Create app.py

app.py

@@ -0,0 +1,84 @@
+import gradio as gr
+import pickle
+import json
+import numpy as np
+import pandas as pd
+
+# Load model and columns
+with open("model/kigali_model.pickle", "rb") as f:
+    model = pickle.load(f)
+
+with open("json/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),
+    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")
+]
+
+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",
+    description="Enter property details to get the price prediction.",
+    article=footer
+).launch()