Update app.py

app.py

@@ -4,43 +4,82 @@ import json
 import numpy as np
 
 # Load model and columns
-with open("banglore_home_prices_model.pickle", "rb") as f:
+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"]
 
-locations = data_columns[3:]  # Extract location columns (FROM FORTH COLUMN TO END FOUND IN LOCATION)
+# 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
+}
 
-def predict_price(total_sqft, bath, bhk, location):
+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] = total_sqft
-    x[1] = bath
-    x[2] = bhk
-    if location in locations:
-        loc_index = data_columns.index(location)
+    x[0] = size_sqm
+    x[1] = number_of_bedrooms
+    x[2] = number_of_bathrooms
+    x[3] = number_of_floors
+    x[4] = parking_space
+
+    # Apply location mapping
+    if location in location_mapping:
+        loc_index = data_columns.index(f"Location_{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}")
+        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)
     
-    # Make prediction
-    return model.predict([x])[0]
+    # Predict using the model
+    prediction = model.predict(input_data_transformed)
+    return round(prediction[0], 2)  # round prediction for better readability
 
-# Create the Gradio interface
+# Define Gradio interface components
 inputs = [
-    gr.Number(minimum=1,label="Total Square Feet"),
-    gr.Number(minimum=1,label="Bath"),
-    gr.Number(minimum=1,label="BHK [Bedroom, Hall, and Kitchen]"),
-    gr.Dropdown(choices=locations, label="Location")
+    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="Predicted Price (Lakh)")
-
-# Add the link under the output prediction
-#link = gr.Markdown("For more details, visit [Github](https://github.com/94etienne/AI_PROJECTS_RESEARCH/blob/main/1_banglore_home_price.rar)")
-
+outputs = gr.Textbox(label="Prediction (FRW)")
 
 # Footer content
 footer = "Etienne NTAMBARA @AI_Engineer"
 
 # Launch the interface
-gr.Interface(fn=predict_price, inputs=inputs, outputs=outputs, title="Real Estate Price Prediction", article=footer).launch()
+gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title="Property Price Prediction",
+    description="Enter property details to get the price prediction.",
+    article=footer
+).launch(__debug__=True)