Create app.py

app.py

@@ -0,0 +1,38 @@
+import xgboost as xgb
+import pandas as pd
+import gradio as gr
+
+from sklearn.model_selection import train_test_split
+train = pd.read_csv('/content/sample_data/california_housing_train.csv', nrows = 10000)
+train['total_bedrooms'].fillna(train['total_bedrooms'].mean(), inplace=True)
+train.drop('households', axis=1, inplace=True)
+train.drop('total_rooms', axis=1, inplace=True)
+train.drop('total_bedrooms', axis=1, inplace=True)
+train=train.loc[train['median_house_value']<=500000,:]
+
+X=train[['longitude',
+ 'latitude',
+ 'housing_median_age',
+ 'population',
+ 'median_income']]
+Y=train['median_house_value']
+
+X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2, random_state=0)
+
+xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', eta=0.03, max_depth = 5, n_estimators = 200)
+xg_reg.fit(X_train,Y_train)
+
+def pred_house(longitude,latitude,housing_median_age,population,median_income):
+    to_predict = pd.DataFrame({'longitude': [longitude],
+                               'latitude': [latitude], 
+                               'housing_median_age': [housing_median_age],
+                               'population': [population],
+                               'median_income': [median_income]})
+    return round(xg_reg.predict(to_predict)[0],2)
+
+demo = gr.Interface(
+    fn=pred_house,
+    inputs=[gr.Slider(-120, -114), gr.Slider(32, 39), gr.Slider(0, 50), "number", "number", ],
+    outputs="number",
+)
+demo.launch()