Update app.py

app.py

@@ -30,26 +30,29 @@ property_type_mapping = {
 }
 
 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(f"Location_{location}")
+        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(f"Property_Type_{property_type}")
+        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)
@@ -66,9 +69,11 @@ 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