimum=0, interactive=True),
gr.Number(label="Number of Floors", value=0, placeholder="Enter number of floors", minimum=0, interactive=True),
gr.Number(label="Parking Space", value=0, placeholder="Enter parking space availability", minimum=0, interactive=True),
gr.Dropdown(choices=list(location_mapping.keys()), label="Location"),
gr.Dropdown(choices=list(property_type_mapping.keys()), label="Property Type")
]
outputs = gr.Textbox(label="Prediction (FRW)")
# Footer content
footer = "Etienne NTAMBARA @AI_Engineer"
# Launch the interface
gr.Interface(
fn=predict,
inputs=inputs,
outputs=outputs,
title="Property Price Prediction in Kigali City @2024",
description="Enter property details to get the price prediction.",
article=footer
).launch(share=True)
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