Update app.py

app.py

@@ -17,9 +17,9 @@ loaded_model = tf.keras.saving.load_model('./model/house_value_model.keras')
 
 def test_ml_model(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value):
 
-    df_test = pd.DataFrame(data=[longitude, latitude, housing_median_age,
+    df_test = pd.DataFrame(data=[[longitude, latitude, housing_median_age,
                             total_rooms, total_bedrooms, population,
-                            households, median_income, median_house_value], columns=data_heading)
+                            households, median_income, median_house_value]], columns=data_heading)
     df_test_norm = pd.DataFrame(scaler(df_test), columns=data_heading)
     features = {name:np.array(value) for name, value in df_test_norm.items()}
     
@@ -27,9 +27,9 @@ def test_ml_model(longitude, latitude, housing_median_age, total_rooms, total_be
     return (f'predicted: {result}')
 
 demo = gr.Interface(fn=test_ml_model,
-                    inputs=[[gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
+                    inputs=[gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
                             gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0),
-                            gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0)]], 
+                            gr.Number(value=0.0), gr.Number(value=0.0), gr.Number(value=0.0)], 
                     outputs="text",
                     description="It will help to classifiy the houses in this neighborhood above a avaerage price or not.",
                     title='Classifier')