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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 = {
'kacyiru': 1,
'kanombe': 2,
'kibagabaga': 3,
'kicukiro': 4,
'kimironko': 5,
'nyamirambo': 6,
'nyarutarama': 7
}
property_type_mapping = {
'bungalow': 1,
'house': 2,
'villa': 3
}
def transform_data(size_sqm, number_of_bedrooms, number_of_bathrooms, number_of_floors, parking_space, location, property_type):
# Prepare the input array with zeros
x = np.zeros(len(data_columns))
# Assign input values to the corresponding columns
x[0] = size_sqm
x[1] = number_of_bedrooms
x[2] = number_of_bathrooms
x[3] = number_of_floors
x[5] = parking_space # Ensure that parking_space aligns with the correct index in your model
# Apply location mapping
if location in location_mapping:
loc_index = data_columns.index(location)
x[loc_index] = 1
# Apply property type mapping
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 round(prediction[0], 2) # round prediction for better readability
# Define Gradio interface components
inputs = [
gr.Number(label="Size (sqm)", value=0),
gr.Number(label="Number of Bedrooms", value=0),
gr.Number(label="Number of Bathrooms", value=0),
gr.Number(label="Number of Floors", value=0),
gr.Number(label="Parking Space", value=0),
gr.Dropdown(choices=list(location_mapping.keys()), label="Location"),
gr.Dropdown(choices=list(property_type_mapping.keys()), label="Property Type"),
# Add new inputs for other columns, like furnished, proximity, etc.
]
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="KIGALI Property Price Prediction (Real Time AI APPLICATION",
description="Enter property details to get the price prediction.",
article=footer
).launch(debug=True) |