Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import pickle
 import json
 import numpy as np
-import pandas as pd
 
 # Load model and columns
 with open("kigali_model.pickle", "rb") as f:
@@ -13,49 +12,52 @@ with open("columns.json", "r") as f:
 
 # 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
+    'kacyiru': 1,
+    'kanombe': 2,
+    'kibagabaga': 3,
+    'kicukiro': 4,
+    'kimironko': 5,
+    'nyamirambo': 6,
+    'nyarutarama': 7
 }
 
 property_type_mapping = {
-    'apartment': 1,
-    'bungalow': 2,
-    'house': 3,
-    'villa': 4
+    '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
+    # 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[4] = parking_space
+    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 prediction[0]
+    return round(prediction[0], 2)  # round prediction for better readability
 
 # Define Gradio interface components
 inputs = [
@@ -65,9 +67,11 @@ inputs = [
     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")
+    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
@@ -78,7 +82,7 @@ gr.Interface(
     fn=predict,
     inputs=inputs,
     outputs=outputs,
-    title="Property Price Prediction in Kigali City @2024",
+    title="KIGALI Property Price Prediction (Real Time AI APPLICATION",
     description="Enter property details to get the price prediction.",
     article=footer
-).launch(share=True)
+).launch(debug=True)